Machine learning based strategy surpasses the traditional method for selecting the first trial Lens parameters for corneal refractive therapy in Chinese adolescents with myopia

Development and evaluation of a deep neural network model for orthokeratology lens fitting

PURPOSE

To optimise the precision and efficacy of orthokeratology, this investigation evaluated a deep neural network (DNN) model for lens fitting. The objective was to refine the standardisation of fitting procedures and curtail subjective evaluations, thereby augmenting patient safety in the context of increasing global myopia.

METHODS

A retrospective study of successful orthokeratology treatment was conducted on 266 patients, with 449 eyes being analysed. A DNN model with an 80%-20% training-validation split predicted lens parameters (curvature, power and diameter) using corneal topography and refractive indices. The model featured two hidden layers for precision.

RESULTS

The DNN model achieved mean absolute errors of 0.21 D for alignment curvature (AC), 0.19 D for target power (TP) and 0.02 mm for lens diameter (LD), with R2 values of 0.97, 0.95 and 0.91, respectively. Accuracy decreased for myopia of less than 1.00 D, astigmatism exceeding 2.00 D and corneal curvatures >45.00 D. Approximately, 2% of cases with unique physiological characteristics showed notable prediction variances.

CONCLUSION

While exhibiting high accuracy, the DNN model's limitations in specifying myopia, cylinder power and corneal curvature cases highlight the need for algorithmic refinement and clinical validation in orthokeratology practice.

Development of a Machine-Learning-Based Tool for Overnight Orthokeratology Lens Fitting

Purpose

Orthokeratology (ortho-K) is widely used to control myopia. Overnight ortho-K lens fitting with the selection of appropriate parameters is an important technique for achieving successful reductions in myopic refractive error. In this study, we developed a machine-learning model that could select ortho-K lens parameters at an expert level.

Methods

Machine-learning models were established to predict the optimal ortho-K parameters, including toric lens option (toric or non-toric), overall diameter (OAD; 10.5 or 11.0 mm), base curve (BC), return zone depth (RZD), landing zone angle (LZA), and lens sagittal depth (LensSag). The analysis included 547 eyes of 297 Korean adolescents with myopia or astigmatism. The dataset was randomly divided into training (80%, n = 437 eyes) and validation (20%, n = 110 eyes) sets at the patient level. The model was trained based on clinical ortho-K lens fitting performed by highly experienced experts and ophthalmic measurements.

Results

The final machine-learning models showed accuracies of 92.7% and 86.4% for predicting the toric lens option and OAD, respectively. The mean absolute errors for the BC, RZD, LZA, and LensSag predictions were 0.052 mm, 2.727 µm, 0.118°, and 5.215 µm, respectively. The machine-learning model outperformed the manufacturer's conventional initial lens selector in predicting BC and RZD.

Conclusions

We developed an expert-level machine-learning-based model for determining comprehensive ortho-K lens parameters. We also created a web-based application.

Translational Relevance

This model may provide more accurate fitting parameters for lenses than those of conventional calculations, thus reducing the need to rely on trial and error.

A machine learning-based algorithm for estimating the original corneal curvature based on corneal topography after orthokeratology

OBJECTIVE

To estimate the original corneal curvature after orthokeratology by applying a machine learning-based algorithm.

METHODS

A total of 497 right eyes of 497 patients undergoing overnight orthokeratology for myopia for more than 1 year were enrolled in this retrospective study. All patients were fitted with lenses from Paragon CRT. Corneal topography was obtained by a Sirius corneal topography system (CSO, Italy). Original flat K (K1) and original steep K (K2) were set as the targets of calculation. The importance of each variable was explored by Fisher's criterion. Two machine learning models were established to allow adaptation to more situations. Bagging Tree, Gaussian process, support vector machine (SVM), and decision tree were used for prediction.

RESULTS

K2 after one year of orthokeratology (K2after) was most important in the prediction of K1 and K2. Bagging Tree performed best in both models 1 and 2 for K1 prediction (R = 0.812, RMSE = 0.855 in model 1 and R = 0.812, RMSE = 0.858 in model 2) and K2 prediction (R = 0.831, RMSE = 0.898 in model 1 and R = 0.837, RMSE = 0.888 in model 2). In model 1, the difference was 0.006 ± 1.34 D (p = 0.93) between the predictive value of K1 and the true value of K1 (K1before) and was 0.005 ± 1.51 D(p = 0.94) between the predictive value of K2 and the true value of K2 (K2before). In model 2, the difference was -0.056 ± 1.75 D (p = 0.59) between the predictive value of K1 and K1before and was 0.017 ± 2.01 D(p = 0.88) between the predictive value of K2 and K2before.

