{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T18:20:13Z","timestamp":1785867613205,"version":"3.56.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Modalities for myopia control, such as orthokeratology, repeated low-intensity red light (RLRL) treatment, and low-concentration atropine, have become popular topics. However, the effects of these three modalities on ocular surface health remain unclear. The tear meniscus height (TMH), a crucial criterion for evaluating ocular surface health and diagnosing dry eye, is conventionally measured via manual demarcation of ocular surface images, which is inefficient and involves subjective judgment. Therefore, this study sought to establish a deep learning model for automatic TMH measurement on ocular surface images to improve the efficiency and accuracy of the initial screening of dry eye associated with myopia control modalities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To establish a model, 1,200 ocular surface images captured with an OCULUS Keratograph 5M were collected. The tear meniscus area on the image was initially marked by one experienced ophthalmologist and verified by the other. The whole image dataset was divided into a training set (70%), a validation set (20%), a test set (10%), and an external validation set (100 ocular surface images) for model construction. The deep learning model was applied to ocular surface imaging data from previous clinical trials using orthokeratology, RLRL therapy, and 0.01% atropine for myopia control. TMHs at follow-ups were automatically measured by the deep learning model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Two hundred training iterations were performed to establish the model. At the 124th iteration, the IoU of the validation set peaked at 0.913, and the parameters of the model were saved for the testing process. The model IoU was 0.928 during testing. The AUC of the ROC curve was 0.935, and the R2 of the linear regression analysis was 0.92. The good performance and comprehensive validation of the model warrants its application to automatic TMH measurement in clinical trials of myopia control. There were no significant changes in the TMH during the follow-up period after treatment with orthokeratology, RLRL, or 0.01% atropine.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>A deep learning model was established for automatic measurement of the TMH on Keratograph 5M-captured ocular surface images. This model demonstrated high accuracy, great consistency with manual measurements, and applicability to the initial screening of dry eye associated with myopia control modalities.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fbioe.2025.1554432","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T05:13:21Z","timestamp":1744348401000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A deep learning-assisted automatic measurement of tear meniscus height on ocular surface images and its application in myopia control"],"prefix":"10.3389","volume":"13","author":[{"given":"Weixiao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Rong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiwen","family":"Hei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guihua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meinan","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bei","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruihua","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.2147\/opth.s218313","article-title":"Keratograph 5M as A useful and objective tool for evaluating the ocular surface in limbal stem cell deficiency","volume":"13","author":"Alfaro-Ju\u00e1rez","year":"2019","journal-title":"Clin. 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