Purpose: Optical coherence tomography (OCT) is essential for the diagnosis and follow-up of corneal edema, but assessment can be challenging in minimal or localized edema. The objective was to develop and validate a novel automated tool to detect and visualize corneal edema with OCT. Methods: We trained a convolutional neural network to classify each pixel in the corneal OCT images as “normal” or “edema” and to generate colored heat maps of the result. The development set included 199 OCT images of normal and edematous corneas. We validated the model's performance on 607 images of normal and edematous corneas of various conditions. The main outcome measure was the edema fraction (EF), defined as the ratio between the number of pixels labeled as edema and those representing the cornea for each scan. Overall accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were determined to evaluate the model's performance. Results: Mean EF was 0.0087 ± 0.01 in the normal scans and 0.805 ± 0.26 in the edema scans (P < 0.0001). Area under the receiver operating characteristic curve for EF in the diagnosis of corneal edema in individual scans was 0.994. The optimal threshold for distinguishing normal from edematous corneas was 6.8%, with an accuracy of 98.7%, sensitivity of 96.4%, and specificity of 100%. Conclusions: The model accurately detected corneal edema and distinguished between normal and edematous cornea OCT scans while providing colored heat maps of edema presence.

Purpose: To combine objective machine-derived corneal parameters obtained with new swept-source optical coherence tomography (SS-OCT) tomographer (Anterion) to differentiate between normal (N), keratoconus (KC) and forme fruste KC (FFKC). Setting: Laser Center, Hôpital Fondation Adolphe de Rothschild, Paris, France. