Automated detection of hyperreflective foci in the outer nuclear layer of the retina.

Autor: Schmidt MF; Department of Neurology, Clinic of Optic Neuritis, The Danish Multiple Sclerosis Center (DMSC), Rigshospitalet, Glostrup, Denmark., Christensen JL; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark., Dahl VA; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark., Toosy A; NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square UCL Institute of Neurology, University College London, London, UK., Petzold A; Moorfields Eye Hospital NHS Foundation Trust, London, UK.; Neuro-ophthalmology Expertise Centre, University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.; UCL Institute of Neurology, London, UK., Hanson JVM; Department of Ophthalmology, University Hospital Zurich and University of Zurich, Zurich, Switzerland., Schippling S; Multimodal Imaging in Neuroimmunological Diseases (MINDS), University Hospital Zurich and University of Zurich, Zurich, Switzerland., Frederiksen JL; Department of Neurology, Clinic of Optic Neuritis, The Danish Multiple Sclerosis Center (DMSC), Rigshospitalet and University of Copenhagen, Glostrup, Denmark., Larsen M; Department of Ophthalmology, Rigshospitalet and University of Copenhagen, Glostrup, Denmark.
Jazyk: angličtina
Zdroj: Acta ophthalmologica [Acta Ophthalmol] 2023 Mar; Vol. 101 (2), pp. 200-206. Date of Electronic Publication: 2022 Sep 08.
DOI: 10.1111/aos.15237
Abstrakt: Purpose: 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.
Methods: This longitudinal exploratory study investigated 14 eyes of seven patients including six patients with optic neuropathy and one with mild non-proliferative diabetic retinopathy. In total, 2596 OCT B-scan 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.
Results: 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).
Conclusion: 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.
(© 2022 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation.)
Databáze: MEDLINE
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