CONCLUSION

Bagging Tree performed best in predicting K1 and K2. Machine learning can be applied to predict the corneal curvature for those who cannot provide the initial corneal parameters in the outpatient clinic, providing a relatively certain degree of reference for the refitting of the Ortho-k lenses.

Biometric factors and orthokeratology lens parameters can influence the treatment zone diameter on corneal topography in Corneal Refractive Therapy lens wearers

PURPOSE

To investigate the relationship between patients' baseline biometric factors or lens parameters and the diameter of the treatment zone in young myopic children undergoing Corneal Refractive Therapy.

METHODS

The data of patients undergoing Corneal Refractive Therapy lens treatment within two years were retrospectively reviewed. Baseline clinical data, including sex, age, refractive power, corneal topography readings, ocular optical biometric measurements, and Corneal Refractive Therapy lens parameters, were subjected to Pearson, Spearman, and partial correlation analyses to identify the potential factors that may influence treatment zone diameter on corneal topography. Logistic and linear regression analyses were used to predict the treatment zone size.

RESULTS

The Right eyes of 309 patients were included in this study. The spherical refraction, flat keratometric reading, Reverse Zone Depth 2, Landing Zone Angle 1, and lens diameter were independent factors of treatment zone diameter. In the multivariate analyses, Landing Zone Angle 1 was positively correlated, while Reverse Zone Depth 2 and lens diameter were negatively correlated with the size of the treatment area. The accuracy of logistic regression in predicting the treatment zone size was 71.5%.

CONCLUSION

Adjustments to Corneal Refractive Therapy lens parameters may influence the treatment zone diameter on corneal topography. A higher Reverse Zone Depth 2, smaller Landing Zone Angle 1, and larger lens diameter can lead to a smaller treatment zone for Corneal Refractive Therapy lens treatment.

Artificial intelligence technology for myopia challenges: A review

Myopia is a significant global health concern and affects human visual function, resulting in blurred vision at a distance. There are still many unsolved challenges in this field that require the help of new technologies. Currently, artificial intelligence (AI) technology is dominating medical image and data analysis and has been introduced to address challenges in the clinical practice of many ocular diseases. AI research in myopia is still in its early stages. Understanding the strengths and limitations of each AI method in specific tasks of myopia could be of great value and might help us to choose appropriate approaches for different tasks. This article reviews and elaborates on the technical details of AI methods applied for myopia risk prediction, screening and diagnosis, pathogenesis, and treatment.

Advances in artificial intelligence models and algorithms in the field of optometry

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.

Machine learning algorithm improves accuracy of ortho-K lens fitting in vision shaping treatment

PURPOSE

To construct a machine learning (ML)-based model for estimating the alignment curve (AC) curvature in orthokeratology lens fitting for vision shaping treatment (VST), which can minimize the number of lens trials, improving efficiency while maintaining accuracy, with regards to its improvement over a previous calculation method.

METHODS

Data were retrospectively collected from the clinical case files of 1271 myopic subjects (1271 right eyes). The AC curvatures calculated with a previously published algorithm were used as the target data sets. Four kinds of machine learning algorithms were implemented in the experimental analyses to predict the targeted AC curvatures: robust linear regression models, support vector machine (SVM) regression models with linear kernel functions, bagging decision trees, and Gaussian processes. The previously published calculation method and the novel machine learning method were then compared to assess the final parameters of ordered lenses.

RESULTS

The linear SVM and Gaussian process machine learning models achieved the best performance. The input variables included sex, age, horizontal visible iris diameter (HVID), spherical refraction (SER), cylindrical refraction, eccentricity value (e value), flat K (K1) and steep K (K2) readings, anterior chamber depth (ACD), and axial length (AL). The R-squared values for the output AC1K1, AC1K2 and AC2K1 values were 0.91, 0.84, and 0.73, respectively. The previous calculation method and machine learning methods displayed excellent consistency, and the proposed methods performed best based on flat K reading and e values.

CONCLUSIONS

The ML model can provide practitioners with an efficient method for estimating the AC curvatures of VST lenses and reducing the probability of cross-infection originating from trial lenses, which is especially useful during pandemics, such as that for COVID-19.