{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T11:26:10Z","timestamp":1769167570701,"version":"3.49.0"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000},"content-version":"vor","delay-in-days":37,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Stat"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>\n                    Glaucoma is a major cause of blindness and vision impairment worldwide, and visual field (VF) tests are essential for monitoring the conversion of glaucoma. While previous studies have primarily focused on using VF data at a single time point for glaucoma prediction, there has been limited exploration of longitudinal trajectories. Additionally, many deep learning techniques treat the time\u2010to\u2010glaucoma prediction as a binary classification problem (glaucoma Yes\/No), resulting in the misclassification of some censored subjects into the nonglaucoma category and decreased power. To tackle these challenges, we propose and implement several deep\u2010learning approaches that naturally incorporate temporal and spatial information from longitudinal VF data to predict time\u2010to\u2010glaucoma. When evaluated on the Ocular Hypertension Treatment Study (OHTS) dataset, our proposed convolutional neural network (CNN)\u2010long short\u2010term memory (LSTM) emerged as the top\u2010performing model among all those examined. The implementation code can be found online (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/rivenzhou\/VF_prediction\">https:\/\/github.com\/rivenzhou\/VF_prediction<\/jats:ext-link>\n                    ).\n                  <\/jats:p>","DOI":"10.1002\/sta4.649","type":"journal-article","created":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:43:57Z","timestamp":1707353037000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning models to predict primary open\u2010angle glaucoma"],"prefix":"10.1002","volume":"13","author":[{"given":"Ruiwen","family":"Zhou","sequence":"first","affiliation":[{"name":"Division of Biostatistics Washington University in St. Louis, School of Medicine  St. Louis Missouri USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Philip 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