RU2810918C2 - Method and device for evaluating effectiveness of ophthalmological lens for control of vision impairments - Google Patents

Abstract

FIELD: optics.

SUBSTANCE: method for assessing the effectiveness of an ophthalmic lens for controlling visual impairment in an ophthalmic lens user includes obtaining data representing the optical characteristics of the ophthalmic lens, the characteristics of the wearer, and the behaviour of the wearer; and determining, based on the predetermined relationship model, the effectiveness of the ophthalmic lens. The specified relationship model compares the data representing the optical performance of an ophthalmic lens, user characteristics, and user behaviour with the data from a lens performance/efficiency database. The predetermined relationship model includes correlating or linking, using a mathematical function, a criterion, which is an optical index calculated based on the user characteristics and the user behaviour, and an efficiency factor, which is the specified efficiency, for each lens in the database.

EFFECT: ability to evaluate effectiveness of the lens in accordance with visual, behavioural and biometric parameters.

15 cl, 9 dwg, 2 tbl, 23 ex

Description

Field of technology

Various aspects of the present invention generally relate to evaluating the effectiveness of an ophthalmic lens for controlling visual impairment in a wearer, and more particularly, to methods and apparatus for determining lenses for controlling myopia or relieving visual fatigue.

BACKGROUND OF THE INVENTION

It has been observed that some people, particularly children, do not focus their vision accurately when viewing an object at a short distance, that is, in near vision conditions. Because of this focusing defect on the part of a nearsighted child whose distance vision is corrected, the image of a nearby object is also formed behind his retina, even in the foveal region.

Many types of products can be used to slow the progression of myopia, such as ophthalmic lenses, contact lenses, or medications. For example, to avoid the progression of myopia due to such a focusing defect, it is known to use myopia correction lenses that are of the progressive multifocal ophthalmic lens type. Bifocal lenses can also be an example of ophthalmic lenses that can be used to slow the progression of myopia.

Each person may respond differently to different myopia control products. However, since the progression of myopia is a long-term process, it is difficult for the ophthalmologist to evaluate the effectiveness of a given myopia control product for the user. Clinical trials and prototyping are time-consuming and expensive. A model that could provide an assessment of the effectiveness of a new solution for slowing the progression of myopia may be desirable so that significant time and money can be saved, either by reducing the number of lenses to test, by pre-selecting the most promising candidates, or by helping to develop a solution for myopia control.

Moreover, in clinical practice it is difficult to guess which product is best for a child. A model that ranks myopia solutions with high accuracy can greatly assist the physician in selecting the best product.

The essence of the invention

The following is a simplified summary provided to provide a basic understanding of the various aspects of the disclosed invention. This summary is not an extensive overview of all aspects contemplated and is not intended either to identify key or critical elements of all aspects or to define the scope of any or all aspects. The sole purpose is to present some concept of one or more aspects in a simplified form as a prelude to a more detailed description that will be presented later.

This invention describes a method for selecting or creating/designing lenses. In some embodiments, the method may be applied to lenses, for example, for myopia control, visual fatigue, etc. In some embodiments, a predictive model is provided that ranks myopia control solutions according to visual, behavioral, and biometric parameters. In some embodiments, a predictive model may be built based on the characteristics of the myopic eye, user behavior, and defocus induced by the myopia control solution.

In one aspect of the present invention, a method, computer-readable medium, and apparatus are provided for evaluating the effectiveness of an ophthalmic lens for controlling at least one visual impairment (eg, myopia) in at least one wearer of said ophthalmic lens. The device may obtain data representing the optical characteristics of the ophthalmic lens, the characteristics of the at least one user, and the behavior of the at least one user. The device may determine, using at least one processor, based on a given relationship model, the effectiveness of an ophthalmic lens for said at least one user from said representative data corresponding to said at least one user.

In another aspect of the present invention, there is provided a method, computer-readable medium, and apparatus for constructing a relationship model configured to provide the effectiveness of at least one ophthalmic lens for controlling at least one visual impairment (eg, myopia) in users of the at least one ophthalmic lens . The device may obtain data representing the optical characteristics of the at least one ophthalmic lens, the characteristics of the observed users, and the behavior of the observed users. The device may obtain monitored effectiveness corresponding to the specified representative data. The apparatus may construct, using at least one processor, a relationship model by correlating said representative data corresponding to said observed users with monitored performances. In another aspect of the present invention, there is provided a method, computer-readable medium, and apparatus for constructing a relationship model that is preferably configured to perform a method for evaluating the performance of an ophthalmic lens in any of the execution modes.

To achieve the above and related objects, the disclosed aspects include features fully described below and specifically set forth in the claims. The following description and accompanying drawings, presented in detail, illustrate certain features of aspects of the present invention. However, these features indicate only some of the various ways in which the principles of the various aspects may be used, and this description is intended to include all such aspects and their equivalents.

Brief description of graphic materials

In fig. 1 is a schematic diagram illustrating an example of a predictive model for myopia control.

In fig. 2 is a schematic diagram illustrating the pattern of defocus on the retina according to areas on the lens.

In fig. 3 is a schematic diagram illustrating a simplified example of the predictive model described in FIG. 1 and 2.

In fig. 4 is a schematic diagram illustrating another simplified example of the predictive model described in FIG. 1 and 2.

In fig. 5 is a graph showing the relationship between the defocus score and myopia control data for a given subject.

In fig. 6 is a flow diagram of a method for constructing a relationship model configured to provide the effectiveness of at least one ophthalmic lens to control at least one visual impairment in users wearing at least one ophthalmic lens.

