{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T20:54:38Z","timestamp":1793393678322,"version":"4.5.0"},"reference-count":55,"publisher":"American Medical Association (AMA)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n IMPORTANCE<\/jats:title>\n Identifying pediatric eye diseases at an early stage is a worldwide issue. Traditional screening procedures depend on hospitals and ophthalmologists, which are expensive and time-consuming. Using artificial intelligence (AI) to assess children\u2019s eye conditions from mobile photographs could facilitate convenient and early identification of eye disorders in a home setting.<\/jats:p>\n <\/jats:sec>\n \n OBJECTIVE<\/jats:title>\n To develop an AI model to identify myopia, strabismus, and ptosis using mobile photographs.<\/jats:p>\n <\/jats:sec>\n \n DESIGN, SETTING, AND PARTICIPANTS<\/jats:title>\n This cross-sectional study was conducted at the Department of Ophthalmology of Shanghai Ninth People\u2019s Hospital from October 1, 2022, to September 30, 2023, and included children who were diagnosed with myopia, strabismus, or ptosis.<\/jats:p>\n <\/jats:sec>\n \n MAIN OUTCOMES AND MEASURES<\/jats:title>\n A deep learning\u2013based model was developed to identify myopia, strabismus, and ptosis. The performance of the model was assessed using sensitivity, specificity, accuracy, the area under the curve (AUC), positive predictive values (PPV), negative predictive values (NPV), positive likelihood ratios (P-LR), negative likelihood ratios (N-LR), and the F1-score. GradCAM++ was utilized to visually and analytically assess the impact of each region on the model. A sex subgroup analysis and an age subgroup analysis were performed to validate the model\u2019s generalizability.<\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n A total of 1419 images obtained from 476 patients (225 female [47.27%]; 299 [62.82%] aged between 6 and 12 years) were used to build the model. Among them, 946 monocular images were used to identify myopia and ptosis, and 473 binocular images were used to identify strabismus. The model demonstrated good sensitivity in detecting myopia (0.84 [95% CI, 0.82-0.87]), strabismus (0.73 [95% CI, 0.70-0.77]), and ptosis (0.85 [95% CI, 0.82-0.87]). The model showed comparable performance in identifying eye disorders in both female and male children during sex subgroup analysis. There were differences in identifying eye disorders among different age subgroups.<\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS AND RELEVANCE<\/jats:title>\n In this cross-sectional study, the AI model demonstrated strong performance in accurately identifying myopia, strabismus, and ptosis using only smartphone images. These results suggest that such a model could facilitate the early detection of pediatric eye diseases in a convenient manner at home.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2024.25124","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:26:32Z","timestamp":1722943592000},"page":"e2425124","source":"Crossref","is-referenced-by-count":52,"title":["Artificial Intelligence for Early Detection of Pediatric Eye Diseases Using Mobile Photos"],"prefix":"10.1001","volume":"7","author":[{"given":"Qin","family":"Shu","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Shanghai Ninth People\u2019s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China"},{"name":"Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiali","family":"Pang","sequence":"additional","affiliation":[{"name":"Shanghai Artificial 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