{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:01:22Z","timestamp":1785416482520,"version":"3.56.0"},"reference-count":29,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acta Ophthalmologica"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Hyperreflective foci are poorly understood transient elements seen on optical coherence tomography (OCT) of the retina in both healthy and diseased eyes. Systematic studies may benefit from the development of automated tools that can map and track such foci. The outer nuclear layer (ONL) of the retina is an attractive layer in which to study hyperreflective foci as it has no fixed hyperreflective elements in healthy eyes. In this study, we intended to evaluate whether automated image analysis can identify, quantify and visualize hyperreflective foci in the ONL of the retina.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This longitudinal exploratory study investigated 14 eyes of seven patients including six patients with optic neuropathy and one with mild non\u2010proliferative diabetic retinopathy. In total, 2596 OCT B\u2010scan were obtained. An image analysis blob detector algorithm was used to detect candidate foci, and a convolutional neural network (CNN) trained on a manually labelled subset of data was then used to select those candidate foci in the ONL that fitted the characteristics of the reference foci best.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the manually labelled data set, the blob detector found 2548 candidate foci, correctly detecting 350 (89%) out of 391 manually labelled reference foci. The accuracy of CNN classifier was assessed by manually splitting the 2548 candidate foci into a training and validation set. On the validation set, the classifier obtained an accuracy of 96.3%, a sensitivity of 88.4% and a specificity of 97.5% (AUC 0.989).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study demonstrated that automated image analysis and machine learning methods can be used to successfully identify, quantify and visualize hyperreflective foci in the ONL of the retina on OCT scans.<\/jats:p><\/jats:sec>","DOI":"10.1111\/aos.15237","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T13:00:43Z","timestamp":1662642043000},"page":"200-206","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Automated detection of hyperreflective foci in the outer nuclear layer of the retina"],"prefix":"10.1111","volume":"101","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7086-4638","authenticated-orcid":false,"given":"Mathias Falck","family":"Schmidt","sequence":"first","affiliation":[{"name":"Department of Neurology, Clinic of Optic Neuritis The Danish Multiple Sclerosis Center (DMSC), Rigshospitalet  Glostrup Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob L\u00f8nborg","family":"Christensen","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Computer Science Technical University of Denmark  Lyngby Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vedrana Andersen","family":"Dahl","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Computer Science Technical University of Denmark  Lyngby Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Toosy","sequence":"additional","affiliation":[{"name":"NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation Queen Square UCL Institute of Neurology, University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel","family":"Petzold","sequence":"additional","affiliation":[{"name":"Moorfields Eye Hospital NHS Foundation Trust  London UK"},{"name":"Neuro\u2010ophthalmology Expertise Centre University Medical Centre, University of Amsterdam  Amsterdam The Netherlands"},{"name":"UCL Institute of Neurology  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James V. 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