In fig. 7 is a flow diagram of a method for assessing the effectiveness of an ophthalmic lens for controlling at least one visual impairment in at least one wearer of said ophthalmic lens.

In fig. 8 is a conceptual data flow diagram illustrating the flow of data between various means/components in an example apparatus.

In fig. 9 is a diagram illustrating an example of a hardware implementation of a device using a processing system.

Detailed description

The detailed description provided below in conjunction with the accompanying graphics is intended as a description of various possible configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details in order to ensure a thorough understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. In some cases, well-known structures and components are shown in block diagram form to avoid making such concepts difficult to understand.

Certain aspects of the definition of lenses for myopia control or visual fatigue relief will now be presented with reference to various devices and methods. The apparatus and methods will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. (collectively referred to as "elements"). These elements may be implemented using electronic hardware, computer software, or any combination thereof. Whether such elements are implemented in hardware or software depends on the specific application and the design constraints imposed on the overall system.

By way of example, an element, or any portion of an element, or any combination of elements may be implemented as a “processing system” that includes one or more processors, or one or more computing devices. Examples of processors include microprocessors, microcontrollers, graphics processing units (GPUs), central processing units (CPUs), application processors, digital signal processors (DSPs), reduced instruction set processors (RISC), systems on a chip (SoC), direct link processors , field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic gates, discrete hardware circuits, and other suitable hardware configured to perform the various functions described in this invention. One or more processors in a processing system may execute software. Software shall be broadly construed to mean instructions, sets of instructions, code, code segments, program code, programs, routines, software components, applications, software applications, software packages, procedures, routines, objects, executables, threads of execution, procedures, functions. etc., whether software, firmware, middleware, microcode, hardware description language or otherwise.

Accordingly, in one or more example embodiments, the described functions may be implemented in hardware, software, or any combination thereof. When implemented in software, the functions may be stored or encoded as one or more instructions or code on a machine-readable medium. Machine-readable media includes computer storage media. Storage media can be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media may include random access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), optical disk drive, magnetic disk drive, other magnetic storage devices, combinations of the above types of computer-readable media, or any other medium that can be used to store computer-executable code in the form of instructions or data structures that can be accessed by a computer.

The present invention describes a method for lens selection, or concept, or evaluation using user data (eg, medical prescriptions, lifestyle, lens wear time, etc.) and specific eye characteristics (eg, peripheral refraction or eye shape). In some embodiments, user data may be collected from an "average" user determined from population data, or from a given individual.

In some embodiments, a predictive model may be built based on the following steps: a) creating a criterion based on lens, eye, lifestyle parameters (the criterion may be referred to as an optical index, more specifically a defocus index (DI)); b) calculation of the criterion for one user and for existing lenses that have been tested for a certain effect; c) obtaining a performance coefficient for lens testing (eg, myopia control); d) correlation or linkage using a mathematical function of the criterion and efficiency coefficient for each lens.

In some embodiments, step d) of correlating or linking using a mathematical criterion function and a performance coefficient for each lens involves using a given relationship model.

In some embodiments, a predictive model may be used for the lens concept. For example, for a lens whose characteristics are unknown, the criterion can be calculated and obtained using a predictive model, the predictive performance coefficient for a standard user or group of standard users.

In some embodiments, a predictive model may be used to select a lens. For example, for a given user, the criteria may be recalculated for each lens based on the user's individual data. A new predictive model can be determined, and the most effective lens for a user can be selected based on the new predictive model for that user.

In some embodiments, a predictive model may be used to evaluate the lens. For example, for a standard user, a specific user, or a group of users (standard or special), the criteria may be recalculated for a specific lens. The predicted effect of a particular lens can be determined and compared with other lenses (either for each user or as an average effect).

In some embodiments, the predictive model may be updated based on new clinical trials testing new lenses (lens performance and performance) or a lens performance/performance database. In some embodiments, a group of standard users may have different refractions and other eye characteristics. A typical user may have eye characteristics that are characteristic of a specific group of users.

In some embodiments, a predictive model is provided that predicts myopia control performance based on a defocus score. A defocus score (criterion) can be calculated based on visual characteristics (eg, refraction, accommodative response lag), ocular or biometric characteristics (eg, peripheral refraction for each retinal area), lifestyle or behavior of the subject (eg, time spent in visual mode) near and far, usually at close range) and different lenses. The effectiveness of an ophthalmic lens for the control of a visual impairment in one or more ophthalmic lens wearers lies in the ability of the ophthalmic lens to produce the desired or intended result for the control of a visual impairment in one or more users. In some embodiments, efficiency may be defined as a percentage, for example, 100% is the highest efficiency and 0% is the lowest efficiency.

For each lens, a defocus index can be calculated. The defocus score is defined as the weighted sum of the defocus values in different areas of the retina corresponding to areas on the lens (for example, areas for near and distance vision).

In fig. 1 is a schematic diagram 100 illustrating an example of a predictive model for myopia control. The example shows retina 102 and lens 104. Retina 102 contains five regions: central 110, superior 112, inferior 114, temporal 116, and nasal 118. Lens 104 has two anchors or two regions (near vision point 120 and distance vision point 122). .

When looking through each of the two areas (near view point 120 and distance view point 122) of the lens 104, the retinal areas are associated with areas on the lens, that is, the optical power in areas 120 and 122 on the lens 104 gives a given defocus value on the retina 102 associated with the power of the lens in that part of the retina, the peripheral refraction in that part of the retina, and the delay of accommodation (usually considered zero for distance vision, but not necessarily).

In fig. 2 is a schematic representation 200 illustrating a defocus pattern on retina 102 corresponding to areas 120 and 122 on lens 104. As shown, each area on retina 102 has a defocus value corresponding to area 120 on lens 104, as well as a defocus value corresponding to area 122 on lens 104.

