https://scholars.lib.ntu.edu.tw/handle/123456789/607098
標題: | Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning | 作者: | Chueh K.-M YI-TING HSIEH HOMER H. CHEN Ma I.-H SHENG-LUNG HUANG |
關鍵字: | age;artificial intelligence;deep learning;fovea;macula;optical coherence tomography;sex | 公開日期: | 2022 | 卷: | 235 | 起(迄)頁: | 221-228 | 來源出版物: | American Journal of Ophthalmology | 摘要: | Purpose: To develop deep learning models for identification of sex and age from macular optical coherence tomography (OCT) and to analyze the features for differentiation of sex and age. Design: Algorithm development using database of macular OCT. Methods: We reviewed 6147 sets of macular OCT images from the healthy eyes of 3134 individuals from a single eye center in Taiwan. Deep learning–based algorithms were used to develop models for the identification of sex and age, and 10-fold cross-validation was applied. Gradient-weighted class activation mapping was used for feature analysis. Results: The accuracy for sex prediction using deep learning from macular OCT was 85.6% ± 2.1% compared with accuracy of 61.9% using macular thickness and 61.4% ± 4.0% using deep learning from infrared fundus photography (P < .001 for both). The mean absolute error for age prediction using deep learning from macular OCT was 5.78 ± 0.29 years. A thorough analysis of the prediction accuracy and the gradient-weighted class activation mapping showed that the cross-sectional foveal contour lead to a better sex distinction than macular thickness or fundus photography, and the age-related characteristics of macula were on the whole layers of retina rather than the choroid. Conclusions: Sex and age could be identified from macular OCT using deep learning with good accuracy. The main sexual difference of macula lies in the foveal contour, and the whole layers of retina differ with aging. These novel findings provide useful information for further investigation in the pathogenesis of sex- and age-related macular structural diseases. ? 2021 Elsevier Inc. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121113544&doi=10.1016%2fj.ajo.2021.09.015&partnerID=40&md5=872ca2691301355242cc7467d866d1da https://scholars.lib.ntu.edu.tw/handle/123456789/607098 |
ISSN: | 00029394 | DOI: | 10.1016/j.ajo.2021.09.015 | SDG/關鍵字: | child; cross-sectional study; eye fundus; human; optical coherence tomography; pathology; preschool child; procedures; retina macula lutea; Child; Child, Preschool; Cross-Sectional Studies; Deep Learning; Fundus Oculi; Humans; Macula Lutea; Tomography, Optical Coherence |
顯示於: | 電機工程學系 |
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