{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:46:42Z","timestamp":1760060802685,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["JP23K09064"],"award-info":[{"award-number":["JP23K09064"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Retinal breaks are critical lesions that can cause retinal detachment and vision loss if not detected and treated early. Automated, accurate delineation of retinal breaks in ultra-widefield fundus (UWF) images remains challenging. In this study, we developed and validated a deep learning segmentation model based on the PraNet architecture to localize retinal breaks in break-positive cases. We trained and evaluated the model using a dataset comprising 34,867 UWF images of 8083 cases. Performance was assessed using image-level segmentation metrics, including accuracy, precision, recall, Intersection over Union (IoU), dice score, and centroid distance score. The model achieved an accuracy of 0.996, precision of 0.635, recall of 0.756, IoU of 0.539, dice score of 0.652, and centroid distance score of 0.081. To our knowledge, this is the first study to present pixel-level segmentation of retinal breaks in UWF images using deep learning. The proposed PraNet-based model showed high accuracy and robust segmentation performance, highlighting its potential for clinical application.<\/jats:p>","DOI":"10.3390\/s25185862","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T12:33:32Z","timestamp":1758285212000},"page":"5862","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pixel-Level Segmentation of Retinal Breaks in Ultra-Widefield Fundus Images with a PraNet-Based Machine Learning Model"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5774-0016","authenticated-orcid":false,"given":"Takuya","family":"Takayama","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Jichi Medical University, Shimotsuke, Tochigi 329-0498, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsubasa","family":"Uto","sequence":"additional","affiliation":[{"name":"DeepEyeVision Inc., Shimotsuke, Tochigi 329-0498, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taiki","family":"Tsuge","sequence":"additional","affiliation":[{"name":"DeepEyeVision Inc., Shimotsuke, Tochigi 329-0498, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusuke","family":"Kondo","sequence":"additional","affiliation":[{"name":"DeepEyeVision Inc., Shimotsuke, Tochigi 329-0498, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hironobu","family":"Tampo","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Jichi Medical University, Shimotsuke, Tochigi 329-0498, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mayumi","family":"Chiba","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Jichi Medical University, Shimotsuke, Tochigi 329-0498, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1132-6148","authenticated-orcid":false,"given":"Toshikatsu","family":"Kaburaki","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Jichi Medical University, Shimotsuke, Tochigi 329-0498, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasuo","family":"Yanagi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Yokohama City University, Kanagawa 232-0023, Japan"},{"name":"Retina Research Group, Singapore Eye Research Institute, Singapore 168751, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5331-4730","authenticated-orcid":false,"given":"Hidenori","family":"Takahashi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Jichi Medical University, Shimotsuke, Tochigi 329-0498, Japan"},{"name":"Center for Cyber Medicine Research, Tsukuba University, Ibaraki 305-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e144","DOI":"10.1016\/S2214-109X(20)30489-7","article-title":"Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: The Right to Sight: An analysis for the Global Burden of Disease Study","volume":"9","author":"Steinmetz","year":"2021","journal-title":"Lancet Glob. 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