Therefore, the formula for calculating the defocus index of the retina 102 and lens 104 may be as follows:

DI = %FV * ((wCR * (CRx + CD _Far + Lag _Far )

+ wUR * (URx + UD _Far + Lag _Far )

+ wLR * (LRx + LD _Far + Lag _Far )

+ wTR * (TRx + TD _Far + Lag _Far )

+ wNR * (NRx + ND _Far + Lag _Far )) +

%NV * ((wCR * (CRx + CD _Near + Lag _Near )

+ wUR * (URx + UD _Near + Lag _Near )

+ wLR * (LRx + LD _Near + Lag _Near )

+ wTR * (TRx + TD _Near + Lag _Near )

+ wNR * (NRx + ND _Near + Lag _Near )), (1)

Where

User behavior parameters (lifestyle related):

%FV: Percentage of time spent in distance vision

%NV: Percentage of time spent in Near mode = 1 – %FV

Weighting factors for local parts of the retina (some parts of the retina are more conducive to emmetropization than others):

wCR: weighting factor for central retina

wUR: weighting factor for the upper retina

wLR: weighting factor for the lower retina

wTR: weighting factor for the temporal retina

wNR: weighting factor for the nasal retina

(Peripheral) relative refraction for each retinal area:

CRx: central refraction/correction

NRx: average relative peripheral refraction in the nasal retina

TRx: average relative peripheral refraction in the temporal retina

URx: average relative peripheral refraction in the upper retina

LRx: average relative peripheral refraction in the lower retina

Defocus induced by the lens on each area of the retina:

CD _Far : defocus imposed by a lens on the central part of the retina when looking through the lens point for distance vision

ND _Far : Defocus imposed by a lens on the nasal retina when looking through the lens point for distance vision

TD _Far : defocus imposed by a lens on the temporal retina when looking through the lens point for distance vision

UD _Far : defocus imposed by the lens at the top of the retina when looking through the lens point for distance vision

LD _Far : defocus imposed by a lens on the lower part of the retina when looking through the lens point for distance vision

CD _Near : defocus imposed by a lens on the central part of the retina when looking through the lens point for near vision

ND _Near : Defocus imposed by a lens on the nasal retina when looking through the lens point for near vision

TD _Near : defocus imposed by a lens on the temporal retina when looking through the lens point for near vision

UD _Near : Defocus imposed by a lens at the top of the retina when looking through the lens point for near vision

LD _Near : defocus imposed by a lens on the lower part of the retina when looking through the lens point for near vision

Accommodation error induced by a lens at a given close distance:

Lag _Far : The delay in accommodation imposed by a lens at a long distance (when looking at a distant object). It is assumed that the sign for the delay is negative. For advance accommodation, the sign will be positive. Usually the delay in distance vision is zero.

Lag _Near : The lag of accommodation imposed by a lens at near distance (when looking at a close object at a given distance). It is assumed that the sign for the delay is negative. For advance accommodation, the sign will be positive. Different close distances will have different delay values.

The defocus produced by a lens is proportional to the amount of light for a given lens power. That is, if a lens has, for example, two powers passing through the pupil with a 50/50% ratio, the defocus is 50% of one power + 50% of the other power.

This example can be extended to every point of the lens and retina with hundreds of points of lens and peripheral refractions.

In fig. 3 is a schematic diagram 300 illustrating a simplified example of the predictive model described above in FIG. 1 and 2. The example shows retina 302 and lens 304. Retina 302 contains only one region, that is, central 310. Lens 304 has two anchors or two regions (near vision point 320 and distance vision point 322). Thus, in the simplified example, there are only two areas on the lens 304 320 and 322, the accommodation lag at the near vision point 320 (the accommodation lag at the distance vision point 322 is considered zero), and there is no peripheral refraction in the simplified example. This model can be well adapted, for example, to bifocal progressive lenses. There is no need to measure peripheral refraction, which can be difficult and expensive. The defocus pattern on retina 302 corresponding to areas 320 and 322 on lens 304 is shown in FIG. 3.

Therefore, the formula for calculating the defocus index of the retina 302 and lens 304 may be as follows:

DI = %FV * (CRx + CD _Far ) + %NV * (CRx + CD _Near + Lag _Near ) (2)

Where

Lifestyle related parameters:

%FV: Percentage of time spent using distance vision

%NV: Percentage of time spent in Near mode = 1 – %FV

Defocus induced by the lens on each area of the retina:

CD _Far : defocus imposed by a lens on the central part of the retina when looking through the lens point for distance vision

CD _Near : defocus imposed by a lens on the central part of the retina when looking through the lens point for near vision

(Peripheral) relative refraction for each retinal area:

CRx: central refraction/correction

Accommodation error induced by a lens at a given close distance:

Lag _Near : The lag of accommodation imposed by a lens at near distance (when looking at a close object at a given distance). It is assumed that the sign for the delay is negative. For advance accommodation, the sign will be positive. Different close distances will have different delay values.

In fig. 4 is a schematic diagram 400 illustrating another simplified example of the predictive model described above in FIG. 1 and 2. The example shows a retina 402 and a lens 406. The retina 402 contains two regions, that is, a central 410 and a peripheral 412. There is only one anchor or region on the lens 406 (distance vision point 422). Thus, in a simplified example, only the distance vision point 422 is considered, with two regions (central 410 and peripheral 412) on the retina 402. The entire peripheral retina is assumed to have the same peripheral refractive value. This can be accommodated for concentric peripheral correction lenses. A defocus pattern on the retina 402 corresponding to a region 422 on the lens 406 is shown in FIG. 4.

