You are here

  • Home
  • Archive
  • Volume 108, Issue 9
  • Automated identification of fleck lesions in Stargardt disease using deep learning enhances lesion detection sensitivity and enables morphometric analysis of flecks

Article Text

Article menu
Download PDFPDF
Retina
Automated identification of fleck lesions in Stargardt disease using deep learning enhances lesion detection sensitivity and enables morphometric analysis of flecks
  1. Jasdeep Sabharwal1,
  2. Tin Yan Alvin Liu1,
  3. Bani Antonio-Aguirre2,
  4. Mya Abousy2,3,
  5. Tapan Patel1,
  6. Cindy X Cai1,
  7. Craig K Jones4,
  8. Mandeep S Singh1,2,3
  1. 1 Johns Hopkins Wilmer Eye Institute, Baltimore, Maryland, USA
  2. 2 Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  3. 3 Department of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA
  4. 4 The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland, USA
  1. Correspondence to Mandeep S Singh; singhcorrespauth{at}gmail.com

Abstract

Purpose To classify fleck lesions and assess artificial intelligence (AI) in identifying flecks in Stargardt disease (STGD).

Methods A 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.

Results Of 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).

Conclusions AI-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.

  • Telemedicine
  • Imaging
  • Clinical Trial
  • Genetics
  • Vision

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

https://doi.org/10.1136/bjo-2023-323592

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplementary information.

    View Full Text

    Footnotes

    • JS and TYAL are joint first authors.

    • Contributors JS, TYAL and MSS were involved in planning the work. JS, TYL, BAA, MA, CC, TP, CKJ and MSS contributed to conducting and reporting this work. MSS is guarantor.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.