CN113450899A - Intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images - Google Patents
Intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images Download PDFInfo
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- 230000002612 cardiopulmonary effect Effects 0.000 title claims abstract description 88
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 20
- 238000003745 diagnosis Methods 0.000 title claims abstract description 16
- 201000010099 disease Diseases 0.000 claims abstract description 21
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 21
- 210000004072 lung Anatomy 0.000 claims abstract description 14
- 238000000605 extraction Methods 0.000 claims description 20
- 210000000115 thoracic cavity Anatomy 0.000 claims description 13
- 210000000988 bone and bone Anatomy 0.000 claims description 10
- 210000000038 chest Anatomy 0.000 claims description 6
- 230000004927 fusion Effects 0.000 claims description 5
- 230000009466 transformation Effects 0.000 claims description 2
- 239000000284 extract Substances 0.000 description 4
- 238000009966 trimming Methods 0.000 description 4
- 238000013507 mapping Methods 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000001575 pathological effect Effects 0.000 description 2
- 208000019693 Lung disease Diseases 0.000 description 1
- 230000000747 cardiac effect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 208000019622 heart disease Diseases 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000002685 pulmonary effect Effects 0.000 description 1
- 238000013102 re-test Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Abstract
The invention provides an intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images, which comprises the following steps: step S1: acquiring a heart-lung positive image and a heart-lung side image, calculating the structural vertexes of the acquired heart-lung positive image and the heart-lung side image, and acquiring the spatial coordinates of heart-lung key points according to the structural vertexes; step S2: matching and comparing the space coordinates of the heart-lung key points with the reference coordinates on a pre-established standard heart-lung model; step S3: comparing the heart-lung model of the patient with the heart-lung model of the historical patient, and deriving a suspected disease corresponding to the comparison result; step S4: selecting a corresponding doctor list aiming at suspected diseases of a patient, and forming a recommended doctor in the doctor list; according to the invention, the patient heart-lung model is respectively compared with the sick heart model and the sick lung model in the database to generate a suspected disease, and the patient registers through the suspected disease, so that the medical treatment waiting time of the patient is reduced.
Description
Technical Field
The invention relates to the technical field of image marking, in particular to an intelligent diagnosis guiding method of cardiopulmonary examination images based on artificial intelligence.
Background
In recent years, the living environment of people is more and more seriously polluted, the probability of the disease of people is higher and higher, particularly the condition of the heart and lung diseases is judged, CT images are an important reference amount, and self-service CT detection equipment appears in some places at present, so that patients can take photos by self;
however, after the patient uses the self-service CT detection device to shoot the CT image, the patient often does not know the content presented by the CT image, and only can take the CT image to a hospital to inquire a doctor to know the suspected disease, and only can register a large upper department, and accurately guide to a related small department through the judgment of the doctor guided by the large department, so that the diagnosis time of the patient is delayed;
therefore, an intelligent diagnosis guide method capable of matching suspected diseases according to the CT image is urgently needed, so that the patient can know the suspected diseases suffered by the patient through the CT image, and the medical treatment waiting time of the patient is reduced.
Disclosure of Invention
In view of the above, the present invention provides an auxiliary labeling method for lung examination images, which is based on artificial intelligence, can automatically label the lung examination images and can perform a retest through the artificial intelligence.
In order to solve the technical problems, the invention adopts the technical scheme that: an intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images comprises the following steps:
an intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images comprises the following steps:
step S1: acquiring a heart-lung positive image and a heart-lung side image, calculating the structural vertexes of the acquired heart-lung positive image and the heart-lung side image, and acquiring the spatial coordinates of heart-lung key points according to the structural vertexes;
step S2: matching and comparing the space coordinates of the heart-lung key points with the reference coordinates on a pre-established standard heart-lung model;
step S3: comparing the heart-lung model of the patient with the heart-lung model of the historical patient, and deriving a suspected disease corresponding to the comparison result;
step S4: and selecting a corresponding doctor list aiming at the suspected disease of the patient, and forming a recommended doctor in the doctor list.
The step S1 includes:
s1-1, extracting two horizontal cross sections and a vertical longitudinal section through a horizontal armpit line, a horizontal waist line and a chest cavity center line of a standard heart-lung model which are created in advance; respectively extracting point clouds through two horizontal cross sections and a vertical longitudinal section; respectively fitting a horizontal armpit curve, an anterior chest cavity central line curve, a posterior chest cavity central line curve and a horizontal waist line curve through the extracted point cloud; and trimming the horizontal armpit curve, the anterior chest cavity central line curve, the posterior chest cavity central line curve and the horizontal waist line curve to form a space quadrangle by connecting the ends of the horizontal armpit curve, the anterior chest cavity central line curve, the posterior chest cavity central line curve and the horizontal waist line curve, wherein the formed space quadrangle is the heart-lung positive image extraction range of a photographer.
