Feasibility of Automated Gonioscopy Imaging in Clinical Practice.

PRCIS: Automated gonioscopy provided good-quality images of the anterior chamber angle. There was a short learning curve for operators, and the examination was well tolerated by patients. Patients expressed a preference for automated gonioscopy compared with traditional gonioscopy. PURPOSE: The purpose of this study was to assess the feasibility of using a desktop automated gonioscopy camera in glaucoma clinics by examining patient tolerability, ease of use, and image quality and comparing patient preference compared with traditional gonioscopy. PATIENTS AND METHODS: A prospective study was conducted in a university hospital clinic. Traditional gonioscopy was performed followed by imaging of the iridocorneal angle (ICA) using the Nidek GS-1 camera by 2 glaucoma specialists. Participants were asked to rate the comfort of automated gonioscopy and which method they preferred. The clinicians graded the ease of acquisition for each patient, and the image quality was reviewed by a grader. RESULTS: Forty-three eyes of 25 participants were included. Sixty-eight percent of participants viewed automated gonioscopy as "extremely comfortable," and the remainder reported it "comfortable". Forty percent preferred automated gonioscopy compared with traditional gonioscopy, while 52% were equivocal. Clinicians scored 32% of participants as "somewhat difficult" to the image. In 46% of eyes, good-quality photographs were obtained for 360 degrees of the ICA. Only 1 eye had no parts of the ICA clearly visible. Seventy-four percent of eyes had at least half of the ICA clearly visible in all 4 quadrants. CONCLUSION: Automated gonioscopy provided good-quality images of the ICA for most patients. It was often not possible to image the entire 360 degrees at the first attempt, but the examination was comfortable for patients, and only 8% preferred traditional gonioscopy to the automated photographic examination.

Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation.

OBJECTIVE: To develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs. METHODS AND ANALYSIS: We used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout. RESULTS: The model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs. CONCLUSION: The proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

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

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.