{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T11:18:59Z","timestamp":1769167139124,"version":"3.49.0"},"reference-count":37,"publisher":"BMJ","issue":"1","funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["T32 GM 65841"],"award-info":[{"award-number":["T32 GM 65841"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Mayo Clinic Foundation","award":["No specific award number."],"award-info":[{"award-number":["No specific award number."]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Br J Ophthalmol"],"accepted":{"date-parts":[[2022,11,11]]},"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Despite, the potential clinical utility of 60\u20134 visual fields, they are not frequently used in clinical practice partly, due to the purported impact of facial contour on field defects. The purpose of this study was to design and test an artificial intelligence-driven platform to predict facial structure-dependent visual field defects on 60\u20134 visual field tests.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Subjects with no ocular pathology were included. Participants were subject to optical coherence tomography, 60\u20134 Swedish interactive thresholding algorithm visual field tests and photography. The predicted visual field was compared with observed 60\u20134 visual field results in subjects. Average and point-specific sensitivity, specificity, precision, negative predictive value, accuracy, and F1-scores were primary outcome measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>30 healthy were enrolled. Three-dimensional facial reconstruction using a convolution neural network (CNN) was able to predict facial contour-dependent 60\u20134 visual field defects in 30 subjects without ocular pathology. Overall model accuracy was 97%\u00b13% and 96%\u00b13% and the F1-score, dependent on precision and sensitivity, was 58%\u00b119% and 55%\u00b115% for the right eye and left eye, respectively. Spatial-dependent model performance was observed with increased sensitivity and precision within the far inferior nasal field reflected by an average F1-score of 76%\u00b120% and 70%\u00b129% for the right eye and left eye, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This pilot study reports the development of a CNN-enhanced platform capable of predicting 60\u20134 visual field defects in healthy controls based on facial contour. Further study with this platform may enhance understanding of the influence of facial contour on 60\u20134 visual field testing.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bjo-2022-321651","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T14:57:42Z","timestamp":1669388262000},"page":"112-116","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting 60\u20134 visual field tests using 3D facial reconstruction"],"prefix":"10.1136","volume":"108","author":[{"given":"Sepideh","family":"Jamali Dogahe","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Armin","family":"Garmany","sequence":"additional","affiliation":[{"name":"Graduate School of Biomedical Sciences, Alix School of Medicine, Medical Scientist Training Program, Mayo Clinic, Rochester, Minnesota, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyedmostafa","family":"Sadegh Mousavi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3735-4297","authenticated-orcid":false,"given":"Cheryl L","family":"Khanna","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"239","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"2026011304301010000_108.1.112.1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/s41536-021-00169-5","article-title":"Longevity leap: mind the healthspan gap","volume":"6","author":"Garmany","year":"2021","journal-title":"NPJ Regen Med"},{"key":"2026011304301010000_108.1.112.2","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","year":"2021","journal-title":"Lancet Glob Health"},{"key":"2026011304301010000_108.1.112.3","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1001\/jamaophthalmol.2016.1284","article-title":"Visual impairment and blindness in adults in the United States: demographic and geographic variations from 2015 to 2050","volume":"134","author":"Varma","year":"2016","journal-title":"JAMA Ophthalmol"},{"key":"2026011304301010000_108.1.112.4","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.17-23713"},{"key":"2026011304301010000_108.1.112.5","doi-asserted-by":"crossref","unstructured":"Crabb DP , Russell RA , Malik R , et al . 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