Deep Learning-Based Detection of Reticular Pseudodrusen in Age-Related Macular Degeneration on Optical Coherence Tomography
Year: 2024
Type: preprint
Abstract: ABSTRACT Reticular pseudodrusen (RPD) signify a critical phenotype driving vision loss in age-related macular degeneration (AMD). Their detection is paramount in the clinical management of those with AMD, yet they remain challenging to reliably identify. We thus developed a deep learning (DL) model to segment RPD from 9,800 optical coherence tomography B-scans, and this model produced RPD segmentations that had higher agreement with four retinal specialists (Dice similarity coefficient [DSC]=0·76 [95% confidence interval [CI] 0·71–0·81]) than the agreement amongst the specialists (DSC=0·68, 95% CI=0·63–0·73; p <0·001). In five external test datasets consisting of 1,017 eyes from 812 individuals, the DL model detected RPD with a similar level of performance as two retinal specialists (area-under-the-curve of 0·94 [95% CI=0·92–0·97], 0·95 [95% CI=0·92–0·97] and 0·96 [95% CI=0·94–0·98] respectively; p ≥0·32). This DL model enables the automatic detection and quantification of RPD with expert-level performance, which we have made publicly available.
Source: medRxiv
Authors: Himeesh Kumar, Yelena Bagdasarova, Scott Song, Doron G. Hickey, Amy C. Cohn, Mali Okada, Robert P. Finger, Jan H. Terheyden, Ruth E. Hogg, Pierre-Henry Gabrielle, Louis Arnould, Maxime Jannaud, Xavier Hadoux, Peter van Wijngaarden, Carla J. Abbott, Lauren A.B. Hodgson, Roy Schwartz, Adnan Tufail, Emily Y. Chew, Cecilia S. Lee, Erica L. Fletcher, Melanie Bahlo, Brendan R.E. Ansell, Alice Pébay, Robyn H. Guymer, Aaron Y. Lee, Zhichao Wu
Institutions: The University of Melbourne, The Royal Victorian Eye & Ear Hospital, Centre for Eye Research Australia, University of Washington, Heidelberg University, University Hospital Bonn, University Medical Centre Mannheim, Medizinische Fakultät Mannheim, Queen's University Belfast, CHU Dijon Bourgogne, Université de Bourgogne, Moorfields Eye Hospital NHS Foundation Trust, University College London, National Institutes of Health, National Eye Institute, Walter and Eliza Hall Institute of Medical Research, The Royal Melbourne Hospital
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4402471946