EP4637624A1 - Methods for quantifying canine dental biometrics - Google Patents

Abstract

Methods of determining oral biometrics of an animal such as a dog are provided. The methods include generating a three- dimensional 3D model, for example through computer tomography, of a physical dental cast for further assessment of oral biometrics. Such measurements can serve as indicators of tooth overcrowding, which can be indicative of a periodontal disease risk.

Description

METHODS FOR QUANTIFYING CANINE DENTAL BIOMETRICS

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent Application No. 63/476,738, filed on December 22, 2022, the entire content of which is incorporated by reference herein.

1. FIELD

The presently disclosed subject matter is directed to novel methods of determining oral biometrics of a canine.

2. BACKGROUND

Periodontal disease (PD) is one of the most common pathological conditions in dogs, with small breeds reported as being especially vulnerable to developing PD. PD includes gingivitis, the reversible inflammation of the gingiva, and periodontitis, the inflammation and irreversible destruction of the supporting structures of the teeth. If left untreated, the damage to the gingiva, periodontal ligament, cementum and alveolar bone can result in the loss of the affected teeth.

The prevalence of PD in dogs measured in dogs under general anesthesia or from postmortem samples broadly ranges from 44% to 100% [1-5], A number of factors contribute to this observed variation, including different diagnostic scoring methods, differences in the breeds studied, body weight, and differences in care. For example, study cohorts have varied between 408 client-owned dogs [2] versus 80 institutionalized research beagles [3],

Other studies have focused on differences between animal size. For example, one study included a cohort of 1,350 North American dogs that focused on calculus, gingival inflammation, furcation exposure and attachment loss as significant factors in small dogs compared with larger dogs [6], In the United Kingdom, a meta-study of 4,000 electronic veterinary patient records noted significant breed differences in the prevalence of periodontal disease, with Yorkshire terriers reporting the highest prevalence [7],

Periodontitis susceptibility in toy and small breeds is corroborated by longitudinal studies of periodontitis prevalence rates in Yorkshire terriers, miniature schnauzers and Labrador retrievers [8, 9, 10], The rate of periodontitis incidence was far greater in miniature schnauzers relative to Labrador retrievers (n=52), with 28.6% of the miniature schnauzer teeth assessed progressing to early periodontitis (up to 25% attachment loss) compared to 5.7% of teeth in Labradors over the study period [8, 9], The study designs excluded dogs once they had attained early periodontitis in 12 or more teeth. In these studies, 35 miniature schnauzers (67%) had at least 12 teeth progress to early periodontitis within 60 weeks [8] whilst no Labradors attained 12 early periodontitis teeth over the full 2-year study [9], Of 50 Yorkshire terriers, 22 dogs (44%) came off trial at their first assessment at 37 weeks of age due to the presence of early periodontitis in 12 or more teeth [10],

The mechanisms that drive PD predisposition in smaller breeds are undefined. Differences in bacterial populations or overall bacterial load could act as a greater stimulus for disease. Alternatively, differences in the sensitivity or resolution of the host’s inflammatory response could drive the higher rate of observed disease pathology. The host’s anatomy can play a part in both of these hypotheses. For example, toy and small breed dogs are more likely to have persistent deciduous teeth along with their adult dentition [11],

As with retained deciduous teeth, tooth crowding reduces natural cleaning mechanisms, so the protected tooth surfaces resulting from crowding can lead to a higher bacterial load [12- 15], Tooth crowding by definition means that the mesio-distal length of the teeth is excessive compared with the total dental arch length [13], Selective breeding of smaller dogs has selected for dogs with altered growth regulators leading to smaller muscular-skeletal mass. A possible explanation for tooth overcrowding being more common in small breed dogs is that the reduction in the teeth size does not occur at the same rate as the shortening of the maxillary and mandibular bones z.e., the regulatory mechanisms regulating tooth size are genetically independent of those that regulate mandibular and maxillary dimensions. Gioso et al. [16] showed that the ratio between height of the mandible and height of the first molar decreases significantly in conjunction with the size of the dog. If crowding does occur, it suggests that the teeth have not “shrunk” proportionally. If this is the case, then smaller breeds will have a greater surface area at the tooth:gingiva interface relative to their overall size. Hypothetically, this would skew the inflammatory load:bodyweight ratio, meaning a smaller dog’s immune system is presented with a proportionally greater inflammatory stimulus in response to plaque build-up which can incur a higher incidence of PD.

Studies in human dentistry have shown correlations between tooth crowding and inflammation in subjects with poor or moderate oral hygiene [22], Generally, the level of oral hygiene applied in dogs is less than optimal; hence, the relationship between tooth overcrowding and periodontal disease can be of greater import in dogs. Tooth overcrowding has been proposed as a predisposing factor of PD in toy and small dog breeds, although no data are available to support this. There are a lack of studies defining and evaluating oral biometrics in dogs. Radiography can be used for linear measurements but provides only two-dimensional information. The manual caliper technology used to measure dentition in humans is time consuming and presents limitations as to what type of measurement can be performed (z.e., not able to measure surface areas). Accordingly, there remains a need in the art for methods to assess inter-breed tooth overcrowding, in an accurate and repeatable manner.

The present disclosure addresses this need with the development of methods for determining oral biometrics, which utilizes reconstructed 3D models obtained from CT images. The present disclosure utilizes computer generated reconstructions of canine dental casts coupled with computer imaging analysis software to provide an accurate and rapid technology to measure tooth overcrowding in dogs and provide an oral health assessment.

3. SUMMARY

The purpose and advantages of the disclosed subject matter will be set forth in and apparent from the description that follows, as well as will be learned by practice of the disclosed subject matter. Additional advantages of the disclosed subject matter will be realized and attained by the methods and systems particularly pointed out in the written description and claims hereof, as well as from the appended drawings.

The disclosed subject matter provides a method of determining oral biometrics of an animal, the method comprising: (a) obtaining a plurality of digital images of a dental cast from the animal; (b) generating a three-dimensional (3D) dental cast model from the plurality of digital images; (c) determining a dental cast score, based on dentition measurements of the 3D dental cast model; and (d) determining, based on the dental cast score, the oral biometrics of an animal.

In certain embodiments, the animal is of the genus Canis.

In certain embodiments, the dental cast is a maxillary dental cast. In certain embodiments, obtaining a dental cast comprises: (a) generating a dental impression of the oral tissues; and (b) obtaining a dental cast from the impression of the oral tissues.

In certain embodiments, the digital images of the dental cast are acquired by a computed tomography (CT) scanner. In certain embodiments, digital images of the dental cast are axial CT images. In certain embodiments, axial CT images are converted into a 3D model of the dental cast.

In certain embodiments, the dental cast scoring comprises determining one or more linear and surface dentition measurements. In certain embodiments, the one or more linear and surface dentition measurements are selected from the group consisting of: individual mesio- distal length; arch perimeter; gingival margin perimeter for four teeth; tooth surface area for four teeth; and combinations.

In certain embodiments, the linear and surface dentition measurements are computed to calculate one or more oral biometrics, wherein the oral biometric are selected from the group consisting of: (a) total mesio-distal length; (b) ratio of the total mesio-distal length and the arch perimeter; (c) ratio of the total mesio-distal length and body weight; (d) sum of the gingival margin perimeter for the four teeth; (e) ratio of the sum of the gingival margin perimeter and the arch perimeter; (f) ratio of the sum of the gingival margin perimeter and the body weight; (g) sum of the tooth surface area for the two teeth; (h) ratio of the sum of the tooth surface area and body weight; and (i) combinations thereof.

In certain embodiments, individual mesio-distal length for each maxillary tooth except molars comprises measurements taken at the greatest mesio-distal width of each tooth. In certain embodiments, the arch perimeter is measured following the jaw shape. In certain embodiments, the arch perimeter is traced from the buccolingual centre of the distal aspect of the fourth premolars, through the dental arch and over the incisal edge of the incisors, terminating at the contact point between the central incisors. In certain embodiments, the gingival margin perimeter is measured at the interface between the most coronal point of the gingiva and the sulcular epithelium. In certain embodiments, the tooth surface area comprises the tooth crown.

In certain embodiments, the individual mesio-distal length and the arch perimeter measurements were computed on a projected plane located over the 3D model, wherein mesio and distal aspect landmarks are perpendicularly projected over the 3D model. In certain embodiments, the projected plane is located 1 cm above the canine teeth tip and perpendicular to the anatomical sagittal plane. In certain embodiments, the gingival margin perimeter and the tooth surface area are computed on the 3D model.

The presently disclosed subject matter also provides a method for establishing a personalized oral health care plan for an animal, the method comprising: (a) obtaining a plurality of digital images of a dental cast from the animal; (b) generating a three-dimensional (3D) dental cast model from the plurality of digital images; (c) determining a dental cast score, based on dentition measurements of the 3D dental cast model; (d) determining, based on the dental cast score, oral biometrics of an animal; (e) using the oral biometrics to generate a personalized oral health care plan. In certain embodiments, the personalized oral health care plan includes one or more recommendations of tooth brushing, treats, diet, veterinary treatments or assessments, and combinations thereof. 4. BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure and its features and advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings.

Figure 1 shows a comprehensive overview of the canine study subjects.

Figure 2 shows a representative CT generated image demonstrating mesio-distal length measurements (mm) of teeth 108 up to 208.

Figure 3 shows a representative CT generated image demonstrating the arch perimeter line from teeth 108 up to 208.

Figure 4 shows a representative CT generated image demonstrating the gingival margin perimeter drawn on a maxillary fourth premolar.

Figure 5 shows a representative CT generated 3D model demonstrating the projection plane located over the model, where mesio-distal aspect landmarks were perpendicularly projected and measurements were computed.

Figure 6 shows a graph depicting the ratio of total mesio-distal length and arch perimeter relative to dog size. Data are presented as means with 97.5% confidence intervals.

Figure 7 shows a graph depicting the sum of gingival margin perimeter over arch perimeter by dog size. Data are presented as means with 97.5% confidence intervals.

Figure 8 shows a graph depicting the total mesio-distal length over body weight (mm/kg BW) by dog size. Data are presented as means with 95% confidence intervals.

Figure 9 shows a graph depicting the sum of gingival margin perimeter over body weight (mm/kg BW) by dog size. Data are presented as means with 95% confidence intervals.

Figure 10 shows a graph depicting the sum of tooth surface area over body weight (mm²/kg BW) by dog size. Data are presented as means with 95% confidence intervals.

Figure 11 shows a Bland-Altman plot comparing the caliper and software estimates of mesio-distal length (mm). Data are differences between methods with 95% agreement limits, against the average of the two methods.

Figure 12 shows a graph depicting the power to detect differences in the sum of the mesio-distal length (mm) for a range of sample sizes using the caliper and the CT method.

Figure 13 shows a graph depicting the total mesio-distal length (mm) against arch perimeter (mm), according to dog size.

Figure 14 shows an example computer system. 5. DETAILED DESCRIPTION

The present disclosure demonstrates a refined methodology to evaluate oral biometrics, particularly relevant for evaluating the differences in dogs of varied sizes. The present disclosure demonstrates 3D modelling can provide accurate and reproducible results enabling smaller cohort study numbers. In particular, the present disclosure demonstrated a disproportionate relationship between tooth size and arch perimeter/body weight in the toy/small dogs relative to the medium/large dogs. Thus, this identified relationship represents a predisposing factor that contributes to an increased susceptibility in toy/small dogs to develop periodontitis.

The present disclosure is based, in part, on the discovery that the animal dentition measurements can be accurately and reproducibly obtained by computer modeling of 3D reconstructed canine dental casts. Using the methods disclosed here, inter-breed tooth overcrowding can be assessed.

For clarity, and not by way of limitation, the detailed description of the invention is divided into the following subsections:

5.1 Definitions;

5.2 Companion animals;

5.3 Methods of determining; and

5.4 Systems.

5.1 DEFINITIONS

The terms used in this specification generally have their ordinary meanings in the art, within the context of this disclosure and in the specific context where each term is used. Certain terms are discussed below, or elsewhere in the specification, to provide additional guidance to the practitioner in describing the methods of the disclosure and how to make and use them.

For purposes of interpreting this specification, the following definitions will apply and whenever appropriate, terms used in the singular will also include the plural and vice versa. In the event that any definition set forth below conflicts with any document incorporated herein by reference, the definition set forth below shall control.

As used herein, the use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification can mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” Still further, the terms “having,” “including,” “containing” and “comprising” are interchangeable and one of skill in the art is cognizant that these terms are open ended terms. Further, the term “comprising” encompasses “including” as well as “consisting,” e.g., a composition “comprising” X can consist exclusively of X or can include something additional, e.g., X + Y.

As used herein, the term “about” or “approximately” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within 3 or more than 3 standard deviations, per the practice in the art. Alternatively, “about” can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, and more preferably still up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold, and more preferably within 2- fold, of a value.

As used herein, the term “about” or “approximately” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within 3 or more than 3 standard deviations, per the practice in the art. Alternatively, “about” can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, and more preferably still up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold, and more preferably within 2- fold, of a value.

Ranges provided herein are understood to be shorthand for all of the values within the range. For example, a range of 1 to 50 is understood to include any number, combination of numbers, or sub-range from the group consisting 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 as well as all intervening decimal values between the aforementioned integers such as, for example, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, and 1.9. Ranges disclosed herein, for example, “between about X and about Y” are, unless specified otherwise, inclusive of range limits about X and about Y as well as X and Y. With respect to sub-ranges, “nested sub-ranges” that extend from either endpoint of the range are specifically contemplated. For example, a nested sub-range of an exemplary range of 1 to 50 can include 1 to 10, 1 to 20, 1 to 30, and 1 to 40 in one direction, or 50 to 40, 50 to 30, 50 to 20, and 50 to 10 in the other direction.

