Department of Ophthalmology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Sci Rep. 2021 Mar 26;11(1):6950. doi: 10.1038/s41598-021-85699-0.
Qualitative analysis of fundus photographs enables straightforward pattern recognition of advanced pathologic myopia. However, it has limitations in defining the classification of the degree or extent of early disease, such that it may be biased by subjective interpretation. In this study, we used the fovea, optic disc, and deepest point of the eye (DPE) as the three major markers (i.e., key indicators) of the posterior globe to quantify the relative tomographic elevation of the posterior sclera (TEPS). Using this quantitative index from eyes of 860 myopic patients, support vector machine based machine learning classifier predicted pathologic myopia an AUROC of 0.828, with 77.5% sensitivity and 88.07% specificity. Axial length and choroidal thickness, the existing quantitative indicator of pathologic myopia only reached an AUROC of 0.758, with 75.0% sensitivity and 76.61% specificity. When all six indices were applied (four TEPS, AxL, and SCT), the discriminative ability of the SVM model was excellent, demonstrating an AUROC of 0.868, with 80.0% sensitivity and 93.58% specificity. Our model provides an accurate modality for identification of patients with pathologic myopia and may help prioritize these patients for further treatment.
眼底照相的定性分析能够直接识别高度近视的病理性改变。但是，它在定义疾病程度或早期病变范围方面存在局限性，可能会受到主观解释的影响。在这项研究中，我们使用黄斑、视盘和眼球最深处（DPE）作为后极部的三个主要标志物（即关键指标）来量化后巩膜的相对断层抬高（TEPS）。使用 860 名近视患者的眼部定量指标，基于支持向量机的机器学习分类器预测病理性近视的 AUC 为 0.828，敏感性为 77.5%，特异性为 88.07%。眼轴长度和脉络膜厚度是病理性近视的现有定量指标，其 AUC 仅为 0.758，敏感性为 75.0%，特异性为 76.61%。当应用所有六个指标（四个 TEPS、AxL 和 SCT）时，SVM 模型的判别能力非常出色，AUC 为 0.868，敏感性为 80.0%，特异性为 93.58%。我们的模型为病理性近视患者的识别提供了一种准确的方法，可能有助于为这些患者的进一步治疗提供优先级。
