JMIR Publications

ABSTRACT

Background:

As machine learning (ML) becomes increasingly utilized in the field of medicine, some studies have applied it to ophthalmology, primarily for the diagnosis of pathologic myopia and high myopia-associated glaucoma, as well as for predicting the progression of high myopia. ML-based detection still requires evidence-based validation to prove its accuracy and feasibility.

Objective:

This study was executed to discern the performance of ML methods in detecting high myopia and pathologic myopia in clinical practice.

Methods:

PubMed, Cochrane, Embase, and Web of Science were thoroughly retrieved up to September 3, 2023. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was leveraged to appraise the risk of bias in the eligible studies. The meta-analysis was implemented employing a bivariate mixed-effects model. During the meta-analysis process, subgroup analyses by the objectives and methods of ML were executed.

Results:

This study ultimately included 45 studies, of which 32 were utilized for quantitative meta-analysis. The meta-analysis results unveiled that for the diagnosis of pathologic myopia, the summary receiver operating characteristic (SROC), sensitivity, and specificity of ML were 0.97 (95% CI: 0.95-0.98), 0.91 (95% CI: 0.89-0.92), and 0.95 (95% CI: 0.94-0.97), respectively. Specifically, deep learning (DL) showed an SROC of 0.97 (95% CI: 0.95-0.98), sensitivity of 0.92 (95% CI: 0.90-0.93), and specificity of 0.96 (95% CI: 0.95-0.97), while conventional ML (non-DL) showed an SROC of 0.86 (95% CI: 0.75-0.92), sensitivity of 0.77 (95% CI: 0.69-0.84), and specificity of 0.85 (95% CI: 0.75-0.92). For the diagnosis and prediction of high myopia, the SROC, sensitivity, and specificity of ML were 0.98 (95% CI: 0.96-0.99), 0.94 (95% CI: 0.90-0.96), and 0.94 (95%CI: 0.88-0.97), respectively. For the diagnosis of high myopia-associated glaucoma, the SROC, sensitivity, and specificity of ML were 0.96 (95% CI: 0.94-0.97), 0.92 (95% CI: 0.85-0.96), and 0.88 (95% CI: 0.67-0.96), respectively.

Conclusions:

ML, especially image-based DL, is relatively accurate in predicting and diagnosing myopia. DL can be used as a potential method for intelligent image processing and intelligent recognition, and intelligent examination tools can be developed in subsequent research to provide help for areas where medical resources are scarce.