Zuo Huiyi, Huang Baoyu, He Jian, Fang Liying, Huang Minli
Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China.
J Med Internet Res. 2025 Jan 3;27:e57644. doi: 10.2196/57644.
In recent years, with the rapid development of machine learning (ML), it has gained widespread attention from researchers in clinical practice. ML models appear to demonstrate promising accuracy in the diagnosis of complex diseases, as well as in predicting disease progression and prognosis. 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.
This study aims to discern the performance of ML methods in detecting high myopia and pathologic myopia in clinical practice, thereby providing evidence-based support for the future development and refinement of intelligent diagnostic or predictive tools.
PubMed, Cochrane, Embase, and Web of Science were thoroughly retrieved up to September 3, 2023. The prediction model risk of bias assessment tool was leveraged to appraise the risk of bias in the eligible studies. The meta-analysis was implemented using a bivariate mixed-effects model. In the validation set, subgroup analyses were conducted based on the ML target events (diagnosis and prediction of high myopia and diagnosis of pathological myopia and high myopia-associated glaucoma) and modeling methods.
This study ultimately included 45 studies, of which 32 were used 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.
ML demonstrated highly promising accuracy in diagnosing high myopia and pathologic myopia. Moreover, based on the limited evidence available, we also found that ML appeared to have favorable accuracy in predicting the risk of developing high myopia in the future. 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.
PROSPERO CRD42023470820; https://tinyurl.com/2xexp738.
近年来，随着机器学习（ML）的快速发展，它在临床实践中受到了研究人员的广泛关注。ML模型在复杂疾病的诊断以及预测疾病进展和预后方面似乎显示出了有前景的准确性。一些研究已将其应用于眼科，主要用于病理性近视和高度近视相关性青光眼的诊断，以及预测高度近视的进展。基于ML的检测仍需要循证验证以证明其准确性和可行性。
本研究旨在辨别ML方法在临床实践中检测高度近视和病理性近视的性能，从而为智能诊断或预测工具的未来发展和完善提供循证支持。
截至2023年9月3日，全面检索了PubMed、Cochrane、Embase和Web of Science。利用预测模型偏倚风险评估工具评估纳入研究中的偏倚风险。采用双变量混合效应模型进行荟萃分析。在验证集中，根据ML目标事件（高度近视的诊断和预测以及病理性近视和高度近视相关性青光眼的诊断）和建模方法进行亚组分析。
本研究最终纳入45项研究，其中32项用于定量荟萃分析。荟萃分析结果显示，对于病理性近视的诊断，ML的汇总受试者工作特征曲线（SROC）、敏感性和特异性分别为0.97（95%CI 0.95 - 0.98）、0.91（95%CI 0.89 - 0.92）和0.95（95%CI 0.94 - 0.97）。具体而言，深度学习（DL）的SROC为0.97（95%CI 0.95 - 0.98），敏感性为0.92（95%CI 0.90 - 0.93），特异性为0.96（95%CI 0.95 - 0.97），而传统ML（非DL）的SROC为0.86（95%CI 0.75 - 0.92），敏感性为0.77（95%CI 0.69 - 0.84），特异性为0.85（95%CI 0.75 - 0.92）。对于高度近视的诊断和预测，ML的SROC、敏感性和特异性分别为0.98（95%CI 0.96 - 0.99）、0.94（95%CI 0.90 - 0.96）和0.94（95%CI 0.88 - 0.97）。对于高度近视相关性青光眼的诊断，ML的SROC、敏感性和特异性分别为0.96（95%CI 0.94 - 0.97）、0.92（95%CI 0.85 - 0.96）和0.88（95%CI 0.67 - 0.96）。
ML在诊断高度近视和病理性近视方面显示出极具前景的准确性。此外，基于现有有限证据，我们还发现ML在预测未来发生高度近视的风险方面似乎具有良好的准确性。DL可作为智能图像处理和智能识别的潜在方法，后续研究可开发智能检查工具，为医疗资源稀缺地区提供帮助。
PROSPERO CRD42023470820；https://tinyurl.com/2xexp738 。
