{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chiang M"],"funding":["NEI NIH HHS","American Glaucoma Society","Research to Prevent Blindness","Fight for Sight","National Institutes of Health","University of Southern California Southern California Clinical and Translational Science Institute"],"pagination":["100-107"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8286291"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["226"],"pubmed_abstract":["<h4>Purpose</h4>To compare the performance of a novel convolutional neural network (CNN) classifier and human graders in detecting angle closure in EyeCam (Clarity Medical Systems, Pleasanton, California, USA) goniophotographs.<h4>Design</h4>Retrospective cross-sectional study.<h4>Methods</h4>Subjects from the Chinese American Eye Study underwent EyeCam goniophotography in 4 angle quadrants. A CNN classifier based on the ResNet-50 architecture was trained to detect angle closure, defined as inability to visualize the pigmented trabecular meshwork, using reference labels by a single experienced glaucoma specialist. The performance of the CNN classifier was assessed using an independent test dataset and reference labels by the single glaucoma specialist or a panel of 3 glaucoma specialists. "],"journal":["American journal of ophthalmology"],"pubmed_title":["Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study."],"pmcid":["PMC8286291"],"funding_grant_id":["K23 EY029763","U10 EY017337","P30 EY029220"],"pubmed_authors":["Gokoffski K","Lin S","Shan M","Song B","Guth D","Chiang M","Wong BJ","Shen A","Dredge J","Xu BY","Pardeshi AA","Randhawa J","Nguyen A","Varma R"],"additional_accession":[]},"is_claimable":false,"name":"Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study.","description":"<h4>Purpose</h4>To compare the performance of a novel convolutional neural network (CNN) classifier and human graders in detecting angle closure in EyeCam (Clarity Medical Systems, Pleasanton, California, USA) goniophotographs.<h4>Design</h4>Retrospective cross-sectional study.<h4>Methods</h4>Subjects from the Chinese American Eye Study underwent EyeCam goniophotography in 4 angle quadrants. A CNN classifier based on the ResNet-50 architecture was trained to detect angle closure, defined as inability to visualize the pigmented trabecular meshwork, using reference labels by a single experienced glaucoma specialist. The performance of the CNN classifier was assessed using an independent test dataset and reference labels by the single glaucoma specialist or a panel of 3 glaucoma specialists. ","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Jun","modification":"2025-04-22T21:51:54.415Z","creation":"2025-04-06T03:53:46.68Z"},"accession":"S-EPMC8286291","cross_references":{"pubmed":["33577791"],"doi":["10.1016/j.ajo.2021.02.004"]}}