{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:53:22Z","timestamp":1778802802849,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Agency for Medical Research and Development (AMED)","award":["22hk0102084h0001"],"award-info":[{"award-number":["22hk0102084h0001"]}]},{"name":"Japan Agency for Medical Research and Development (AMED)","award":["23hk0102084h0002"],"award-info":[{"award-number":["23hk0102084h0002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targets corneal opacity, which is a global cause of blindness. This study has two purposes. The first is to detect corneal opacity from videos in which the anterior segment of the eye is captured. The other is to develop an AI pipeline to detect corneal opacities. First, we extracted image frames from videos and processed them using a convolutional neural network (CNN) model. Second, we manually annotated the images to extract only the corneal margins, adjusted the contrast with CLAHE, and processed them using the CNN model. Finally, we performed semantic segmentation of the cornea using annotated data. The results showed an accuracy of 0.8 for image frames and 0.96 for corneal margins. Dice and IoU achieved a score of 0.94 for semantic segmentation of the corneal margins. Although corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. The incorporation of manual annotation into the AI pipeline, through semantic segmentation, facilitated high accuracy in detecting corneal opacity.<\/jats:p>","DOI":"10.3390\/bioengineering11030273","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:14:40Z","timestamp":1710245680000},"page":"273","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Development of the AI Pipeline for Corneal Opacity Detection"],"prefix":"10.3390","volume":"11","author":[{"given":"Kenji","family":"Yoshitsugu","sequence":"first","affiliation":[{"name":"Graduate School of Information Science, University of Hyogo, Kobe Information Science Campus, Kobe 6500047, Japan"},{"name":"OUI Inc., Tokyo 1070062, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-8578","authenticated-orcid":false,"given":"Eisuke","family":"Shimizu","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 1070062, Japan"},{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2832-4934","authenticated-orcid":false,"given":"Hiroki","family":"Nishimura","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 1070062, Japan"},{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan"},{"name":"Yokohama Keiai Eye Clinic, Kanagawa 2400065, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohan","family":"Khemlani","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 1070062, Japan"},{"name":"Yokohama Keiai Eye Clinic, Kanagawa 2400065, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shintaro","family":"Nakayama","sequence":"additional","affiliation":[{"name":"OUI Inc., Tokyo 1070062, Japan"},{"name":"Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tadamasa","family":"Takemura","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science, University of Hyogo, Kobe Information Science Campus, Kobe 6500047, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1136\/bjophthalmol-2011-301378","article-title":"The number of ophthalmologists in practice and training worldwide: A growing gap despite more than 200,000 practitioners","volume":"96","author":"Resnikoff","year":"2012","journal-title":"Br. 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