As used herein, references to “embodiment,” “an embodiment,” “one embodiment”,

“another embodiment,” “in various embodiments,” “certain embodiments,” etc., indicate that the embodiment s) described can include a particular feature, structure, or characteristic, but every embodiment might not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. After reading the description, it will be apparent to one skilled in the relevant art(s) how to implement the disclosure in alternative embodiments.

As used herein, the terms “animal,” “companion animal,” or “pet” refer to a wide variety of animals, such as quadrupeds, primates, and other mammals. For example, the terms “animal,” “companion animal,” or “pet” can refer to domestic animals including, but not limited to, dogs, cats, horses, cows, ferrets, rabbits, pigs, rats, mice, gerbils, hamsters, goats, and the like. The terms “animal” or “pet” can also refer to wild animals including, but not limited to, wolf, bison, elk, deer, lion, tiger, and the like. In certain embodiments, the animal is a companion animal. In certain instances, the companion animal is a dog or a cat. For example, but not by way of limitation, the companion animal can be a “domestic” dog, e.g., Canis lupus familiaris. In certain embodiments, the companion animal can be a “domestic” cat such as Felis domesticus.

As used herein, the term “cohort” refers to a group or segment of animal subjects with shared factors or influences, such as age, breed, medical history, periodontal disease risk factors, environmental influences, etc.

As used herein, the term “disease” refers to any condition or disorder that damages or interferes with the normal function of a cell, tissue, or organ.

As used herein, the term “oral disease or disorder,” refers to a disease or disorder that occurs in an oral cavity of a subject (e.g., an animal). For example, the disease or disorder can affect the teeth, structures that support the teeth such as periodontal ligament, alveolar bone, or the gums of the subject. Exemplary oral diseases or disorders of the present disclosure include, but are not limited to, periodontal disease, gingival stomatitis, odontoclastic resorptive lesions, and oral malodor.

As used herein, the term “periodontal disease,” also known as gum disease, refers to an inflammation or infection that affects the tissues surrounding the teeth. Periodontal disease can range in severity, e.g., from gingivitis (e.g., dental plaque-induced gingivitis) to periodontitis. An example of the range is found on avdc.org/avdc-nomenclature. 5.2 COMPANION ANIMALS

The presently disclosed subject matter focuses on the health assessment of companion animals. In specific embodiments, the companion animal is a domestic dog.

Dog Breeds

The present disclosure relates to, inter alia, methods for assessing health and well-being of animals, taking into account divers animal characteristics such as size, sex, breed, and species. As domesticated animals receive the majority of veterinary care, analyzing trends and individual animal characteristics for the most common domesticated animal category (e.g., dogs) can provide an indication of the efficacy of the examined method when applied to other animals.

The present disclosure relates to, inter alia, methods to determine oral biometrics of an animal of the genus Canis. The Canis genus comprises domestic dogs (Canis lupus familiaris), wolves, coyotes, foxes, jackals, dingoes and the present disclosure can be used for all these animals. In some embodiments, the subject is a domestic dog, herein referred to simply as a dog. This Canis genus is part of the Canidae family, which includes numerous extant species.

As used herein, the expression “size category” refers to the definition of the animal (e.g., dogs, cats, etc.) in terms of the average weight of the particular animal breed. Animals (e.g., dogs, cats, etc.) of the same breed can have relatively uniform physical characteristics, such as size, coat color, physiology, and behavior, as compared to animals of a different breed. It is noted that the discussion below is focused on dogs, however, other companion animals and wild animals are intended to be covered by the scope of this disclosure and the present disclosure is not intended to be limited to dogs.

The dog can be any breed of dog, including toy, small, medium, large or giant breeds. Non-limiting examples of toy breeds include Affenpinscher, Australian Silky Terrier, Bichon Frise, Bolognese, Cavalier King Charles Spaniel, Chihuahua, Chinese Crested, Coton De Tulear, English Toy Terrier, Griffon Bruxellois, Havanese, Italian Greyhound, Japanese Chin, King Charles Spaniel, Lowchen (Little Lion Dog), Maltese, Miniature Pinscher, Papillon, Pekingese, Pomeranian, Pug, Russian Toy, and Yorkshire Terrier. Examples of small breeds include, but are not limited to, French Bulldog, Beagle, Dachshund, Pembroke Welsh Corgi, Miniature Schnauzer, Cavalier King Charles Spaniel, Shih Tzu, and Boston Terrier. Examples of medium dog breeds include, but are not limited to, Bulldog, Cocker Spaniel, Shetland Sheepdog, Border Collie, Basset Hound, Siberian Husky, Dalmatian, Doberman, Staffordshire Bull Terrier. Examples of large breed dogs include, but are not limited to, Bearded Collie, Great Dane, Neapolitan mastiff, Scottish Deerhound, Dogue de Bordeaux, Newfoundland, English mastiff, Saint Bernard, Leonberger, and Irish Wolfhound. Cross-breeds can generally be categorized as toy, small, medium, and large dogs depending on their body weight. In certain embodiments, the dog is a toy breed. In certain embodiments, the dog is a medium, large or giant breed. In some embodiments, the dog is a mix of two or more breeds. In such instances, the mixed-breed dog can still be categorized by size depending on their body weight and can exhibit traits (e.g., behavioral traits, genetic traits, etc.) associated with each of the two or more breeds found in the dog.

The Federation Cynologique Internationale currently recognizes 346 pure dog breeds. The breed of a dog can be identified, for example, either by observing its physical traits or by genetic analysis. A pedigree dog is the offspring of two dogs of the same breed, which is eligible for registration with a recognized club or society that maintain a register for dogs of that description. There are a number of pedigree dog registration schemes, of which the Kennel Club is the most well-known.

Table 1

In certain embodiments, the dog size categories are selected according to Salt et al., 2017 (Table 1) [27], In other embodiments, the dog size categories are selected according to alternative designations. A small breed can correspond with animals that have an average body weight of from about 6.5 kilograms to about 9 kilograms. A medium breed can correspond with an animal that has an average body weight between about 9 kilograms and about 30 kilograms. A large breed can correspond with an animal that has an average body weight of between about 30 kilograms and about 40 kilograms. A giant breed can correspond with an animal that has an average body weight of between over about 40 kilograms.

5.3 METHODS

Oral biometric measurements have been used as a tool to determine that certain breeds of dog appear to have less space available between teeth and larger teeth relative to their body weight. The former can translate to an increased rate of plaque accumulation and the latter a greater inflammatory stimulus per kg body weight resulting in earlier onset of gingivitis. These oral biometrics differences represent one of what can be many predisposing factors for increased susceptibility to periodontal disease in toy/small breed dogs.

The presently disclosed subject matter provides a method for determining the oral biometrics of a companion animal, which utilizes advanced tomography or scanning. Computerized tomography (CT) scans of dental casts/impressions of the upper jaw of the mouth of the animal are analyzed by software which calculates various oral biometrics (e.g., mesial-distal length, gingival margin perimeter, dental arch perimeter). The present disclosure demonstrates computerized analysis of dental cast scans provides a more accurate approach than existing standard techniques for obtaining oral biometrics, z.e., manual caliper measurements. In certain embodiments, the biometric measures can be incorporated with one or more additional measurements and/or risk factors to determine the periodontal risk of an individual. Further, in some embodiments, the measurements can then be used to generate a tailored oral care plan for the individual.

