{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:55:08Z","timestamp":1781762108564,"version":"3.54.5"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"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. Cell Dev. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>In this study, we developed and validated a deep learning method for the detection and angular position identification of subclinical zonular laxity using anterior segment optical coherence tomography (AS-OCT).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 600 curated AS-OCT images from 536 patients (600 images) undergoing cataract surgery were evenly stratified into subclinical zonular laxity (n = 300 images from 297 patients) and normal control (n = 300 images from 239 patients) groups. Data were partitioned at the patient level to prevent data leakage, with 60% for training, 15% for validation, and 25% for testing. An additional five clinical cases were used for external validation. We implemented MDCL-Net, a novel classification framework integrating mask-aware feature enhancement, dynamic contextual feature aggregation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved an accuracy of 79.72%, an area under the receiver operating characteristic curve (AUC) of 86.41%, and an F1-score of 78.93%. Ablation studies confirmed the contribution of each module, and in clinical validation, model-predicted zonular laxity ranges showed good agreement with intraoperative observations across five representative cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This work presents the first deep learning method capable of both detecting and spatially localizing subclinical zonular abnormalities in AS-OCT images, demonstrating strong clinical applicability and potential as a reliable preoperative screening tool to enhance surgical planning and safety in cataract procedures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcell.2026.1848716","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:30:25Z","timestamp":1781760625000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep learning-based classification method for subclinical zonular laxity in AS-OCT images"],"prefix":"10.3389","volume":"14","author":[{"given":"Jialin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Tianjin University of Technology","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lujie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital","place":["Tianjin, China"]},{"name":"Tianjin Children\u2019s Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuaixin","family":"Lu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JingLi","family":"Liang","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Teng","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Sun","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Wen","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.preteyeres.2018.04.002","article-title":"Anterior segment optical coherence tomography","volume":"66","author":"Ang","year":"2018","journal-title":"Prog. 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