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On Clinical Agreement on the Visibility and Extent of Anatomical Layers in Digital Gonio Photographs

2021; Association for Research in Vision and Ophthalmology; Volume: 10; Issue: 11 Linguagem: Inglês

10.1167/tvst.10.11.1

2164-2591

Andrea Peroni, Anna Paviotti, Mauro Campigotto, Luís Abegão Pinto, Carlo Alberto Cutolo, Yue Shi, Caroline Cobb, Jacintha Gong, Sirjhun Patel, Stewart Gillan, Andrew J. Tatham, Emanuele Trucco,

Corneal surgery and disorders

Purpose: To quantitatively evaluate the inter-annotator variability of clinicians tracing the contours of anatomical layers of the iridocorneal angle on digital gonio photographs, thus providing a baseline for the validation of automated analysis algorithms. Methods: Using a software annotation tool on a common set of 20 images, five experienced ophthalmologists highlighted the contours of five anatomical layers of interest: iris root (IR), ciliary body band (CBB), scleral spur (SS), trabecular meshwork (TM), and cornea (C). Inter-annotator variability was assessed by (1) comparing the number of times ophthalmologists delineated each layer in the dataset; (2) quantifying how the consensus area for each layer (i.e., the intersection area of observers' delineations) varied with the consensus threshold; and (3) calculating agreement among annotators using average per-layer precision, sensitivity, and Dice score. Results: The SS showed the largest difference in annotation frequency (31%) and the minimum overall agreement in terms of consensus size (∼28% of the labeled pixels). The average annotator's per-layer statistics showed consistent patterns, with lower agreement on the CBB and SS (average Dice score ranges of 0.61–0.7 and 0.73–0.78, respectively) and better agreement on the IR, TM, and C (average Dice score ranges of 0.97–0.98, 0.84–0.9, and 0.93–0.96, respectively). Conclusions: There was considerable inter-annotator variation in identifying contours of some anatomical layers in digital gonio photographs. Our pilot indicates that agreement was best on IR, TM, and C but poorer for CBB and SS. Translational Relevance: This study provides a comprehensive description of inter-annotator agreement on digital gonio photographs segmentation as a baseline for validating deep learning models for automated gonioscopy.