Determining oral biometrics

The presently disclosed subject matter provides novel methods for assessing the oral health of an animal based on oral biometrics. The present disclosure provides a method of determining the oral biometrics of an animal by analyzing a computer-generated 3D model of the oral cavity of the animal. In certain embodiment, such methods can be minimally invasive with improved accuracy. The biometric data can then be further analyzed against reference libraries of one or more data pools, with the resulting output being predictive indicators of oral health.

In certain embodiments, the method of determining the oral biometrics of an animal includes acquiring a computer-generated 3D model of the dental cast. In alternative embodiments, the 3D image can be created with an intraoral camera.

In certain embodiments, the computer-generated model can be created from a maxillary dental cast of the animal, which is quicker and more accurate for the animal as opposed to taking manual in mouth measurements. In certain embodiments, obtaining a maxillary dental cast includes obtaining a negative reproduction of the oral tissues of the animal. The negative reproduction of the oral tissues is obtained using an impression material. The impression material for producing the negative reproduction of the oral tissues can be a commercially available material. In certain embodiments, the impression material can comprise polysiloxane. In certain embodiments, a gypsum dental cast is produced from the impression of the negative reproduction of the oral tissues.

In certain embodiments, a dental cast is subjected to a computerized tomography (CT) scanner, wherein axial CT images of the dental cast are produced. In certain embodiment, the CT scanner can be a multi-slice helical CT machine capable 360-degree rotation. In certain embodiments, CT scanning can be carried out with a tube current of 80 mA at 120 KVP. In certain embodiment, the CT scanner can produce 2D images of the dental cast, wherein the images are stored in Digital Imaging and Communications in Medicine (DICOM) format. In certain embodiments, the data is stored and linked to individual electronic medical records. In certain embodiment, the images can be segmented using standard medical 3D image-based engineering software, wherein the segmented images can be used to produce a computergenerated 3D model of the dental cast. In particular embodiments, producing the 3D model of the dental cast can be based on 3D reconstruction model. The 3D reconstruction model can take in the segmented images and output the 3D model of the dental cast. The 3D reconstruction model can additionally utilize semantics associated with the segmented images. As an example and not by way of limitation, the semantics associated with a segmented image comprise a semantic label for each pixel indicating which oral cavity part the pixel belongs to. In particular embodiments, the 3D reconstruction model can be trained based on a plurality of training data comprising segmented images of dental casts associated with a specific dog breed. In particular embodiments, the 3D reconstruction model can be based on one or more neural networks. Accordingly, the computer-generated 3D model of the dental cast can provide an accurate replica of the animal’s mouth.

Once the 3D image of the oral cavity is created, dental cast scoring measurements are obtained. In certain embodiments, the dental cast scoring measurements can be performed using an imaging analysis software. In certain embodiments, the imaging analysis software is aided by user-guided commands. In alternative embodiments, the measurements are obtained using a computer-aided approach through automation of software processing to reduce manual annotation work, e.g., using machine learning. As an example and not by way of limitation, one or more machine-learning models can be trained for predicting dental cast scoring measurement. To train such machine-learning models, a plurality of training data can be utilized. Each training data can comprise a 3D image of an oral cavity associated with a dog of a particular dog breed and a corresponding label indicating the score measurement. In certain embodiment, the dental cast scoring can be obtained manually with caliper measurements. According to the disclosed subject matter, the dental cast model can be scored based on two or three or four of the following measurements: (1) individual mesio-distal length (mm) for each maxillary tooth except molars; (2) arch perimeter (mm); (3) gingival margin perimeter (mm) for four selected teeth; and (4) tooth surface area (mm²) for four selected teeth. In particular embodiments, the aforementioned machine-learning models for predicting dental cast scoring can be configured for outputting one or more measurements corresponding to one or more of the above measurements. As an example and not by way of limitation, given an input 3D image, the machine-learning models can output a respective measurement for the individual mesio-distal length for each maxillary tooth except molars, arch perimeter, gingival margin perimeter for four selected teeth, tooth surface area (mm²) for four selected teeth. These measurements can be used to further generate the scoring measurement. In particular embodiments, the machine-learning models configured for outputting measurements corresponding to the above measurements can be trained based on a plurality of training data. Each training data can comprise a 3D image of an oral cavity associated with a dog of a particular dog breed and labels indicating corresponding measurements of the above four measurements.

In certain embodiments, the individual mesio-distal length represents the tooth size and is the length between the mesial aspect and the distal aspect of a tooth. In certain embodiments, mesio-distal length measurements can be taken at the greatest mesio-distal width of each tooth regardless of teeth malposition.

In certain embodiments, the arch perimeter line represents the space available for accommodating the teeth in the jaw, wherein a line can be traced from the bucco-lingual centre of the distal aspect of the fourth premolars, through the dental arch and over the incisal edge of the incisors, terminating at the contact point between the central incisors. In certain embodiments, the arch perimeter line can be measured following the jaw shape, regardless of teeth malposition.

In certain embodiments, the gingival margin perimeter constitutes the area on the tooth, where the bacterial plaque interacts with the gingiva and represents the contact area for triggering inflammatory processes. In certain embodiment, the gingival margin perimeter can be measured at the interface between the most coronal point of the gingiva and the sulcular epithelium.

In certain embodiments, the tooth surface area constitutes the size of the crown and represents the tooth surface area available for plaque accumulation, z.e., total bacterial load. In certain embodiments, the tooth surface can be calculated as the area delimited by the gingival margin.

According to the presently disclosed subject matter, the measurement of the individual mesio-distal length, arch perimeter, gingival margin perimeter and tooth surface area can be used to assess oral health status (i.e., overcrowding), as one or more indicators for oral disease, e.g., periodontal disease. For example, in certain embodiments, the oral biometric comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or at least eight of the following measurement: (1) total mesio-distal length (sum of all individual mesio-distal lengths); (2) ratio of the total mesio-distal length and the arch perimeter; (3) ratio of the total mesio-distal length and body weight; (4) sum of the gingival margin perimeter for the four selected teeth; (5) ratio of the sum of the gingival margin perimeter and the arch perimeter; (6) ratio of the sum of the gingival margin perimeter and the body weight; (7) sum of the tooth surface area for the two selected teeth; (8) ratio of the sum of the tooth surface area and body weight; or combinations thereof.

In certain embodiments, the ratios of total mesio-distal length over arch perimeter and gingival margin perimeter over arch perimeter can be analysed as primary measures of oral biometrics as they relate to the local site of disease initiation. In certain embodiments, the ratios of the total mesio- distal length over arch perimeter, gingival margin perimeter over arch perimeter, and tooth surface area over body weight can be analysed as secondary measures to give insight into overall systemic burden and regulatory mechanisms of size.

