On Clinical Agreement on the Visibility and Extent of Anatomical Layers in Digital Gonio Photographs
| Autor: | Mauro Campigotto, Carlo Alberto Cutolo, Emanuele Trucco, Stewart N. Gillan, Andrew J. Tatham, Anna Paviotti, Yue Shi, Jacintha Gong, Andrea Peroni, Caroline Cobb, Luís Abegão Pinto, Sirjhun Patel |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.diagnostic_test
Pixel Anterior Chamber Biomedical Engineering Gonioscopy Scleral spur Iris Dice AI software validation Article Ophthalmology medicine.anatomical_structure Automated gonioscopy Inter-annotator variability Trabecular Meshwork medicine Photography Segmentation automated gonioscopy Visibility Cartography inter-annotator variability Iridocorneal angle Mathematics |
| Zdroj: | Translational Vision Science & Technology |
| ISSN: | 2164-2591 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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