Therefore, the formula for calculating the defocus index of the retina 402 and lens 406 may be as follows:

DI = (CRx + CD _Far ) + wUR * (PRx + PD _Far ) (3)

Where

(Peripheral) relative refraction for each retinal area:

CRx: central refraction/correction

PRx: average relative peripheral refraction in the peripheral retina

Defocus induced by the lens on each area of the retina:

CD _Far : defocus imposed by a lens on the central part of the retina when looking through the lens point for distance vision

PD _Far : defocus imposed by a lens on the periphery of the retina when looking through the lens point for distance vision.

In this example, the lens is considered to have the same power over the portion corresponding to the projection of the peripheral retina, or the average power in this area is considered.

Data on myopia control have been obtained in many clinical trials for many different lenses. The table below shows the average myopia reference values for various lenses as an example, as well as the DI for each lens calculated for the user:

Lens Single focal lens (SVL) Spectacle lens 1 for those with esophoria Spectacle lens 1 for those with exophoria Spectacle lens 2 Spectacle lens 3 Spectacle lens 4 Contact lens 1 Contact lens 2 Control role of myopia 0% 25% 10 % 50 % 15 % 60% 59% 45% Defocus indicator -2.7 -2.16667 -2.33389 -1.71667 -2.28153 0.2645 -0.32 -0.95

The above table is based on the following user characteristics:

%FV = 30%

%NV = 70%

Delay at 30 cm = 1 diopter (diopter)

Peripheral relative refraction Weight coefficient Central part of the retina 0 1 Upper retina -0.8 0.3 Lower retina -0.8 0.2 Temporal retina -0.8 0.3 Nasal retina -0.8 0.2

In fig. 5 is a graph 500 showing the relationship between a defocus score and myopia control data for a given subject. As shown, for this particular subject or average subject,

Myopia control (%) = 0.1983 * DI + 0.6325. (4)

In some embodiments, the predictive models described above with reference to FIGS. 1–5 can be used to evaluate new lenses. For example, for new spectacle lenses for myopia control with the same user data as above, it can be calculated that,

DI = -2.06,

Estimated myopia control efficiency = 0.1983 * -2.06 + 0.6325 = 22%.

Thanks to the predictive model, the average myopia control efficiency can be estimated at 22%. This assessment can help decide whether a new lens is promising enough to enter clinical trials or whether more work on the concept is needed.

In some embodiments, the predictive models described above with reference to FIGS. 1–5 can be used to select lenses for different children. For example, two parents with small children may visit an ophthalmologist. The first child is 6 years old; his refraction is -2.00 diopters (diopters) in both eyes; his near vision delay is 1 diopter; his phoria for near vision is -4 pdpt (prismatic dioptres), exophoria; time spent on activities requiring visual strain is 10% (likes to play football). The second child is 7 years old; her refraction in both eyes is -4.00 diopters; her near vision delay is 2 D; her near vision phoria is -4 pdpt, exophoria; time spent on activities requiring visual strain is 90% (likes to read, prone to sunburn).

Both children are quite small and have limited hygiene habits. Therefore, the optometrist may decide that contact lenses are not suitable. He may want to consider which spectacle lenses would be most appropriate for each child: single vision lenses or progressive-lens lenses (PAL).

Based on the predictive models described above in FIG. 1–5, DI can be calculated for each child using different lenses. For example, for the first child, the DI for a single vision lens might be -1.10; The DI for PAL can be -0.70. For the second child, the DI for single vision lenses may be -3.73; DI for PAL can be -4.12.

Therefore, for the first child, the optometrist may decide to prescribe PAL lenses (lower DI, hence better myopia control); and for the second child, the optometrist may recommend using a single vision lens because the DI suggests that PAL will increase myopia progression compared to a single vision lens.

In fig. 6 is a flow diagram 600 of a method for constructing a relationship model configured to provide the effectiveness of at least one ophthalmic lens to control at least one visual impairment (eg, myopia) in users of the at least one ophthalmic lens. In some embodiments, the method may be performed by a device (eg, device 802/802' shown in FIG. 8 or FIG. 9).

At step 602, the device may obtain data representing the optical characteristics of the at least one ophthalmic lens, the characteristics of the observed users, and the behavior of the observed users. In some embodiments, the behavior of the monitored users may include proportions of time spent in near and distance vision. In some embodiments, the optical characteristics of the at least one ophthalmic lens may include the optical power of the at least one ophthalmic lens at at least one region of the at least one ophthalmic lens. In some embodiments, at least one portion of the at least one ophthalmic lens may comprise at least one point from a near viewpoint or a distance viewpoint of the at least one ophthalmic lens. In some embodiments, the characteristics of the observed users may include at least one of the following: a central refraction, one or more peripheral refractions, or an accommodative response delay. In some embodiments, one or more peripheral refractions may be related to the shape of the eye; and the delay in the accommodative response may be related to the psychological profile of the observed users. In some embodiments, at least a portion of the characteristics of the observed wearers may be caused by at least one ophthalmic lens.

Lag of accommodation is insufficient accommodation that results in blurriness. In a person who is not sensitive to blur, the delay of accommodation is higher. Sensitivity to blurriness is related to a person's personality, which is also considered as part of the psychological profile.

A Relationship between Tolearance of blur and Personality, IOVS, November 20110, Vol. 51, No. 11, Woods et al., states that factors suggest that people who lack self-confidence , requires strong evidence of blurriness before they become annoyed by the blurry image or that caused by the visual device. Moreover, disorganized people may tolerate blurriness because it is simply another manifestation of their disordered personal environment. These two factors predicted blurriness tolerance better than perceived general psychological adaptation to sensory input.

At block 604, the device may obtain monitored efficiencies corresponding to the specified representative data. In some embodiments, representative data and monitored performance may be obtained from clinical data regarding at least one ophthalmic lens. In some embodiments, the characteristics of the observed users and the behavior of the observed users may be continuously updated based on clinical trials.

At step 608, the device may build, using at least one processor, a relationship model by correlating said representative data corresponding to said observed users with monitored performances. In some embodiments, to construct a relationship model, the device may calculate an optical index for each ophthalmic lens of the at least one ophthalmic lens based on said optical characteristics of the ophthalmic lens, said characteristics of said observed users, and said behavior of said observed users, and correlate the optical indicators with the monitored performance . In some embodiments, the relationship model may represent a linear or nonlinear function between efficiency and optical index.

In some embodiments, the optical index may be calculated as a weighted function of the values at at least one retinal region of the observed users. In some embodiments, an optical index may be calculated based on one or more of the following: percentage of time spent in distance vision, percentage of time spent in near vision, a weighting factor for at least one retinal region of the observed users, refraction of at least for one region of the retina of the observed users, the optical powers of the ophthalmic lens corresponding to at least one region of the retina, or the latency of the accommodative response induced by the ophthalmic lens at a given distance. In such embodiments, the optical index may be the defocus index described above with reference to FIG. 1–5. In some embodiments, the optical index can be calculated using formula (1). In some embodiments, the optical index can be calculated using a modified or simplified version of formula (1), such as formula (2) or (3). In some embodiments, the ophthalmic lens powers corresponding to at least one retinal region may represent defocus caused by the lens on at least one retinal region, for example, one or more of CD _Far , ND _Far , TD _Far , UD _Far , LD _Far , CD _Near , ND _Near , TD _Near , UD _Near , LD _Near , CD _Far , CD _Near or PD _Far .

In fig. 7 is a flow diagram 700 of a method for assessing the effectiveness of an ophthalmic lens for controlling at least one visual impairment (eg, myopia) in at least one user of the ophthalmic lens. In some embodiments, the method may be performed by a device (eg, device 802/802' shown in FIG. 8 or FIG. 9).

At step 702, the device may obtain data representing the optical characteristics of the ophthalmic lens, the characteristics of the at least one user, and the behavior of the at least one user. In some embodiments, at least a portion of the at least one user's characteristics are caused by the ophthalmic lens. In some embodiments, the behavior of at least one user may include proportions of time spent in near and distance vision mode. In some embodiments, the optical characteristics of the ophthalmic lens may include the optical power of the ophthalmic lens at at least one region of the ophthalmic lens. In some embodiments, the at least one portion of the ophthalmic lens may comprise at least one point from a near viewpoint or a far viewpoint of the ophthalmic lens. In some embodiments, the characteristics of at least one user may include at least one of the following: a central refraction, one or more peripheral refractions, or an accommodative response delay. In some embodiments, one or more peripheral refractions may be related to the shape of the eye; and the delay in the accommodative response may be related to the psychological profile of at least one user.

At step 704, the device may determine, based on a given relationship model, the performance of an ophthalmic lens for said at least one user from said representative data corresponding to the at least one user. In some embodiments, said effectiveness may be accomplished by selecting a suitable ophthalmic lens suitable for controlling said at least one visual impairment in said at least one user. In such embodiments, selecting an appropriate ophthalmic lens may represent lens selection and evaluation as well as lens concept as the predicted performance ratio (efficiency) directed at specific users is aimed at selecting appropriate lenses for those users.

In some embodiments, a given relationship model may compare data representing the optical performance of an ophthalmic lens, the performance of the at least one user, and the behavior of the at least one user with data from a lens performance/performance database.

A group of normal users may have different refractions and other eye characteristics. A typical user may have eye characteristics that are characteristic of a specific group of users.

In some embodiments, data related to the refraction and eye characteristics of users is stored in a database.

In some embodiments, data related to the refraction and ocular characteristics of a standard user, which may have ocular characteristics that are representative of a particular group of users, is stored in a database.

In some embodiments, a given relationship model may compare data representing the optical characteristics of the ophthalmic lens, the characteristics of the at least one user, and the behavior of the at least one user with data relating to a standard user who may have eye characteristics that represent a particular group users.

In some embodiments, a given relationship model may compare data representing the behavior of the at least one user with data in a database of users and their corresponding behavior.

The predictive model can select an appropriate ophthalmic lens based on lens selection and evaluation, as well as lens concept, as dependent on the predictive efficiency factor (efficiency) and the characteristics of the user's eye, as well as the user's behavior.

In some embodiments, to determine the effectiveness of an ophthalmic lens, the device may calculate an optical index for the ophthalmic lens based on said optical characteristics of the ophthalmic lens, said characteristics of said at least one user, and said behavior of said at least one user, and determine the effectiveness of the ophthalmic lens from said optical indicator based on a given relationship model.

In some embodiments, the optical index may be calculated as a weighted function of values at at least one retinal region of at least one user. In some embodiments, an optical index may be calculated based on one or more of the following: percentage of time spent in distance vision, percentage of time spent in near vision, a weighting factor for at least one retinal region of at least one user, refraction for at least one retinal region of at least one user, the optical powers of the ophthalmic lens corresponding to the at least one retinal region, or the latency of the accommodative response induced by the ophthalmic lens at a given distance. In such embodiments, the optical index may be the defocus index described above with reference to FIG. 1–5. In some embodiments, the optical index can be calculated using formula (1). In some embodiments, the optical index can be calculated using a modified or simplified version of formula (1), such as formula (2) or (3). In some embodiments, the ophthalmic lens powers corresponding to at least one retinal region may represent defocus caused by the lens on at least one retinal region, for example, one or more of CD _Far , ND _Far , TD _Far , UD _Far , LD _Far , CD _Near , ND _Near , TD _Near , UD _Near , LD _Near , CD _Far , CD _Near or PD _Far .