According to the presently disclosed subject matter, the methods have confirmed differences in animal biometrics based on breed and breed size. Therefore, the measurements obtained in the disclosed subject matter can be compared to an average dataset for a breed or breed size to determine the level of tooth crowding within an individual animal. Tooth crowding on a basic level is based on the ratio of the total dental arch length divided by the sum of tooth length. Greater overcrowding then equates to an increased risk of plaque accumulation and therefore periodontal disease.

Oral biometrics for assessing periodontal health

The oral biometrics of the present method can be associated with periodontal health or periodontal disease. In certain embodiments, the presently disclosed methods can be combined with additional biomarkers (e.g., microbial, genomic, proteomic). For example, the disclosed methods can be combined with an oral microbiome diagnostic test.

In another aspect, the present disclosure provides methods implemented by a computer system for determining the animal oral health state. In certain embodiments, the methods comprise receiving input data for an animal, wherein the input data comprises at least a first array comprising a first plurality of entries, and each entry comprises a numerical value that indicates oral biometrics; determining, by a machine learning model, an animal oral health state based on the input data for the animal, wherein the animal oral health state identifies a predicted oral health state classification for the animal; obtaining the animal oral health state from the machine learning model; and outputting the animal oral health state.

In certain embodiments, the input data comprises quantified one or more oral biometric, associated with periodontal health or periodontal disease, from a computer-generated 3D dental cast. In certain embodiments, the one or more oral biometric is quantified by scoring a computer-generated 3D dental cast. In certain embodiments, the one or more oral biometric in the data set is associated with periodontal health or periodontal disease.

In certain embodiments, the methods further comprise: obtaining training data for a plurality of animals, wherein the training data indicates for the oral biometrics of each animal from among the plurality of animals; associating the training data with animal oral health state classifications, wherein associating the training data with the animal oral health state classifications comprises associating each animal from among the second plurality of animals with an animal oral health state classification; and training the machine learning model using the training data that is associated with the animal oral health state classifications.

In certain embodiments, the methods further comprise identifying, by the machine learning model, particular oral biometrics that are significantly associated with health, gingivitis, and/or periodontitis for determining the oral health state of the animal.

In certain embodiments, the input data further comprises one or more of an animal breed identifier, an animal size, an animal weight, an animal age, animal health information, animal diet, a geographical location information, a sample location, or a combination thereof.

In another aspect, the present disclosure provides a system comprising: one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors configured to execute the instructions to: receive input data for an animal, wherein the input data comprises at least a first array comprising a first plurality of entries, and each entry comprises a numerical value that indicates an oral biometric; determine, by a machine learning model, an animal oral health state based on the input data for the animal, wherein the animal oral health state identifies a predicted oral health state classification for the animal; obtain the animal oral health state from the machine learning model; and output the animal oral health state.

In a further aspect, the present disclosure provides a non-transitory computer-readable medium comprising: instructions that, when executed by one or more processors of a computing system, cause the one or more processors to: receive input data for an animal, wherein the input data comprises at least a first array comprising a first plurality of entries, and each entry comprises a numerical value that indicates an oral biometric; determine, by a machine learning model, an animal oral health state based on the input data for the animal, wherein the animal oral health state identifies a predicted oral health state classification for the animal; obtain the animal oral health state from the machine learning model; and output the animal oral health state.

For purpose of example and not limitation, a personalized report can be generated summarizing the results of the biometrics obtained from the analysis of the 3D dental cast model. In certain embodiments, electronic communications can be used to communicate the report. For example, a personalized report can be generated and sent to communicate the animal’s oral health status. In other embodiments, the report can be provided as a hard copy. The personalized report can, for example, include an indicator system such as a traffic light system, e.g., green, yellow, red, to communicate the oral health status of the animal. The personalized report can also include a representation of the scale as reference above and an indication of where the animal’s oral health falls on the scale, e.g., 0% is indicative of no disease and 100% is indicative of severe disease. In certain embodiments, the personalized report can be a personalized oral health care plan linked to a tailored care pathway with recommendations for the target animal, including but not limited to tooth brushing, treats, diet, veterinary treatment or assessments.

5.4 SYSTEMS

Figure 14 shows an example computer system 1400. In particular embodiments, one or more computer systems 1400 perform one or more steps of one or more methods described or illustrated herein. In particular embodiments, one or more computer systems 1400 provide functionality described or illustrated herein. In particular embodiments, software running on one or more computer systems 1400 performs one or more steps of one or more methods described or illustrated herein or provides functionality described or illustrated herein. Particular embodiments include one or more portions of one or more computer systems 1400. Herein, reference to a computer system may encompass a computing device, and vice versa, where appropriate. Moreover, reference to a computer system may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems 1400. This disclosure contemplates computer system 1400 taking any suitable physical form. As example and not by way of limitation, computer system 1400 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, or a combination of two or more of these. Where appropriate, computer system 1400 may include one or more computer systems 1400; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, one or more computer systems 1400 may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example and not by way of limitation, one or more computer systems 1400 may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems 1400 may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1400 includes a processor 1402, memory 1404, storage 1406, an input/output (I/O) interface 1408, a communication interface 1410, and a bus 1412. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1402 includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions, processor 1402 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 1404, or storage 1406; decode and execute them; and then write one or more results to an internal register, an internal cache, memory 1404, or storage 1406. In particular embodiments, processor 1402 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 1402 including any suitable number of any suitable internal caches, where appropriate. As an example and not by way of limitation, processor 1402 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in memory 1404 or storage 1406, and the instruction caches may speed up retrieval of those instructions by processor 1402. Data in the data caches may be copies of data in memory 1404 or storage 1406 for instructions executing at processor 1402 to operate on; the results of previous instructions executed at processor 1402 for access by subsequent instructions executing at processor 1402 or for writing to memory 1404 or storage 1406; or other suitable data. The data caches may speed up read or write operations by processor 1402. The TLBs may speed up virtual-address translation for processor 1402. In particular embodiments, processor 1402 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 1402 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 1402 may include one or more arithmetic logic units (ALUs); be a multicore processor; or include one or more processors 1402. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.

In particular embodiments, memory 1404 includes main memory for storing instructions for processor 1402 to execute or data for processor 1402 to operate on. As an example and not by way of limitation, computer system 1400 may load instructions from storage 1406 or another source (such as, for example, another computer system 1400) to memory 1404. Processor 1402 may then load the instructions from memory 1404 to an internal register or internal cache. To execute the instructions, processor 1402 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 1402 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor 1402 may then write one or more of those results to memory 1404. In particular embodiments, processor 1402 executes only instructions in one or more internal registers or internal caches or in memory 1404 (as opposed to storage 1406 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 1404 (as opposed to storage 1406 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor 1402 to memory 1404. Bus 1412 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 1402 and memory 1404 and facilitate accesses to memory 1404 requested by processor 1402. In particular embodiments, memory 1404 includes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory 1404 may include one or more memories 1404, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory. In particular embodiments, storage 1406 includes mass storage for data or instructions. As an example and not by way of limitation, storage 1406 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage 1406 may include removable or non-removable (or fixed) media, where appropriate. Storage 1406 may be internal or external to computer system 1400, where appropriate. In particular embodiments, storage 1406 is non-volatile, solid-state memory. In particular embodiments, storage 1406 includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. This disclosure contemplates mass storage 1406 taking any suitable physical form. Storage 1406 may include one or more storage control units facilitating communication between processor 1402 and storage 1406, where appropriate. Where appropriate, storage 1406 may include one or more storages 1406. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.

