{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T19:27:20Z","timestamp":1791228440103,"version":"4.1.0"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Agency for Medical Research and Development"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Primary angle closure glaucoma (PACG) is a major cause of visual impairment, particularly in Asia. Although effective screening tools are necessary, the current gold standard is complex and time-consuming, requiring extensive expertise. Artificial intelligence has introduced new opportunities for innovation in ophthalmic imaging. Anterior chamber depth (ACD) is a key risk factor for angle closure and has been suggested as a quick screening parameter for PACG. This study aims to develop an AI algorithm to quantitatively predict ACD from anterior segment photographs captured using a portable smartphone slit-lamp microscope. We retrospectively collected 204,639 frames from 1586 eyes, with ACD values obtained by anterior-segment OCT. We developed two models, (Model 1) diagnosable frame extraction and (Model 2) ACD estimation, using SWSL ResNet as the machine learning model. Model 1 achieved an accuracy of 0.994. Model 2 achieved an MAE of 0.093 \u00b1 0.082 mm, an MSE of 0.123 \u00b1 0.170 mm, and a correlation of R = 0.953. Furthermore, our model\u2019s estimation of the risk for angle closure showed a sensitivity of 0.943, specificity of 0.902, and an area under the curve (AUC) of 0.923 (95%CI: 0.878\u20130.968). We successfully developed a high-performance ACD estimation model, laying the groundwork for predicting other quantitative measurements relevant to PACG screening.<\/jats:p>","DOI":"10.3390\/bioengineering11101005","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T06:30:03Z","timestamp":1728455403000},"page":"1005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["The Use of Artificial Intelligence for Estimating Anterior Chamber Depth from Slit-Lamp Images Developed Using Anterior-Segment Optical Coherence Tomography"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-8578","authenticated-orcid":false,"given":"Eisuke","family":"Shimizu","sequence":"first","affiliation":[{"name":"OUI Inc., Tokyo 107-0062, Japan"},{"name":"Yokohama Keiai Eye Clinic, Kanagawa 240-0065, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenta","family":"Tanaka","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 107-0062, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2832-4934","authenticated-orcid":false,"given":"Hiroki","family":"Nishimura","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 107-0062, Japan"},{"name":"Yokohama Keiai Eye Clinic, Kanagawa 240-0065, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naomichi","family":"Agata","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 107-0062, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Makoto","family":"Tanji","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 107-0062, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shintato","family":"Nakayama","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 107-0062, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohan Jeetendra","family":"Khemlani","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 107-0062, Japan"},{"name":"Yokohama Keiai Eye Clinic, Kanagawa 240-0065, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryota","family":"Yokoiwa","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 107-0062, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinri","family":"Sato","sequence":"additional","affiliation":[{"name":"Yokohama Keiai Eye Clinic, Kanagawa 240-0065, Japan"},{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daisuke","family":"Shiba","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasunori","family":"Sato","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Keio University School of Medicine, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1016\/j.ophtha.2021.04.009","article-title":"The Global Extent of Undetected Glaucoma in Adults: A Systematic Review and Meta-analysis","volume":"128","author":"Soh","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","article-title":"Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis","volume":"121","author":"Tham","year":"2014","journal-title":"Ophthalmology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"xvi","DOI":"10.1016\/j.ajo.2018.01.006","article-title":"Anterior Segment Imaging for Angle Closure","volume":"188","author":"Chansangpetch","year":"2018","journal-title":"Am. 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