In some embodiments, to determine the effectiveness of an ophthalmic lens, the device may select an ophthalmic lens for a user from among several ophthalmic lenses based on optical performance calculated for the ophthalmic lenses. Each of the optical metrics can be calculated at least in part based on user characteristics and user behavior.

In fig. 8 is a conceptual data flow block diagram 800 illustrating the flow of data between various means/components in an example device 802. Device 802 may represent a failing computing device or a system comprising multiple computing devices. Apparatus 802 may include an optical index calculation component 804 that calculates an optical index for an ophthalmic lens based on the optical characteristics of the ophthalmic lens, the characteristics of at least one user, and the behavior of at least one user. In one embodiment, the optical index calculation component 804 may perform the operations described above with reference to step 608 in FIG. 6 or step 704 in FIG. 7.

The device 802 may include a lens performance determination component 806 that determines the performance of an ophthalmic lens based on an optical index obtained from an optical index calculation component 804. In one embodiment, lens performance determination component 806 may perform the operations described above with reference to step 608 in FIG. 6 or step 704 in FIG. 7.

Apparatus 802 may include additional components that execute each of the algorithm blocks in the above block diagrams shown in FIGS. 6 and 7. Thus, each block in the above block diagrams shown in FIGS. 6 and 7 may be performed by a component, and the device may include one or more of these components. The components may be one or more hardware components specifically configured to execute the claimed processes/algorithms implemented by a processor configured to execute the claimed processes/algorithms stored on a computer readable medium for implementation by the processor, or some combination thereof.

In fig. 9 is a diagram 900 illustrating an example hardware implementation of a device 802' using a processing system 914. In some embodiments, the device 802' may be the device 802 described above with reference to FIG. 8. Device 802' may contain one or more computing devices. Processing system 914 may be implemented with a bus architecture, generally represented by bus 924. Bus 924 may comprise any number of connecting buses and bridges depending on the particular application of processing system 914 and general design constraints. Bus 924 connects together various circuits, including one or more processors and/or hardware components represented by processor 904, components 804, 806, and computer readable media/storage device 906. Bus 924 may also connect various other circuits, such as clock sources, peripheral devices, voltage regulators and power management circuits that are well known in the art and therefore will not be described further.

The processing system 914 includes a processor 904 coupled to a computer readable medium/storage device 906. The processor 904 is responsible for general processing, including execution of software stored on the computer readable medium/memory 906. The software, when executed by the processor 904, causes the processing system 914 to execute various functions described above for any specific device. Computer readable medium/storage device 906 may also be used to store data manipulated by processor 904 when executing software. Processing system 914 further includes components 804 and 806. The components may be software components running on processor 904, resident/stored on computer readable medium/storage device 906, one or more hardware components coupled to processor 904, or some combination thereof. .

Various aspects of the present invention will now be illustrated:

Example 1 is a method, computer program, or apparatus for evaluating the effectiveness of an ophthalmic lens to control at least one visual impairment in at least one wearer of said ophthalmic lens. The apparatus may obtain data representing the optical characteristics of an ophthalmic lens, the characteristics of said at least one user, and the behavior of said at least one user, and determine, based on a given relationship model, the performance of the ophthalmic lens for said at least one user from said representative data corresponding specified by at least one user. In some embodiments, said effectiveness may be accomplished by selecting a suitable ophthalmic lens suitable for controlling said at least one visual impairment in said at least one user.

In Example 2, the subject matter of Example 1 may optionally include that at least a portion of the at least one user's characteristics are caused by the ophthalmic lens.

In Example 3, the subject matter of Example 1 or Example 2 may optionally include that the behavior of at least one user may include proportions of time spent in near or far vision mode.

In Example 4, an object of the invention according to any of Examples 1-3 may optionally include that the optical characteristics of the ophthalmic lens may include the optical power of the ophthalmic lens at at least one region of the ophthalmic lens.

In Example 5, the subject matter of Example 4 may optionally include that at least one portion of the ophthalmic lens may comprise at least one point from a near viewpoint or a far viewpoint of the ophthalmic lens.

In Example 6, an object of the invention according to any of Examples 1-5 may optionally include that the characteristics of at least one user may include at least one of the following: central refraction, one or more peripheral refractions, or accommodative response delay.

In Example 7, the subject matter of Example 6 may optionally include that one or more peripheral refractions may be related to the shape of the eye, and the delay of accommodation may be related to the psychological profile of at least one user.

In Example 8, the subject matter of any of Examples 1-7 may optionally include that, to determine the effectiveness of an ophthalmic lens, the device may: calculate an optical index for the ophthalmic lens based on said optical characteristics of the ophthalmic lens, said characteristics of said at least one user, and said behavior of said at least one user; and determine the effectiveness of the ophthalmic lens according to the specified optical indicator based on the specified relationship model.

In Example 9, the subject matter of Example 8 may optionally include that the optical index can be calculated as a weighted function of values on at least one retinal region of at least one user.