In particular embodiments, VO interface 1408 includes hardware, software, or both, providing one or more interfaces for communication between computer system 1400 and one or more VO devices. Computer system 1400 may include one or more of these VO devices, where appropriate. One or more of these VO devices may enable communication between a person and computer system 1400. As an example and not by way of limitation, an VO device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable VO device or a combination of two or more of these. An VO device may include one or more sensors. This disclosure contemplates any suitable VO devices and any suitable VO interfaces 1408 forthem. Where appropriate, VO interface 1408 may include one or more device or software drivers enabling processor 1402 to drive one or more of these VO devices. VO interface 1408 may include one or more VO interfaces 1408, where appropriate. Although this disclosure describes and illustrates a particular VO interface, this disclosure contemplates any suitable VO interface.

In particular embodiments, communication interface 1410 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packetbased communication) between computer system 1400 and one or more other computer systems 1400 or one or more networks. As an example and not by way of limitation, communication interface 1410 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface 1410 for it. As an example and not by way of limitation, computer system 1400 may communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, computer system 1400 may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination of two or more of these. Computer system 1400 may include any suitable communication interface 1410 for any of these networks, where appropriate. Communication interface 1410 may include one or more communication interfaces 1410, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.

In particular embodiments, bus 1412 includes hardware, software, or both coupling components of computer system 1400 to each other. As an example and not by way of limitation, bus 1412 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. Bus 1412 may include one or more buses 1412, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field- programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate.

6. EXAMPLES

For the purpose of understanding and not limitation, the presently disclosed subject matter will be better understood by reference to the following Example, which is provided as exemplary of the presently disclosed subject matter, and not by way of limitation.

EXAMPLE 1: DETERMINING ORAL BIOMETRICS OF A DOG

The present Example describes a 3D modelling methodology to quantify differences in canine dentition, specifically between dogs of different breed sizes.

Methods

Study cohort and dental cast acquisition. The WALTHAM Animal Welfare and Ethical Review Body reviewed and approved the study. The cohort consisted of two groups; 5 dogs in group I with bodyweight >10kg presented to Eastcott Veterinary Referrals (Swindon, UK) and 5 dogs in group II with bodyweight <10kg housed at the Waltham Petcare Science Institute. The average age of the dogs was 2.9 years (range 1.0 - 5.0) and the average bodyweight was 16.8 kg (range 2.8 - 39.1); three dogs were female (all neutered) and seven were male (three dogs entire, four dogs neutered). The details of the dogs used for the study shown in Figure 1. Each dog had all maxillary permanent teeth present with the exception of the molars and had no previous orthodontic treatment.

Maxillary dental casts from dogs were taken whilst under general anaesthesia for unrelated dental treatment; no dogs were anaesthetised solely for this study. Anaesthesia was induced with propofol (4 mg/kg) via a cephalic intravenous catheter. Following endotracheal intubation, anaesthesia was maintained with isoflurane in oxygen. All dogs received maintenance intravenous fluids (0.85% sodium chloride) at 2 ml/kg/hr during the general anaesthesia. Cardiorespiratory parameters, mucose membrane color, capillary refill time, temperature, eye position and palpebral/comeal reflexes, saturation of haemoglobin with oxygen (SpCh), capnography and blood pressure were measured and monitored throughout the general anaesthesia.

Polysiloxane impression material was used to obtain a negative reproduction of the oral tissues: express 2 hand mix putty soft refill (3M ESPE, UK) at Waltham and Lab Putty (Coltene, UK) at Eastcott Veterinary Referrals. A gypsum dental cast was obtained from each impression at a dental laboratory (Dental Precision, St Agnes, UK). Acquisition of the computer -generated 3D models from CT scans. Axial CT images were obtained from the dental casts using a Lightspeed Four Slice CT Scanner (General Electric, USA) with a 0.625 mm slice thickness in spiral mode. Scanning was carried out with a tube current of 80 mA at 120 KVP. The resultant 2D images were stored in Digital Imaging and Communications in Medicine (DICOM) format. The segmentation of the images was performed using Mimics® software (Materialise, Leuven, Belgium) and 3D models were obtained from each cast.

Software measurements and scoring panel. Linear measurements were performed by three different scorers using the Mimics® software. The CT images were presented in a randomised order to the three blinded scorers. Each scorer evaluated 14 images from the 10 dental casts; 8 casts were presented once, 2 casts (one each from group I and group II, different casts for each scorer) were presented three times to allow investigation of intra-ob server variability.

When scoring each cast image, the scorer measured: (1) Individual mesio-distal length (mm) for each maxillary tooth except molars; (2) arch perimeter (mm); (3) gingival margin perimeter (mm) for four selected teeth (104, 204, 108, 208); (4) tooth surface area (mm²) for four selected teeth (104, 204, 108, 208). The individual mesio-distal length represents the tooth size and is the length between the mesial aspect and the distal aspect of a tooth. Measurements were taken at the greatest mesio-distal width of each tooth regardless of teeth malposition (Figure 2).

The arch perimeter line represents the space available for accommodating the teeth in the jaw. It was traced from the buccolingual centre of the distal aspect of the fourth premolars, through the dental arch and over the incisal edge of the incisors, terminating at the contact point between the central incisors. It was measured following the jaw shape, regardless of teeth malposition (Figure 3).

The gingival margin perimeter constitutes the area on the tooth where the bacterial plaque interacts with the gingiva and represents the contact area for triggering inflammatory processes. It was measured at the interface between the most coronal point of the gingiva and the sulcular epithelium (Figure 4).

The tooth surface area constitutes the size of the crown and represents the tooth surface area available for plaque accumulation i.e., total bacterial load. It was calculated as the area delimited by the gingival margin. While the gingival margin perimeter and the tooth surface area were computed on the 3D model, the individual mesio-distal length and the arch perimeter were computed on a separate plane, to avoid over/underestimation in the measurements due to joining landmarks located at different heights. The mesial and distal aspect landmarks were carefully selected on each 3D model and projected onto a projection plane perpendicular to it. The projection plane was a plane located 1 cm above the canine teeth tips and perpendicular to the anatomical sagittal plane (Figure 5). Once the landmarks were projected onto this plane, both the individual mesio-distal lengths and the arch perimeter were computed.

From the three direct measures, further measures were calculated including: (1) total mesio-distal length (sum of all individual mesio-distal lengths); (2) ratio of the total mesio- distal length and the arch perimeter; (3) ratio of the total mesio-distal length and body weight; (4) sum of the gingival margin perimeter for the four selected teeth (104, 204, 108, 208); (5) ratio of the sum of the gingival margin perimeter and the arch perimeter; (6) ratio of the sum of the gingival margin perimeter and the body weight; (7) sum of the tooth surface area for the two selected teeth (108, 208); (8) ratio of the sum of the tooth surface area and body weight.

