CN112700863A - Method for accurately evaluating diopter based on Scheimpflug anterior segment morphology and application - Google Patents
Method for accurately evaluating diopter based on Scheimpflug anterior segment morphology and application Download PDFInfo
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Abstract
The invention relates to a method for accurately evaluating diopter based on a Scheimpflug anterior segment form, which comprises the following steps: acquiring objective computer optometry data and anterior segment data of a patient as characteristics used for model learning; dividing a data set, and obtaining a training set and a verification set by using a ten-fold cross verification method; determining a loss function as a Root Mean Square Error (RMSE), solving a splitting standard function Gain by using a Taylor second-order expansion formula of the RMSE, and splitting leaves according to an optimal mode, namely a splitting mode with the maximum Gain; gradually generating each decision tree model to obtain a base classifier until the number of the set maximum trees is reached; and combining the base classifiers to obtain a final model. The method solves the bottleneck problem of insufficient data acquisition accuracy caused by the limitation of conditions such as large related population, long consumed time, high requirement and the like in the current vision screening and clinical correction work of children and teenagers.
Description
Technical Field
The invention belongs to the technical field of ophthalmic medical diagnosis, relates to a machine learning technology, and particularly relates to a method for accurately evaluating diopter based on a Scheimpflug anterior segment form and application thereof.
Background
Diopter (D) is an important reference for the assessment of ametropia (myopia, hyperopia, astigmatism) as a unit of power, and its measurement is a dynamic, multi-procedural process, an important reference for the clinical diagnosis and correction of ametropia, and methods mainly rely on refraction, including subjective refraction and objective (visual and computer) refraction. At present, clinical optometry methods are numerous, and the principle of the optometry method is mainly based on computer optometry for calculating diopter through definition of infrared rays reflected by retina and subjective optometry for obtaining diopter through adjusting a lens group. However, in the computer optometry process, the examinee is required to watch the cursor to induce adjustment, which causes over-correction of myopia, under-correction of hyperopia and influence of astigmatism. Although the automatic fogged vision function exists, influence factors during examination are avoided to a certain extent, and the automatic fogged vision function is more convenient and efficient, but due to the influence of factors such as instability of human eye adjustment, different design principles of machines, peripheral environment and illumination during examination of patients and the like, the situation of inaccurate diopter assessment always exists. Meanwhile, subjective refraction is long in time consumption, the difference exists between the high cooperation of patients, particularly between self perception and expression of children patients, and the relatively accurate result is difficult to obtain due to the fact that inspectors need to have abundant clinical experience and other factors. To reduce the effects of strong and extremely unstable accommodation in the human eye, a cycloplegic (mydriatic) refraction is often used clinically to obtain relatively precise optical power. The method needs to be carried out in related medical institutions, meanwhile, the pupils of the examinees are scattered, so that the normal life is influenced due to short-term blurred vision caused by influence on the adjusting capacity, and the operability is not strong in large-scale vision general survey tasks.
Corneal topography examinations can provide numerous characteristics regarding the morphology of the anterior segment of the eye, including corneal curvature, corneal thickness, anterior chamber, aberrations, etc., in a wide range of clinical applications. But the accurate diopter of the whole eye of the patient cannot be obtained due to the machine design principle. The cornea is used as a main refraction medium of the human eye, the refractive power of the cornea accounts for 2/3 of the diopter of the whole eye, the Scheimpflug corneal topography map is also called as an anterior segment system of the eye, a plurality of parameters of the anterior segment of the eye can be comprehensively reflected, possible internal association also exists with the adjustment of the human eye, the related parameters are fully utilized, the internal relationship is excavated by a machine learning method in combination with quick objective computer optometry, or the actual diopter of the human eye can be accurately evaluated, and an optometrist is liberated to a certain extent, so that the clinical diagnosis and treatment efficiency is improved.
In the past, methods for improving computer optometry measurement results mainly remove noise based on optimization enhancement technologies such as image filtering processing and the like, so that acquired images are clearer and more accurate, and calculation accuracy is improved, so that measurement results are improved. With the maturity and application of artificial intelligence technologies such as machine learning, in particular, an extreme value gradient boost (XGBoost) algorithm proves extremely high performance in the tasks of prediction and diagnosis of clinical diseases such as stroke, tumor, diabetes and the like. Although some researches have been made on calculating subjective refraction results based on wavefront aberration parameters of human eyes by means of the method, various aberration measurement methods have been based on monochromatic light with a single wavelength, and the measured aberration is actually monochromatic aberration. When diopter is measured, white light enters human eyes and is decomposed into light with different wavelengths, and different aberrations, namely wavefront aberration, are generated respectively. Light of different wavelengths produces different results, and particularly has greater influence on low-order aberrations such as myopia, the measured refractive error in the whole visible light range can reach 2.00D, the measurement process needs specific environment, and the influence of external factors is greater. Meanwhile, an aberrometer (OPD-Scan III) based on a retina shadow-examination two-pass technology only collects the form of the anterior surface of the cornea and cannot reflect the change characteristics of the form of the anterior segment of the eye when the human eye gazes. The invention acquires more anterior segment related parameters (cornea front and back surface morphology, anterior chamber and the like) including human eye aberration based on a Scheimpflug anterior segment analysis system (Pentacam) to more comprehensively acquire the characteristics under the gazing dynamic state to fit the subjective refraction result. The examination method is widely applied to clinical diagnosis and treatment of ophthalmology in China, relates to children, teenagers and a plurality of ametropia patients, and has popularization and promotion significance.
Through searching, no patent publication related to the present patent application has been found.
Disclosure of Invention
The invention aims to overcome the defects of insufficient accuracy, inconvenience and low efficiency in diopter measurement in the clinical existing diagnosis technology in the prior art, and provides a diopter accurate evaluation method based on the Scheimpflug anterior ocular segment form and application.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for accurately evaluating diopter based on the Scheimpflug anterior segment morphology comprises the following steps:
step 1: acquiring objective computer optometry data and anterior segment data characteristics of a patient as characteristics used for model learning training, and taking subjective optometry data as label characteristics;
step 2: dividing a data set, and obtaining a training set and a verification set by using a ten-fold cross verification method;
and step 3: initialization model F0(x) For trees with only one root node, using the root mean square error RMSE, data set
In, xiIs the ith sample, N is the sample size, yi∈R；
And 4, step 4: in the last model F0(x) Loss function
Establishing a model in a gradient descending direction, wherein the model is established by adopting a gradient lifting algorithm XGboost, and the method comprises the following specific steps:
calculating all characteristics and selecting Gain values of different division points for each node;
secondly, selecting the features and the dividing points with the largest Gain value to divide the data set of the node into two parts until a stopping condition is met, namely the Gain value is smaller than a threshold value or reaches the height of a maximum tree;
a novel CART tree update model F obtained by using the threem＝Fm-1+ft；
Fourthly, repeatedly executing the step II until the iteration number reaches a set iteration number T;
obtaining the final regression model FM：
The XGboost adopts parameters adjusted by gridding search and is set as follows:
the iteration number is set to be 500, early stopping is used, the maximum depth of the tree is 2, the feature downsampling rate of each tree is 0.8, the feature downsampling rate of each node is 0.9, and the learning rate is 0.05.
Moreover, the characteristics used in model learning in step 1 include objective computer optometry, morphological parameters and anterior segment data, and the objective computer optometry and anterior segment data are matched and labeled.
Moreover, the morphological parameters comprise corneal astigmatism, corneal curvature, corneal aspheric parameters, corneal decentration values, corneal aberrations;
the anterior segment data includes anterior chamber volume.
And in the step 2, the data set is divided and the model performance is verified by using a cross validation method, that is, the data set is randomly and uniformly divided into 10 groups, 1 group is selected as a validation set each time, the rest groups are used as training sets, and the model is trained according to the ten-fold cross validation method.
The application of the method for diopter accurate assessment based on the Scheimpflug anterior segment morphology in diopter detection is disclosed.
Use of a method for the accurate assessment of diopter based on the morphology of the Scheimpflug eye anterior segment as described above for vision screening and/or myopia prevention and control and/or in clinical correction of vision.
The invention has the advantages and positive effects that:
1. the method for accurately evaluating the diopter based on the Scheimpflug anterior segment morphological parameters in the non-mydriatic state by means of the machine learning technology solves the bottleneck problem of insufficient data acquisition accuracy caused by the limitation of conditions such as large crowd, long time consumption, high requirement and the like in the current vision screening and clinical correction work of children and teenagers.
2. The method of the invention enables the patient to obtain the accurate diopter while receiving the corneal morphological parameters, various parameters of the anterior segment and the like obtained by one anterior segment examination, can save the flow of subjective refraction in the clinical diagnosis and treatment process, liberates optometrists to a certain extent, improves the clinical work efficiency and accuracy and shortens the diagnosis time of the patient.
3. The XGboost algorithm is applied to the diopter accurate evaluation task, and effective overall scheme design, flow optimization and algorithm parameter setting are performed for specific application examples. The evaluation accuracy reaches the clinical application standard through a large number of clinical example tests.
4. The method can realize accurate evaluation of the patient diopter under the non-mydriatic condition, and can be applied to screening of communities or campuses and clinical diagnosis and treatment scenes of hospitals. The bottleneck problem of insufficient data acquisition accuracy caused by the limitation of large crowd, long time consumption, high requirement and the like in the vision screening work can be solved. And overcome the not convenient problem inadequately of diopter measurement in present clinical work, liberate optometrist to a certain extent, promote work efficiency and degree of accuracy, reduce patient's time of seeing a doctor.
5. The method provided by the invention is based on a machine learning extreme gradient boost (XGboost) model, the technology has the advantages of strong learning and generalization capabilities, can automatically process missing values and the like, comprehensively analyzes corneal morphological parameters and objective computer optometry results, constructs an intelligent diopter accurate evaluation model, and provides an efficient and accurate auxiliary tool for clinic.
Drawings
Fig. 1 is a simplified flow chart of the diopter precision evaluation method based on the Scheimpflug eye anterior segment morphology in the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be illustrative, not limiting and are not intended to limit the scope of the invention.
The structures used in the present invention are all conventional structures unless otherwise specified; the methods used in the present invention are conventional in the art unless otherwise specified.
A method for accurately evaluating diopter based on the Scheimpflug anterior segment morphology comprises the following steps:
step 1: acquiring objective computer optometry data and anterior segment data characteristics of a patient as characteristics used for model learning training, and taking subjective optometry data as label characteristics;
step 2: dividing a data set, and obtaining a training set and a verification set by using a ten-fold cross verification method;
and step 3: initialization model F0(x) For trees with only one root node, using the root mean square error RMSE, data set
In, xiIs the ith sample, N is the sample size, yi∈R；
And 4, step 4: in the last model F0(x) Loss function
Establishing a model in a gradient descending direction, wherein the model is established by adopting a gradient lifting algorithm XGboost, and the method comprises the following specific steps:
calculating all characteristics and selecting Gain values of different division points for each node;
secondly, selecting the features and the dividing points with the largest Gain value to divide the data set of the node into two parts until a stopping condition is met, namely the Gain value is smaller than a threshold value or reaches the height of a maximum tree;
a novel CART tree update model F obtained by using the threem＝Fm-1+ft；
Fourthly, repeatedly executing the step II until the iteration number reaches a set iteration number T;
obtaining the final regression model FM：
The XGboost adopts parameters adjusted by gridding search and is set as follows:
the iteration number is set to be 500, early stopping is used, the maximum depth of the tree is 2, the feature downsampling rate of each tree is 0.8, the feature downsampling rate of each node is 0.9, and the learning rate is 0.05.
Preferably, the characteristics used in model learning in step 1 include objective computer optometry, morphological parameters and anterior segment data, and the objective computer optometry and anterior segment data are matched and labeled.
Preferably, the morphological parameters comprise corneal astigmatism, corneal curvature, corneal aspheric parameters, corneal eccentricity values and corneal aberrations;
the anterior segment data includes anterior chamber volume.
Preferably, in the step 2, the data set is divided and the model performance is verified by using a cross validation method, that is, the data set is randomly and uniformly divided into 10 groups, 1 group is selected as a validation set each time, the rest groups are used as training sets, and the model is trained according to the ten-fold cross validation method.
The application of the method for diopter accurate assessment based on the Scheimpflug anterior segment morphology in diopter detection is disclosed.
The method based on the accurate diopter assessment of the Scheimpflug eye anterior segment morphology is applied to vision screening and/or myopia prevention and control and/or ophthalmic clinical diagnosis and treatment.
Specifically, the preparation and detection are as follows:
a method for diopter accurate assessment based on Scheimpflug anterior segment morphology, fig. 1 shows a flow chart of the method, comprising the following steps:
the XGBoost intelligent algorithm is an engineering realization of a Gradient Boosting Decision Tree (GBDT), and overcomes the defects of the GBDT. The original GBDT algorithm constructs a new decision tree based on the negative gradient of the empirical loss function, and pruning is performed only after the decision tree is constructed. The XGboost adds a regular term in the stage of constructing the decision tree, namely
Wherein, Ft-1(xi) Representing the optimal solution of the existing t-1 trees, and the regular term about the tree structure is defined as
Wherein T is the number of leaf nodes, wjRepresenting the predicted value of the jth leaf node for which the penalty function is at Ft-1The second order Taylor expansion can be deduced
Wherein T is a decision tree ftThe number of the nodes of the middle leaf is,
Ija set of indices representing all samples belonging to a leaf node j.
Assuming that the structure of the decision tree is known, by making the penalty function relative to wjThe derivative of (1) is 0, the prediction value of each leaf node in the case of the minimum loss function can be obtained
However, it is an NP-hard problem to find the optimal tree structure from all the tree structures, so in practice, a greedy method is often adopted to construct a suboptimal tree structure, and the basic idea is to split one leaf node at a time from a root node, and select the optimal split according to a specific criterion for each possible split. Different decision trees adopt different criteria, such as information gain adopted by an ID3 algorithm, an information gain ratio adopted by a C4.5 algorithm in order to overcome the characteristic that the information gain is prone to be biased to take more values, a CART algorithm adopts a Gini coefficient and a square error, and an XGboost also has a specific rule to select optimal splitting.
The minimum value of the loss function is usually obtained by substituting the predicted value into the loss function
It is easy to calculate the difference of the loss function before and after splitting as
The XGboost constructs a decision tree by maximizing the difference as a criterion, and searches for a corresponding splitting mode when the front and back phase difference of the loss function is maximum by traversing all values of the selected 237 attribute features. In addition, gamma plays a certain role in pre-pruning at the moment because the difference between the front and the back of the loss function is limited to be positive. The method comprises the following specific steps:
step 1: initialization model F0(x) For trees with only one root node, using the root mean square error RMSE, data set
In, xiIs the ith sample, N is the sample size, yi∈R；
Step 2: and establishing a model in the descending direction of the gradient of the last model loss function. The method comprises the following steps:
2.1: for each node, all features are calculated and Gain values for different partition points are selected
2.2: selecting the characteristic with the maximum Gain value and the division point to divide the data set of the node into two parts until a stop condition is met, namely the Gain value is smaller than a threshold value or the height of the maximum tree is reached
And step 3: updating a model with the derived new CART tree
Fm＝Fm-1+ft
And 4, step 4: repeatedly executing the step 2 until the iteration number reaches a set iteration number T;
and 5: to obtain a final regression model FM：
The XGboost adopts parameters adjusted by gridding search and is set as follows: the iteration number is set to be 500, early stopping is used, the maximum depth of the tree is 2, the feature downsampling rate of each tree is 0.8, the feature downsampling rate of each node is 0.9, and the learning rate is 0.05.
Step 6: and predicting subjective refraction values of new cases by using the XGboost prediction model.
The significance of the features automatically learned by the final model is as follows, which can be used for later input of more complex models: automatically objective computer optometry equivalent sphere power (SE); morphological parameters such as corneal astigmatism, curvature, aspheric parameters and eccentricity values; anterior chamber volume and corneal aberration related parameters.
The correctness of the accurate evaluation results for patients with different degrees of diopter by the method of the present invention will be illustrated by three embodiments as follows:
the above examples are illustrated for three groups of low, moderate and high myopia respectively. Subjective refraction is obtained by optometrists, a large error exists between visual computer refraction and subjective refraction, and the error rate between AI prediction results and subjective refraction is obviously improved through comparison display. This will reduce patient's time of seeing a doctor, greatly promotes clinical work efficiency.
The application scene of the invention lies in campus and community screening and hospital use, and aims to improve the working efficiency and accuracy of vision screening and myopia prevention and control in China.
Although the embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and therefore the scope of the invention is not limited to the embodiments disclosed.