The step S1 includes:
s1-3, extracting two horizontal cross sections and a vertical longitudinal section through a horizontal armpit line, a horizontal waist line and a lateral chest center line of a standard heart-lung model which are created in advance; respectively extracting point clouds through two horizontal cross sections and a vertical longitudinal section; respectively fitting a horizontal armpit curve, a left chest centerline curve, a right chest centerline curve and a horizontal waist line curve through the extracted point cloud; and trimming the horizontal armpit curve, the left cavity center line curve, the right chest center line curve and the horizontal waist line curve to form a space quadrangle in an end-to-end connection mode, wherein the formed space quadrangle is the extraction range of the cardiopulmonary side-view image of the photographer.
The step S1 includes:
s1-3, extracting the structural vertex of the cardiopulmonary upright image shot in the cardiopulmonary upright image extraction range, and extracting the structural vertex of the cardiopulmonary side image shot in the cardiopulmonary side image extraction range.
The step S2 includes:
s2-1, when the comparison result is in a reasonable error value, removing the image of the bone tissue from the heart-lung positive image and the heart-lung side image;
if the comparison result is not within the reasonable error value, the procedure returns to step S1 to obtain the cardiopulmonary positive image and the cardiopulmonary side image again.
The step S2 includes:
s2-2, adopting X-ray with the illumination energy of M1 to carry out single scanning to obtain a cardiopulmonary positive image and a cardiopulmonary side image of the cardiopulmonary part of the patient under the X-ray with the energy of M1;
converting the CT numbers of the obtained cardiopulmonary positive image and cardiopulmonary side-view image of the patient into linear attenuation coefficients of the cardiopulmonary part of the patient under X-rays with the energy of M2 by using a bilinear conversion algorithm; and obtaining a cardiopulmonary positive image and a cardiopulmonary side image under X-ray with the energy of M2 through the linear attenuation coefficient.
And generating digital reconstruction images for the cardio-pulmonary positive images and the cardio-pulmonary side images generated twice respectively, and selecting the required cardio-pulmonary part weight factors according to the structural vertexes to obtain the images without the bone tissues.
The step S3 includes:
s3-1, the obtained image without the bone tissue is fused and mapped with a standard heart-lung model to form a heart-lung model of the patient.
The step S3 includes:
s3-2, separating the heart model and the lung model in the patient heart and lung model formed by the fusion map, and respectively comparing the sick heart model and the sick lung model in the database to form a suspected disease aggregate.
The step S4 includes:
s4-1, importing the doctor into a hospital consultation guiding system, and respectively forming the scheduling time and the number of people to be diagnosed in a recommended doctor list;
s4-2, importing an intelligent map system, and respectively forming recommended routes in a recommended doctor list;
and S4-3, combining the recommended distance, the scheduling time and the number of people to be diagnosed to form a recommended doctor.
The invention has the advantages and positive effects that:
firstly, fusing and mapping a cardiopulmonary positive image, a cardiopulmonary side image and a standard cardiopulmonary model to form a cardiopulmonary model of a patient, then separating the cardiopulmonary model from the cardiopulmonary model of the patient, respectively comparing a pathological cardiac model and a pathological pulmonary model in a database to generate a suspected disease, and registering the patient through the suspected disease to reduce the hospitalization waiting time of the patient.
The invention forms the image extraction range of the cardiopulmonary upright image and the cardiopulmonary side-looking image through the pre-established standard cardiopulmonary model, and the patient is in the extraction range for shooting, thereby avoiding the phenomenon of missed shooting and preventing the error in the judgment of the patient's condition.
And thirdly, forming a recommended doctor by importing the hospital diagnosis guide system and the intelligent map system according to the distance, the scheduling time and the number of people to be diagnosed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
In the drawings:
FIG. 1 is a flowchart illustrating steps S1-S4 of the intelligent cardiopulmonary examination image-based guidance method according to the present invention.
