Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach
Our study demonstrates significant inter-rater reliability in labeling the quality and retinal pathologies on OCT scans. While some features show stronger agreement than others, these standardized labels can be utilized to create automated machine learning tools for diagnosing retinal diseases and capturing valuable pathological features in each scan. This standardization will aid in the consistency of medical diagnoses and enhance the accessibility of OCT diagnostic tools.
Authors
- Kiran Kumar Vupparaboina (ORCID: https://orcid.org/0000-0003-0024-8404)
- Katherine Du (ORCID: https://orcid.org/0000-0002-1715-5426)
- Adarsh Gadari (ORCID: https://orcid.org/0009-0008-8238-1957)
- Mohammed Nasar Ibrahim (ORCID: https://orcid.org/0000-0002-6560-3481)
- José‐Alain Sahel (ORCID: https://orcid.org/0000-0002-4831-1153)
- Stavan Shah (ORCID: https://orcid.org/0000-0003-1784-8713)
- Jay Kumar Chhablani (ORCID: https://orcid.org/0000-0003-1772-3558)
- Sandeep Chandra Bollepalli (ORCID: https://orcid.org/0000-0002-9971-9335)
- Shan Sutharahan
Institutions
- University of North Carolina at Greensboro (US)
- University of Pittsburgh Medical Center (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1371/journal.pone.0314707
- Citations
- 2
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- Eye and Ear Foundation of Pittsburgh
- Research to Prevent Blindness
- National Institutes of Health