In Example 10, the subject matter of Example 9 may optionally include that an optical index may be calculated based on one or more of: percentage of time spent in the distance mode, percentage of time spent in the near vision mode, a weighting factor for at least one a region of the retina of at least one user, a refraction for at least one region of the retina of the at least one user, powers of the ophthalmic lens corresponding to the at least one region of the retina, or a latency of the accommodative response induced by the ophthalmic lens at a given distance. In some embodiments, the optical index may be the defocus index described above with reference to FIG. 1–5. In some embodiments, the optical index can be calculated using formula (1). In some embodiments, the optical index can be calculated using a modified or simplified version of formula (1), such as formula (2) or (3). In some embodiments, the ophthalmic lens powers corresponding to at least one retinal region may represent defocus caused by the lens on at least one retinal region, for example, one or more of CD _Far , ND _Far , TD _Far , UD _Far , LD _Far , CD _Near , ND _Near , TD _Near , UD _Near , LD _Near , CD _Far , CD _Near or PD _Far .

In Example 11, the subject matter of any of Examples 8-10 may optionally include that, to determine the effectiveness of an ophthalmic lens, the device may select an ophthalmic lens for a user from a plurality of ophthalmic lenses based on a plurality of optical indices calculated for a plurality of ophthalmic lenses, wherein each of the plurality of optical metrics can be calculated at least in part based on user characteristics and user behavior.

Example 12 is a method or apparatus for constructing a relationship model configured to provide the effectiveness of at least one ophthalmic lens to control at least one visual impairment in users of the at least one ophthalmic lens. The device may: obtain data representing the optical characteristics of the at least one ophthalmic lens, the characteristics of observed users, and the behavior of observed users; obtain trackable performance corresponding to specified representative data; and build a relationship model by correlating said representative data corresponding to said observed users with monitored performance.

In Example 13, the subject matter of Example 12 may optionally include that at least a portion of the characteristics of the observed wearers may be caused by the at least one ophthalmic lens.

In Example 14, the subject matter of Example 12 or Example 13 may not necessarily include that the behavior of the monitored users may include proportions of time spent in near and distance vision.

In Example 15, an object of the invention according to any of Examples 12-14 may optionally include that the optical characteristics of the at least one ophthalmic lens may include the optical power of the at least one ophthalmic lens in at least one region of the at least one ophthalmic lens.

In Example 16, the subject matter of Example 15 may optionally include that at least one portion of the at least one ophthalmic lens may comprise at least one point from a near viewpoint or a distance viewpoint of the at least one ophthalmic lens.

In Example 17, the subject matter of any of Examples 12-16 may optionally include that the characteristics of the observed users may include at least one of the following: central refraction, one or more peripheral refractions, or accommodative response delay.

In Example 18, the subject matter of Example 17 may optionally include that one or more peripheral refractions may be related to the shape of the eye, and the delay of accommodation may be related to the psychological profile of the observed users.

In Example 19, the subject matter of any of Examples 12-18 may optionally include that, to construct a relationship model, the apparatus may: calculate an optical index for each ophthalmic lens of at least one ophthalmic lens based on said optical property of the ophthalmic lens, said characteristics of said observables users and specified behavior of specified observable users; and correlate optical performance with monitored performance.

In Example 20, the subject matter of Example 19 may optionally include that the optical index can be calculated as a weighted function of the values on at least one retinal site of the observed users.

In Example 21, the subject matter of Example 20 may optionally include that an optical index may be calculated based on one or more of: the percentage of time spent in the distance mode, the percentage of time spent in the near vision mode, a weighting factor for at least one a region of the retina of the observed users, a refraction for at least one region of the retina of the observed users, optical powers of the ophthalmic lens corresponding to at least one region of the retina, or a latency of the accommodative response induced by the ophthalmic lens at a given distance.

In Example 22, the subject matter of any of Examples 12 to 21 may optionally include that representative data and monitored performance may be obtained from clinical data relating to at least one ophthalmic lens.

In Example 23, the subject matter of any of Examples 12-22 may optionally include that the characteristics of the observed users and the behavior of the observed users can be continuously updated based on clinical trials.

One skilled in the art will understand that the terminology used herein is intended to describe various embodiments only and is not intended to limit the present invention. As used herein, singular forms are intended to include plural forms unless the context clearly indicates otherwise. Additionally, it will be understood that the terms “comprises” and/or “comprising,” when used herein, define the presence of the claimed features, integers, steps, operations, elements, and/or components, but do not exclude the presence or addition of one or several other features, integers, steps, operations, elements, components and/or groups thereof.

It will be understood that the particular order or hierarchy of blocks in the disclosed processes/flowcharts is illustrative of exemplary approaches. Based on design preferences, it is understood that the particular order or hierarchy of blocks in processes/flowcharts can be reordered. In addition, some blocks may be combined or excluded. In the accompanying claims, elements of the various blocks are presented in an exemplary order and are not intended to be limited to the particular order or hierarchy presented.

The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. Accordingly, the claims are not intended to be limited to the aspects shown herein, but are to be given the full scope consistent with the claims, in which reference to an element in the singular is not intended to mean "one and only one" unless specifically so indicated, but “one or more.” The word “illustrative” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “illustrative” should not necessarily be construed as preferable or superior to other aspects. Unless specifically stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B or C”, “one or more of A, B or C”, “at least one of A, B and C”, “one or more of A, B and C , and “A, B, C, or any combination thereof” includes any combination of A, B, and/or C, and may include multiple A, multiple B, or multiple C. In particular, combinations such as “at least one of A, B or C", "one or more of A, B or C", "at least one of A, B and C", "one or more of A, B, and C", and "A, B, C or any combination thereof" may be A only, B only, C only, A and B, A and C, B and C, or A and B and C, where any such combinations may contain one or more elements of A, B, or C. All structural and functional equivalents of elements of the various aspects described in this invention that are known or later become known to those skilled in the art are expressly incorporated herein by reference and are intended to be included in the claims. Moreover, nothing disclosed herein is intended to be made available to the public, whether or not such disclosure is expressly stated in the claims. The words “module”, “mechanism”, “element”, “device”, etc. cannot be a substitute for the word “means”. Thus, no element of the claim shall be construed as means plus function unless that element is expressly pronounced using the phrase "means for."