Fig. 2 is a flowchart illustrating the overall steps of the intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The invention provides an intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images, which comprises the following steps:
an intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images comprises the following steps:
it should be noted that a standard cardiopulmonary model has been established before the following steps are performed;
step S1: acquiring a heart-lung positive image and a heart-lung side image, calculating the structural vertexes of the acquired heart-lung positive image and the heart-lung side image, and acquiring the spatial coordinates of heart-lung key points according to the structural vertexes; on one hand, the method is prepared for modeling the cardiopulmonary image of the patient, and on the other hand, whether the patient has mistakes and omissions during shooting can be detected;
specifically, S1-1 extracts two horizontal cross sections and one vertical longitudinal section through the horizontal armpit line, the horizontal waist line and the center line of the chest of a standard heart-lung model created in advance; respectively extracting point clouds through two horizontal cross sections and a vertical longitudinal section; respectively fitting a horizontal armpit curve, an anterior chest cavity central line curve, a posterior chest cavity central line curve and a horizontal waist line curve through the extracted point cloud; trimming a horizontal armpit curve, a front chest cavity central line curve, a back chest cavity central line curve and a horizontal waist line curve to form a space quadrangle by connecting the ends of the horizontal armpit curve, the front chest cavity central line curve, the back chest cavity central line curve and the horizontal waist line curve, wherein the formed space quadrangle is the heart-lung positive image extraction range of a photographer, and the front of the upper body is positioned in the range when the patient takes a picture;
similarly, S1-2 extracts two horizontal cross sections and a vertical longitudinal section through the horizontal armpit line, the horizontal waist line and the side chest center line of the pre-created standard cardiopulmonary model; respectively extracting point clouds through two horizontal cross sections and a vertical longitudinal section; respectively fitting a horizontal armpit curve, a left chest centerline curve, a right chest centerline curve and a horizontal waist line curve through the extracted point cloud; trimming a horizontal armpit curve, a left cavity center line curve, a right chest cavity center line curve and a horizontal waist line curve to form a space quadrangle in an end-to-end connection mode, wherein the formed space quadrangle is the heart-lung side-shot image extraction range of a photographer, and the upper half body side is located in the range when the patient takes pictures;
the step creates a limit interval for the shooting of the patient, and the error between the spatial coordinates of the patient and the spatial coordinates of the standard heart-lung model is within a reasonable range only if the photographer is in the image shot in the limit interval.
Specifically, S1-3 extracts a structural vertex of the cardiopulmonary upright image captured within the cardiopulmonary upright image extraction range, extracts a structural vertex of the cardiopulmonary bypass image captured within the cardiopulmonary bypass image extraction range, and calculates spatial coordinates of the cardiopulmonary key points from the structural vertices.
Step S2: matching and comparing the space coordinates of the heart-lung key points with the reference coordinates on a pre-established standard heart-lung model; therefore, whether the patient is in the extraction range of the cardiopulmonary positive image and the extraction range of the cardiopulmonary side image when shooting is judged;
specifically, in S2-1, when the comparison result is within a reasonable error value, the cardiopulmonary upright image and the cardiopulmonary sideview image are removed, and when the comparison result is not within the reasonable error value, the method returns to S1 to re-acquire the cardiopulmonary upright image and the cardiopulmonary sideview image;
specifically, S2-2 adopts X-ray with the irradiation energy of M1 to perform single scanning, and obtains a cardiopulmonary frontal image and a cardiopulmonary side image of the cardiopulmonary part of the patient under the X-ray with the irradiation energy of M1;
converting the CT numbers of the obtained cardiopulmonary positive image and cardiopulmonary side-view image of the patient into linear attenuation coefficients of the cardiopulmonary part of the patient under X-rays with the energy of M2 by using a bilinear conversion algorithm; obtaining a cardiopulmonary positive image and a cardiopulmonary side image under X-ray with energy of M2 through the linear attenuation coefficient;
the bilinear transformation algorithm formula is as follows:
μx(M2)＝[1+HUx(M1)1000]·μw(M2)HU≤0[1+(ρH·R(M2)- 1)·HUx(M1)HUH(M1)]·μw(M2)HU>0---(1)
the parameters in the formula are illustrated below:
μ x (M2): linear attenuation coefficient of the substance to be converted under X-ray with energy M2;
HUx (M1): CT number of the substance needing to be converted under X-ray of original scanning energy M1;
μ w (M2): linear attenuation coefficient of water under X-ray with energy M2;
HUH (M1): CT number of high atomic number substance under X-ray with energy of M1;
ρ H: density of high atomic number species;
and generating digital reconstructed images for the cardiopulmonary positive images and the cardiopulmonary side images generated twice respectively, selecting the required cardiopulmonary weight factors according to the structural vertexes, substituting the gray value of the 120kVp digital reconstructed image pixels into IH, and substituting the gray value of the 80kVp digital reconstructed image pixels into IL to obtain the subtraction image with the bone tissues removed.
Step S3: comparing the heart-lung model of the patient with the heart-lung model of the historical patient, and deriving a suspected disease corresponding to the comparison result;
specifically, S3-1, the obtained image without the bone tissue is fused and mapped with a standard heart-lung model to form a heart-lung model of the patient;
s3-2, separating the heart model and the lung model in the patient heart and lung model formed by the fusion map, and respectively comparing the sick heart model and the sick lung model in the database to form a suspected disease aggregate.
Step S4: selecting a corresponding doctor list aiming at suspected diseases of a patient, and forming a recommended doctor in the doctor list;
specifically, S4-1, a hospital consultation guide system is introduced, and the scheduling time and the number of people to be diagnosed are respectively formed in a recommended doctor list;
s4-2, importing an intelligent map system, and respectively forming recommended routes in a recommended doctor list;
and S4-3, combining the recommended distance, the scheduling time and the number of people to be diagnosed to form a recommended doctor.
