Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods

作者：Yuchen Du, Qiuying Chen, Ying Fan, Jianfeng Zhu, Jiangnan He, Haidong Zou, Dazhen Sun, Bowen Xin, Dagan Feng, Michael Fulham, Xiuying Wang, Lisheng Wang, Xun Xu · 发表于：Journal of Translational Medicine · 年份：2021 · DOI：10.1186/s12967-021-02818-1 · 被引用次数：24 · 研究领域：Ophthalmology and Visual Impairment Studies、Retinal Diseases and Treatments、Retinal Imaging and Analysis

BACKGROUND: Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage. METHODS: A total of 457 eyes (313 patients) were enrolled and were divided into severe MM group and without severe MM group. Radiomics analysis was applied to depict features significantly correlated with severe MM from optic disc region. Receiver Operating Characteristic were used to evaluate these features' performance of classifying severe MM. RESULTS: Eight new MM-related image features were discovered from the optic disc region, which described the shapes, textural patterns and intensity distributions of optic disc region. Compared with clinically reported MM-related features, these newly discovered features exhibited better abilities on severe MM classification. And the mean values of most features were markedly changed between patients with peripapillary diffuse chorioretinal atrophy (PDCA) and macular diffuse chorioretinal atrophy (MDCA). CONCLUSIONS: Machine learning an...