Automated identification of fleck lesions in Stargardt disease using deep learning enhances lesion detection sensitivity and enables morphometric analysis of flecks

• Published in: British journal of ophthalmology Vol. 108; no. 9; pp. 1226 - 1233
• Main Authors: Sabharwal, Jasdeep, Liu, Tin Yan Alvin, Antonio-Aguirre, Bani, Abousy, Mya, Patel, Tapan, Cai, Cindy X, Jones, Craig K, Singh, Mandeep S
• Format: Journal Article
• Language: English
• Published: BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.09.2024; BMJ Publishing Group LTD
• Subjects: Adult; Artificial intelligence; Atrophy; Automation; Biomarkers; Clinical Trial; Clinical trials; Deep Learning; Disease; Female; Fluorescein Angiography - methods; Fundus Oculi; Genetics; Humans; Imaging; Male; Middle Aged; Neural networks; Retina; Retrospective Studies; Stargardt Disease; Telemedicine; Tomography, Optical Coherence - methods; Vision; Visual acuity; Genetics; Clinical Trial; Telemedicine; Imaging; Vision
• ISSN: 0007-1161; 1468-2079; 1468-2079
• DOI: 10.1136/bjo-2023-323592

PurposeTo classify fleck lesions and assess artificial intelligence (AI) in identifying flecks in Stargardt disease (STGD).MethodsA retrospective study of 170 eyes from 85 consecutive patients with confirmed STGD. Fundus autofluorescence images were extracted, and flecks were manually outlined. A deep learning model was trained, and a hold-out testing subset was used to compare with manually identified flecks and for graders to assess. Flecks were clustered using K-means clustering.ResultsOf the 85 subjects, 45 were female, and the median age was 37 years (IQR 25–59). A subset of subjects (n=41) had clearly identifiable fleck lesions, and an AI was successfully trained to identify these lesions (average Dice score of 0.53, n=18). The AI segmentation had smaller (0.018 compared with 0.034 mm2, p<0.001) but more numerous flecks (75.5 per retina compared with 40.0, p<0.001), but the total size of flecks was not different. The AI model had higher sensitivity to detect flecks but resulted in more false positives. There were two clusters of flecks based on morphology: broadly, one cluster of small round flecks and another of large amorphous flecks. The per cent frequency of small round flecks negatively correlated with subject age (r=−0.31, p<0.005).ConclusionsAI-based detection of flecks shows greater sensitivity than human graders but with a higher false-positive rate. With further optimisation to address current shortcomings, this approach could be used to prescreen subjects for clinical research. The feasibility and utility of quantifying fleck morphology in conjunction with AI-based segmentation as a biomarker of progression require further study.