The working principle and the working process of the invention are as follows:
firstly, respectively shooting a positive image and a side image of a patient in a heart-lung positive image extraction range and a heart-lung side image extraction range;
calculating the structural vertexes of the obtained cardiopulmonary upright image and cardiopulmonary side-looking image, and obtaining the spatial coordinates of the cardiopulmonary key points according to the obtained structural vertexes;
matching and comparing the space coordinates of the heart-lung key points with the reference coordinates on a pre-established standard heart-lung model; therefore, whether the patient is in the extraction range of the cardiopulmonary positive image and the extraction range of the cardiopulmonary side image when shooting is judged;
when the comparison result is not in a reasonable error value, the steps are executed again, namely the cardiopulmonary positive image and the cardiopulmonary side image are obtained again;
when the comparison result is in a reasonable error value, removing the image of the bone tissue by using the heart-lung positive image and the heart-lung side image, performing fusion mapping on the obtained image without the bone tissue and a standard heart-lung model to form a heart-lung model of the patient, separating the heart model and the lung model in the heart-lung model of the patient formed by the fusion mapping, and respectively comparing the sick heart model and the sick lung model in the database to form a suspected disease aggregate;
and selecting a corresponding doctor list aiming at the suspected disease of the patient, and forming a recommended doctor in the doctor list.
The embodiments of the present invention have been described in detail, but the description is only for the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention should be covered by the present patent.
Claims (9)
1. An intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images is characterized by comprising the following steps:
step S1: acquiring a heart-lung positive image and a heart-lung side image, calculating the structural vertexes of the acquired heart-lung positive image and the heart-lung side image, and acquiring the spatial coordinates of heart-lung key points according to the structural vertexes;
step S2: matching and comparing the space coordinates of the heart-lung key points with the reference coordinates on a pre-established standard heart-lung model;
step S3: comparing the heart-lung model of the patient with the heart-lung model of the historical patient, and deriving a suspected disease corresponding to the comparison result;
step S4: and selecting a corresponding doctor list aiming at the suspected disease of the patient, and forming a recommended doctor in the doctor list.
2. The intelligent guidance method for cardiopulmonary examination images based on artificial intelligence of claim 1, wherein said step S1 comprises:
s1-1 forms a cardiopulmonary upright image extraction range through a horizontal armpit line, a horizontal waist line and a chest centerline of a standard cardiopulmonary model created in advance.
3. The intelligent guidance method for cardiopulmonary examination images based on artificial intelligence of claim 2, wherein said step S1 comprises:
s1-2, forming a cardiopulmonary side-shot image extraction range through a horizontal armpit line, a horizontal waist line and a side thoracic cavity central line of a standard cardiopulmonary model which are created in advance.
4. The intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination image according to claim 3, wherein said step S1 includes:
s1-3, extracting the structural vertex of the cardiopulmonary upright image shot in the cardiopulmonary upright image extraction range, and extracting the structural vertex of the cardiopulmonary side image shot in the cardiopulmonary side image extraction range.
5. The intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination image according to claim 4, wherein said step S2 includes:
s2-1, when the comparison result is in a reasonable error value, removing the image of the bone tissue from the heart-lung positive image and the heart-lung side image;
if the comparison result is not within the reasonable error value, the procedure returns to step S1 to obtain the cardiopulmonary positive image and the cardiopulmonary side image again.
6. The intelligent diagnosis guiding method based on artificial intelligence cardiopulmonary examination images of claim 5, wherein said step S2 comprises:
s2-2, obtaining an image with bone tissues removed by using a bilinear transformation algorithm.
7. The intelligent guidance method for cardiopulmonary examination images based on artificial intelligence of claim 6, wherein said step S3 comprises:
s3-1, the obtained image without the bone tissue is fused and mapped with a standard heart-lung model to form a heart-lung model of the patient.
8. The intelligent guidance method for cardiopulmonary examination images based on artificial intelligence of claim 7, wherein said step S3 comprises:
s3-2, separating the heart model and the lung model in the patient heart and lung model formed by the fusion map, and respectively comparing the sick heart model and the sick lung model in the database to form a suspected disease aggregate.
9. The intelligent guidance method for cardiopulmonary examination images based on artificial intelligence of claim 8, wherein said step S4 comprises:
s4-1, importing the doctor into a hospital consultation guiding system, and respectively forming the scheduling time and the number of people to be diagnosed in a recommended doctor list;
s4-2, importing an intelligent map system, and respectively forming recommended routes in a recommended doctor list;
and S4-3, combining the recommended distance, the scheduling time and the number of people to be diagnosed to form a recommended doctor.
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Application publication date: 20210928 |