Claims (6)
1. A method for accurately evaluating diopter based on a Scheimpflug anterior segment form is characterized by comprising the following steps of: the method comprises the following steps:
step 1: acquiring objective computer optometry data and anterior segment data characteristics of a patient as characteristics used for model learning training;
step 2: dividing a data set, and obtaining a training set and a verification set by using a ten-fold cross verification method;
and step 3: initialization model F0(x) For trees with only one root node, using the root mean square error RMSE, data set
In, xiIs the ith sample, N is the sample size, yi∈R；
And 4, step 4: in the last model F0(x) Loss function
Establishing a model in a gradient descending direction, wherein the model is established by adopting a gradient lifting algorithm XGboost, and the method comprises the following specific steps:
calculating all characteristics and selecting Gain values of different division points for each node;
secondly, selecting the features and the dividing points with the largest Gain value to divide the data set of the node into two parts until a stopping condition is met, namely the Gain value is smaller than a threshold value or reaches the height of a maximum tree;
a novel CART tree update model F obtained by using the threem＝Fm-1+ft；
Fourthly, repeatedly executing the step II until the iteration number reaches a set iteration number T;
obtaining the final regression model fM：
The XGboost adopts parameters adjusted by gridding search and is set as follows:
the iteration number is set to be 500, early stopping is used, the maximum depth of the tree is 2, the feature downsampling rate of each tree is 0.8, the feature downsampling rate of each node is 0.9, and the learning rate is 0.05.
2. The method for diopter accurate assessment based on the Scheimpflug anterior ocular segment morphology of claim 1, wherein: the characteristics used in model learning in the step 1 comprise objective computer optometry, morphological parameters and anterior segment data, and are matched and labeled with the objective computer optometry and the anterior segment data.
3. The method for diopter accurate assessment based on the Scheimpflug anterior ocular segment morphology of claim 2, wherein: the morphological parameters comprise corneal astigmatism, corneal curvature, corneal aspheric parameters, corneal eccentricity values and corneal aberration;
the anterior segment data includes anterior chamber volume.
4. A method for accurate assessment of refractive power based on the morphology of the Scheimpflug eye anterior segment according to any of claims 1 to 3, wherein: in the step 2, the data set is divided and the model performance is verified by using a cross validation method, that is, the data set is randomly and uniformly divided into 10 groups, 1 group is selected as a validation set each time, the rest groups are used as training sets, and the model is trained according to the ten-fold cross validation method.
5. Use of a method for the accurate assessment of diopter based on the morphology of the Scheimpflug anterior segment of the eye according to any one of claims 1 to 4, in diopter detection.
6. Use of a method for the accurate assessment of diopter based on the morphology of the Scheimpflug anterior segment of the eye according to any one of claims 1 to 4, in vision screening and/or myopia prevention and control and/or in clinical correction of vision.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108335757A (en) * | 2018-02-05 | 2018-07-27 | 王雁 | A method of diopter adjusted value in prediction SMILE operations |
| CN109256207A (en) * | 2018-08-29 | 2019-01-22 | 王雁 | A method of based on XGBoost+SVM hybrid machine Learner diagnosis keratoconus case |
| CN111414972A (en) * | 2020-03-30 | 2020-07-14 | 王雁 | XGboost-based automatic eye refractive correction multi-source data classification method |
-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108335757A (en) * | 2018-02-05 | 2018-07-27 | 王雁 | A method of diopter adjusted value in prediction SMILE operations |
| CN109256207A (en) * | 2018-08-29 | 2019-01-22 | 王雁 | A method of based on XGBoost+SVM hybrid machine Learner diagnosis keratoconus case |
| CN111414972A (en) * | 2020-03-30 | 2020-07-14 | 王雁 | XGboost-based automatic eye refractive correction multi-source data classification method |
Non-Patent Citations (3)
| Title |
|---|
| TONG CUI等: ""Applying Machine Learning Techniques in Nomogram Prediction and Analysis for SMILE Treatment"", 《AMERICAN JOURNAL OF OPHTHALMOLOGY》 * |
| 何燕玲 等: ""Pentacam三维眼前节分析诊断系统对近视眼眼前节的测量"", 《眼科研究》 * |
| 黄日康: ""基于XGBoost算法的个人信用评分方案策划"", 《中国优秀硕士学位论文全文数据库 经济与管理科学辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115007634A (en) * | 2022-06-22 | 2022-09-06 | 浙江大学 | Method for repairing organic contaminated soil based on machine learning |
| CN115007634B (en) * | 2022-06-22 | 2022-12-06 | 浙江大学 | Method for repairing organic contaminated soil based on machine learning |
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