Prioritization of measures for analysis. The ratios of total mesio-distal length over arch perimeter and gingival margin perimeter over arch perimeter were analysed as primary measures as they relate to the local site of plaque accumulation and therefore disease initiation. Ratios of the same measures (total mesial-distal length; gingival margin perimeter) and tooth surface area over body weight were analysed as secondary measures as they give insights into overall systemic burden and regulatory mechanisms of size.

Comparison with the previous gold standard method. As previous human studies relied on calipers to measure biometrics, 8 casts were also subjected to physical caliper measurement to enable comparison between methods. Individual mesio-distal lengths of selected teeth (104, 204, 108 and 208) were measured using an electronic digital caliper (Guo Gen Digital Caliper, 0-150 mm, LCD display, China). The measurements were taken between the mesial aspect and the distal aspect of each tooth, at the greatest mesio-distal width, with the caliper tips parallel to the long axis of each tooth and were repeated three times on each cast, by the same investigator.

Statistical analysis. All statistical analyses were performed in R version 3.2.0 using lme4, multcomp, ggplot2 and snowfall packages [23],

To investigate differences between small and large breed dogs, a linear mixed effects model was fit for each of the primary and secondary measures. For all models, the fixed effect was size group, and the random structure was cast (which equates to dog) with scorer nested in cast. Assumptions of homoscedasticity were checked through visual inspection of residuals for each model. Heteroscedasticity of residuals was observed which was resolved through logic transformations of all responses. Estimates for all measure in both size groups are reported with confidence intervals. Between size group planned contrasts were performed and estimated fold changes with confidence intervals and -values are reported. The two primary measures were Bonferroni corrected to adjust for the increase in risk of false positives. For these measures, 97.5% confidence intervals are presented and threshold - value for significance was 0.025. For the secondary measures, no correction was applied, thus 95% confidence intervals are presented and a threshold -value of 0.05 was used.

To compare the methods of measuring mesio-distal length, two linear mixed models were fit; one to the caliper data and one to a subset of the CT image data only including the casts and teeth measured by the calipers (104, 108, 204, 208). In each case, the mesio-distal length was modelled against tooth, size group and their interaction. For the caliper model, the random effect was cast. For the CT image model, the random effects were scorer with cast nested in scorer. The variance components were extracted from each model to explore the relative magnitude of the method’s variability. As an additional comparison, a Bland- Altman plot was created. A linear model was fit to the caliper data modelling mesio-distal length against tooth, cast and their interaction. From this, the mesio-distal length for each tooth in each cast was estimated along with the residual variance. For each tooth in each cast, the CT image estimate was subtracted from the caliper estimate and this difference was plotted against the mean of the two estimates. Whilst taking into account repeated measures, 95% agreement limits were calculated according to Bland & Altman [24],

Power analyses by simulation were performed to assess the relative power of the methods. Since only four teeth were measured by the calipers and only in eight of the ten casts, the CT image data were subset to include only these teeth to allow a meaningful comparison between the methods. For both methodologies, the mesio-distal length was summed within each repeat on each cast and this sum was fit as the response in a linear mixed effects model. The fixed effect was size group for both models and the random structure was cast for the caliper model and scorer with cast nested in scorer for the CT image model. The variance components from these models were used to simulate data sets an appropriate variability structure, one scorer and one technical rep and an induced difference between groups. For each combination of method, number of dogs and size of induced effect, power is reported as the percentage of the 1000 simulated data sets where a significant difference between groups was detected.

Results

Primary measures

1. Total mesio-distal length/arch perimeter. The estimated average ratio of total mesio-distal length and arch perimeter was 0.846 (0.812, 0.881) for toy/small dogs (group II) and 0.782 (0.751, 0.814) for medium/large dogs (group I) (Figure 6). Comparing the size groups gave a significant fold change of 1.08 (1.03, 1.14) (/?=0.001) which, within this cohort, translates as small dogs had significantly less free space between teeth relative to the large dog group.

2. Gingival margin perimeter/arch perimeter. The average gingival margin perimeter over arch perimeter was 0.941 (0.900, 0.984) for toy/small dogs and 0.873 (0.835, 0.913) for medium/large dogs (Figure 7). Comparing size groups gave a significant fold change of 1.08 (1.02, 1.14) (/?=0.003) meaning, of the teeth studied (104, 108, 204, 208), the small dog group had significantly more tooth/plaque to gingiva interface area relative to their jaw length.

Secondary measures

1. Total mesio-distal length/kg BW. The average ratio of total mesio-distal length over body weight was 18.3 mm/kg BW (12.6, 26.6) for toy/small dogs and 6.09 mm/kg BW (4.19, 8.85) for medium/large dogs (Figure 8). Comparing the size groups gave a significant fold change of 3.00 (1.89, 4.77) (/?<0.001), demonstrating that the toy/small dog group had significantly more tooth length relative to body weight than the large dogs.

2. Gingival margin perimeter/kgBW. The average gingival margin perimeter over body weight was 20.4 mm/kg BW (14.1, 29.4) in toy/small dogs and 6.80 mm/kg BW (4.70, 9.84) in medium/large dogs (Figure 9). Comparing the size groups gave a significant fold change of 2.99 (1.89, 4.73) (/?<0.001), which means that, of the teeth studied (104, 108, 204, 208), the toy/ small dog group had significantly more tooth/plaque to gingiva interface area relative to their body weight.

3. Tooth surface area/kg BW. The average tooth surface area over body weight was 59.8 mm²/kg BW (43.0, 83.3) in toy/small dogs and 25.0 mm²/kg BW (18.0, 34.8) in medium/large dogs (Figure 10). Comparing the size groups gave a significant fold change of 2.39 (1.59, 3.60) (/?<0.001), meaning that, in the teeth studied (104, 108, 204, 208), the toy/small dog group had a greater surface area for plaque accumulation relative to body weight than the large dogs.

Method comparison

The variance components of the models fit to compare the methods of measuring mesio- distal length are shown in Table 2. Overall, the magnitude of the variability is very similar across the two methods, i.e. the new software method is as variable as the original caliper gold standard method. Variability is presented as %CV, i.e., 100*standard deviation/mean. No between scorer variability was quantifiable for the caliper method as only one scorer performed the measurements. In the software data, the between scorer variability is very small compared to the other two levels of variability, suggesting any trained scorer could be used, as they have a limited effect on variability. Figure 11 shows the Bland-Altman plot. The mean difference was -0.0363mm and the 95% agreement limits were ± 1.20 mm showing the methods have good agreement and on average were very similar.

Table 2. Variability of caliper and software models.

The power to detect differences between groups is higher for the CT image method than the calipers (Figure 12). To detect a difference of 5 mm with 80% power would require 14 dogs per group using the calipers or 11 dogs per group using the CT image method. For context, a 15 mm average difference was observed between the size groups using both methods.

