{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:34:11Z","timestamp":1792629251720,"version":"4.5.0"},"reference-count":29,"publisher":"BMJ","issue":"10","license":[{"start":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T00:00:00Z","timestamp":1705622400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["19lk1010024h0003"],"award-info":[{"award-number":["19lk1010024h0003"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Br J Ophthalmol"],"accepted":{"date-parts":[[2023,12,17]]},"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To develop an artificial intelligence (AI) algorithm that diagnoses cataracts\/corneal diseases from multiple conditions using smartphone images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included 6442 images that were captured using a slit-lamp microscope (6106 images) and smartphone (336 images). An AI algorithm was developed based on slit-lamp images to differentiate 36 major diseases (cataracts and corneal diseases) into 9 categories. To validate the AI model, smartphone images were used for the testing dataset. We evaluated AI performance that included sensitivity, specificity and receiver operating characteristic (ROC) curve for the diagnosis and triage of the diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The AI algorithm achieved an area under the ROC curve of 0.998 (95% CI, 0.992 to 0.999) for normal eyes, 0.986 (95% CI, 0.978 to 0.997) for infectious keratitis, 0.960 (95% CI, 0.925 to 0.994) for immunological keratitis, 0.987 (95% CI, 0.978 to 0.996) for cornea scars, 0.997 (95% CI, 0.992 to 1.000) for ocular surface tumours, 0.993 (95% CI, 0.984 to 1.000) for corneal deposits, 1.000 (95% CI, 1.000 to 1.000) for acute angle-closure glaucoma, 0.992 (95% CI, 0.985 to 0.999) for cataracts and 0.993 (95% CI, 0.985 to 1.000) for bullous keratopathy. The triage of referral suggestion using the smartphone images exhibited high performance, in which the sensitivity and specificity were 1.00 (95% CI, 0.478 to 1.00) and 1.00 (95% CI, 0.976 to 1.000) for \u2018urgent\u2019, 0.867 (95% CI, 0.683 to 0.962) and 1.00 (95% CI, 0.971 to 1.000) for \u2018semi-urgent\u2019, 0.853 (95% CI, 0.689 to 0.950) and 0.983 (95% CI, 0.942 to 0.998) for \u2018routine\u2019 and 1.00 (95% CI, 0.958 to 1.00) and 0.896 (95% CI, 0.797 to 0.957) for \u2018observation\u2019, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The AI system achieved promising performance in the diagnosis of cataracts and corneal diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bjo-2023-324488","type":"journal-article","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T13:25:16Z","timestamp":1705670716000},"page":"1406-1413","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":45,"title":["Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases"],"prefix":"10.1136","volume":"108","author":[{"given":"Yuta","family":"Ueno","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7714-422X","authenticated-orcid":false,"given":"Masahiro","family":"Oda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2546-2813","authenticated-orcid":false,"given":"Takefumi","family":"Yamaguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hideki","family":"Fukuoka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryohei","family":"Nejima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshiyuki","family":"Kitaguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7410-3764","authenticated-orcid":false,"given":"Masahiro","family":"Miyake","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masato","family":"Akiyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazunori","family":"Miyata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Kashiwagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6509-9054","authenticated-orcid":false,"given":"Naoyuki","family":"Maeda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0435-3095","authenticated-orcid":false,"given":"Jun","family":"Shimazaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hisashi","family":"Noma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0100-4797","authenticated-orcid":false,"given":"Kensaku","family":"Mori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9071-7018","authenticated-orcid":false,"given":"Tetsuro","family":"Oshika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2024,1,19]]},"reference":[{"key":"2024101503100694000_108.10.1406.1","doi-asserted-by":"crossref","first-page":"e1221","DOI":"10.1016\/S2214-109X(17)30393-5","article-title":"Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis","volume":"5","author":"Flaxman","year":"2017","journal-title":"Lancet Glob Health"},{"key":"2024101503100694000_108.10.1406.2","doi-asserted-by":"publisher","DOI":"10.1136\/bjophthalmol-2011-300539"},{"key":"2024101503100694000_108.10.1406.3","doi-asserted-by":"crossref","first-page":"e130","DOI":"10.1016\/S2214-109X(20)30425-3","article-title":"Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the global burden of disease study","volume":"9","year":"2021","journal-title":"Lancet Glob Health"},{"key":"2024101503100694000_108.10.1406.