Claims (33)

1. A method for assessing the effectiveness of an ophthalmic lens for controlling at least one visual impairment in at least one user of said ophthalmic lens, comprising: obtaining data representing the optical characteristics of the ophthalmic lens, the characteristics of the at least one user, and the behavior of the at least one user; And determining, by the at least one processor, based on a predetermined model, performance relationships of an ophthalmic lens for said at least one user from said representative data corresponding to said at least one user, wherein the predetermined relationship model compares data representing the optical characteristics of the ophthalmic lens, the characteristics of the at least one user, and the behavior of the at least one user with data from a lens performance/performance database, and the predetermined relationship model includes correlating or linking, by a mathematical function, a criterion representing an optical index that is calculated at least in part based on user characteristics and user behavior, and an efficiency coefficient representing said effectiveness, for each lens in the database. 2. The method according to claim 1, characterized in that at least part of the characteristics of at least one user is determined by the ophthalmic lens. 3. Method according to any one of paragraphs. 1-2, characterized in that the behavior of at least one user includes proportions of time spent in near and distance vision mode. 4. Method according to any one of paragraphs. 1–3, characterized in that the optical characteristics of the ophthalmic lens include the optical power of the ophthalmic lens in at least one area of the ophthalmic lens. 5. The method of claim 4, wherein at least one portion of the ophthalmic lens comprises at least one point from the near viewpoint or the far viewpoint of the ophthalmic lens. 6. Method according to any one of paragraphs. 1-5, wherein the characteristics of at least one user include at least one of the following: central refraction, one or more peripheral refractions, or accommodative response delay. 7. The method according to claim 6, characterized in that one or more peripheral refractions are related to the shape of the eye, and the delay of the accommodative response is related to the psychological profile of at least one user. 8. Method according to any one of paragraphs. 1–7, characterized in that the determination of the effectiveness of an ophthalmic lens includes: calculating an optical index for the ophthalmic lens based on said optical characteristics of the ophthalmic lens, said characteristics of said at least one user, and said behavior of said at least one user; And determining the effectiveness of an ophthalmic lens according to a specified optical indicator based on a given relationship model, the optical index being the defocus index. 9. The method according to claim 8, characterized in that the optical index is calculated as a weighted function of values in at least one area of the retina of at least one user, wherein the optical index is calculated based on one or more of the following parameters: the percentage of time spent in the distance vision mode, the percentage of time spent in the near vision mode, a weighting coefficient for the specified at least one region of the retina of the observed users, a refraction for the specified at least at least one region of the retina of the observed users, the optical powers of the ophthalmic lens corresponding to the at least one region of the retina, and the delay of the accommodative response induced by the ophthalmic lens at a given distance. 10. The method of claim 9, wherein the optical index is calculated based on one or more of the following: percentage of time spent in distance vision, percentage of time spent in near vision, weighting factors for at least one retinal region eyes of at least one user, refraction for at least one retinal region of at least one user, optical powers of an ophthalmic lens corresponding to at least one retinal region, or an accommodative response delay caused by an ophthalmic lens at a given distance. 11. Method according to any one of paragraphs. 8-10, wherein determining the effectiveness of an ophthalmic lens includes selecting an ophthalmic lens for a user from a plurality of ophthalmic lenses based on a plurality of optical indices calculated for the plurality of ophthalmic lenses, wherein each of the plurality of optical indices is calculated at least in part based on the user's characteristics and user behavior. 12. A device for assessing the effectiveness of an ophthalmic lens to control at least one visual impairment in at least one user of said ophthalmic lens, comprising: Memory device; And at least one processor connected to the storage device and configured to: obtaining data representing the optical characteristics of the ophthalmic lens, the characteristics of the at least one user, and the behavior of the at least one user; And determining, based on a given model, the effectiveness relationships of an ophthalmic lens for said at least one user from said representative data corresponding to said at least one user, wherein the predetermined relationship model compares data representing the optical characteristics of the ophthalmic lens, the characteristics of the at least one user, and the behavior of the at least one user with data from a lens performance/performance database, and the predetermined relationship model includes correlating or linking, by a mathematical function, a criterion representing an optical index that is calculated at least in part based on user characteristics and user behavior, and an efficiency coefficient representing said effectiveness, for each lens in the database. 13. The device of claim 12, wherein the behavior of at least one user includes proportions of time spent in near and distance vision mode. 14. Device according to any one of paragraphs. 12–13, characterized in that the optical characteristics of the ophthalmic lens include the optical power of the ophthalmic lens in at least one area of the ophthalmic lens. 15. A computer-readable medium comprising a computer program for evaluating the effectiveness of an ophthalmic lens to control at least one visual impairment in at least one user of said ophthalmic lens, the computer executable code comprising instructions for: obtaining data representing the optical characteristics of the ophthalmic lens, the characteristics of the at least one user, and the behavior of the at least one user; And determining, based on a given model, the effectiveness relationships of an ophthalmic lens for said at least one user from said representative data corresponding to said at least one user, wherein the predetermined relationship model compares data representing the optical characteristics of the ophthalmic lens, the characteristics of the at least one user, and the behavior of the at least one user with data from a lens performance/performance database, and the predetermined relationship model includes correlating or linking, by a mathematical function, a criterion representing an optical index that is calculated at least in part based on user characteristics and user behavior, and an efficiency coefficient representing said effectiveness, for each lens in the database.