Discussion

The Example demonstrates that there are significant differences in the oral biometrics of dogs of different breed size, particularly in the upper mandible. Toy/small dogs had a significantly higher total mesio-distal length:tooth arch ratio than medium/small dogs, translating as significantly less space between teeth. These findings are in agreement with a study of the jaws and teeth from a collection of 250 dog skulls [25], Although only the region of the maxilla posterior to the upper canines was examined in that study, the plot of the summed mesio-distal diameters relative to the available arch perimeter is consistent with the present disclosure (Figure 13). The data shows that toy and small breed dogs had significantly larger teeth relative to both their arch perimeter and body weight compared with the medium/large dogs. Thus, relative to their size, compared with large dogs, the teeth of toy and small breed dogs have a larger surface area for bacterial plaque to accumulate. This can lead to a greater plaque bacterial load per kg body weight. More importantly, this also applies to the gingival margin, the local site of inflammation at the tooth gum interface. In the case of the teeth studied (104, 204, 108, 208), the toy/small dog group had significantly greater gingival margin interface relative to their arch perimeter and body weight than the large dog group, meaning the contact area between plaque and gingiva and resulting inflammatory stimulus is proportionally greater in the toy/small dog group.

The disproportionate link between arch perimeter/body weight and tooth size can be a result of the selective breeding of smaller dogs. This indicates that the affected genetic mechanisms that regulate size of body mass are independent from those that regulate size of dentition.

Statistical comparison of the two methods, z.e., software-generated, and manual caliper measurements, showed that the methods have comparable variability and share good agreement. Software-generated and manual caliper measurements were within ± 1.20 mm, which can be considered a small and affordable margin of error. Additionally, the scorer had little effect on the variability of the new 3D modelling method. This provides confidence in using this alternative method which benefits in a reduced time to score, being unaffected to differences in scorer and affords the ability to obtain measurements regardless of what structures lie in the measurement path. The reliability of the software program had also been previously tested by Jamali etal. [26], who compared anthropometric measurements performed using Mimics® and a coordinate measurement machine (CMM). These two methods provided anthropometric measurements within 2 mm, which was considered an acceptable threshold value for the determination of level of accuracy and precision, based on review of the literature in a number of human medicine specialities.

In line with the 3Rs ethical framework, this Example refined a methodology resulting in a reduction in dogs required for the study cohort. The Example demonstrates a method that is both sensitive and reproducible enough to identify significant differences in selected dentition metrics with relatively small cohort numbers. Additionally, the Example allows for all dental casts to be taken from dogs under general anaesthesia for unrelated dental treatments, thus avoiding animals receiving additional GA’s and no CT radiation exposure.

The Example demonstrates significant differences in dentition between dogs of different breed sizes, which can explain an earlier onset of gingivitis and periodontal disease in toy and small dog breeds. The serves as a crucial step in establishing a link between oral biometrics and the incidence of PD. Once a relation between oral biometric and PD incidence is defined, identification of anomalies of the dentition would enable earlier treatment plans with benefits for the animal’s overall health. References

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Although the presently disclosed subject matter and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of the presently disclosed subject matter, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the presently disclosed subject matter. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.

Patents, patent applications, publications, product descriptions and protocols are cited throughout this application the disclosures of which are incorporated herein by reference in their entireties for all purposes.

Claims

WHAT IS CLAIMED IS:

1. A method of determining oral biometrics of an animal, the method comprising:

(a) obtaining a plurality of digital images of a dental cast from the animal;

(b) generating a three-dimensional (3D) dental cast model from the plurality of digital images;

(c) determining a dental cast score, based on dentition measurements of the 3D dental cast model; and

(d) determining, based on the dental cast score, the oral biometrics of an animal.

2. The method of claim 1, wherein the animal is of the genus Canis.

3. The method of claim 1, wherein the dental cast is a maxillary dental cast.

4. The method of claim 1, wherein obtaining a dental cast comprises:

(a) generating a dental impression of the oral tissues; and

(b) obtaining a dental cast from the impression of the oral tissues.

5. The method of claim 1, wherein the digital images of the dental cast are acquired by a computed tomography (CT) scanner.

6. The method of claim 5, wherein digital images of the dental cast are axial CT images.

7. The method of claim 6, wherein axial CT images are converted into a 3D model of the dental cast.

8. The method of claim 1, wherein the dental cast scoring comprises determining one or more linear and surface dentition measurements.

9. The method of claim 8, where the one or more linear and surface dentition measurements are selected from the group consisting of: individual mesio-distal length; arch perimeter; gingival margin perimeter for four teeth; tooth surface area for four teeth; and combinations.

10. The method of claim 9, wherein the linear and surface dentition measurements are computed to calculate one or more oral biometrics, wherein the oral biometric are selected from the group consisting of:

(a) total mesio-distal length;

(b) ratio of the total mesio-distal length and the arch perimeter;

(c) ratio of the total mesio-distal length and body weight;

(d) sum of the gingival margin perimeter for the four teeth;

(e) ratio of the sum of the gingival margin perimeter and the arch perimeter;

(f) ratio of the sum of the gingival margin perimeter and the body weight;

(g) sum of the tooth surface area for the two teeth;

(h) ratio of the sum of the tooth surface area and body weight; and

(i) combinations thereof.

11. The method of claim 9, wherein individual mesio-distal length for each maxillary tooth except molars comprises measurements taken at the greatest mesio-distal width of each tooth.

12. The method of claim 9, wherein the arch perimeter is measured following the jaw shape.

13. The method of claim 9, wherein the arch perimeter is traced from the buccolingual centre of the distal aspect of the fourth premolars, through the dental arch and over the incisal edge of the incisors, terminating at the contact point between the central incisors.

14. The method of claim 9, wherein the gingival margin perimeter is measured at the interface between the most coronal point of the gingiva and the sulcular epithelium.

15. The method of claim 9, wherein the tooth surface area comprises the tooth crown.

16. The method of claim 9, wherein the individual mesio-distal length and the arch perimeter measurements were computed on a projected plane located over the 3D model, wherein mesio and distal aspect landmarks are perpendicularly projected over the 3D model.

17. The method of claim 16, wherein the projected plane is located 1 cm above the canine teeth tip and perpendicular to the anatomical sagittal plane.

18. The method of claim 9, wherein gingival margin perimeter and the tooth surface area are computed on the 3D model.

19. A method for establishing a personalized oral health care plan for an animal, the method comprising:

(a) obtaining a plurality of digital images of a dental cast from the animal;

(b) generating a three-dimensional (3D) dental cast model from the plurality of digital images;

(c) determining a dental cast score, based on dentition measurements of the 3D dental cast model;

(d) determining, based on the dental cast score, oral biometrics of an animal; and

(e) using the oral biometrics to generate a personalized oral health care plan.

20. The method of claim 19, wherein the personalized oral health care plan includes one or more recommendations of tooth brushing, treats, diet, veterinary treatments or assessments, and combinations thereof.