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2013.05.025"},{"key":"2024101503100694000_108.10.1406.5","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1053\/j.gastro.2020.09.027","article-title":"Artificial intelligence system to determine risk of T1 colorectal cancer metastasis to lymph node","volume":"160","author":"Kudo","year":"2021","journal-title":"Gastroenterology"},{"key":"2024101503100694000_108.10.1406.6","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"De Fauw","year":"2018","journal-title":"Nat Med"},{"key":"2024101503100694000_108.10.1406.7","doi-asserted-by":"crossref","first-page":"3738","DOI":"10.1038\/s41467-021-24116-6","article-title":"Preventing corneal blindness caused by keratitis using artificial intelligence","volume":"12","author":"Li","year":"2021","journal-title":"Nat Commun"},{"key":"2024101503100694000_108.10.1406.8","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.8758","article-title":"Automated explainable multidimensional deep learning platform of retinal images for retinopathy of prematurity screening","volume":"4","author":"Wang","year":"2021","journal-title":"JAMA Netw Open"},{"key":"2024101503100694000_108.10.1406.9","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-02138-w","article-title":"Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images","volume":"11","author":"Koyama","year":"2021","journal-title":"Sci Rep"},{"key":"2024101503100694000_108.10.1406.10","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2022.0269","article-title":"Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy","volume":"5","author":"Gomez Rossi","year":"2022","journal-title":"JAMA Netw Open"},{"key":"2024101503100694000_108.10.1406.11","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-71425-9","article-title":"A deep learning approach in diagnosing fungal keratitis based on corneal photographs","volume":"10","author":"Kuo","year":"2020","journal-title":"Sci Rep"},{"key":"2024101503100694000_108.10.1406.12","doi-asserted-by":"crossref","DOI":"10.1186\/s12938-021-00906-3","article-title":"ACCV: automatic classification algorithm of cataract video based on deep learning","volume":"20","author":"Hu","year":"2021","journal-title":"Biomed Eng Online"},{"key":"2024101503100694000_108.10.1406.13","doi-asserted-by":"publisher","DOI":"10.1136\/bjophthalmol-2021-318866"},{"key":"2024101503100694000_108.10.1406.14","doi-asserted-by":"publisher","DOI":"10.1136\/bjophthalmol-2019-314729"},{"key":"2024101503100694000_108.10.1406.15","doi-asserted-by":"crossref","first-page":"17851","DOI":"10.1038\/s41598-020-75027-3","article-title":"Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs","volume":"10","author":"Gu","year":"2020","journal-title":"Sci Rep"},{"key":"2024101503100694000_108.10.1406.16","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s10384-022-00941-0","article-title":"Japan ocular imaging registry: a national ophthalmology real-world database","volume":"66","author":"Miyake","year":"2022","journal-title":"Jpn J Ophthalmol"},{"key":"2024101503100694000_108.10.1406.17","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"key":"2024101503100694000_108.10.1406.18","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/26.4.404"},{"key":"2024101503100694000_108.10.1406.19","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1097\/APO.0000000000000301","article-title":"Deployment of artificial intelligence in real-world practice: opportunity and challenge","volume":"9","author":"He","year":"2020","journal-title":"Asia Pac J Ophthalmol (Phila)"},{"key":"2024101503100694000_108.10.1406.20","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.ccell.2021.04.002","article-title":"Artificial intelligence for clinical oncology","volume":"39","author":"Kann","year":"2021","journal-title":"Cancer Cell"},{"key":"2024101503100694000_108.10.1406.21","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0307-0"},{"key":"2024101503100694000_108.10.1406.22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ajo.2018.11.024","article-title":"Management of invasive squamous cell carcinomas of the conjunctiva","volume":"200","author":"Santoni","year":"2019","journal-title":"Am J Ophthalmol"},{"key":"2024101503100694000_108.10.1406.23","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.21579"},{"key":"2024101503100694000_108.10.1406.24","unstructured":"Redmon J , Farhadi A . YOLOv3: an incremental improvement. arXiv 2018. doi:10.48550\/arXiv.1804.02767"},{"key":"2024101503100694000_108.10.1406.25","unstructured":"He K , Zhang X , Ren S , et al . YOLOv3: an incremental improvement. IEEE Conference on Computer Vision and Pattern Recognition; 2018"},{"key":"2024101503100694000_108.10.1406.26","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.m127","article-title":"Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies","volume":"368","author":"Freeman","year":"2020","journal-title":"BMJ"},{"key":"2024101503100694000_108.10.1406.27","first-page":"214","article-title":"Corneal blindness: a global perspective","volume":"79","author":"Whitcher","year":"2001","journal-title":"Bull World Health Organ"},{"key":"2024101503100694000_108.10.1406.28","doi-asserted-by":"publisher","DOI":"10.1001\/archopht.1989.01070020221029"},{"key":"2024101503100694000_108.10.1406.29","doi-asserted-by":"crossref","first-page":"3820","DOI":"10.1111\/all.15078","article-title":"Symptom-based stratification for hay fever: a crowdsourced study using the smartphone application AllerSearch","volume":"76","author":"Inomata","year":"2021","journal-title":"Allergy"}],"container-title":["British Journal of Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/bjo-2023-324488","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T06:17:02Z","timestamp":1728973022000},"score":1,"resource":{"primary":{"URL":"https:\/\/bjo.bmj.com\/lookup\/doi\/10.1136\/bjo-2023-324488"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,19]]},"references-count":29,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,9,20]]},"published-print":{"date-parts":[[2024,10]]}},"alternative-id":["10.1136\/bjo-2023-324488"],"URL":"https:\/\/doi.org\/10.1136\/bjo-2023-324488","relation":{},"ISSN":["0007-1161","1468-2079"],"issn-type":[{"value":"0007-1161","type":"print"},{"value":"1468-2079","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,19]]}}}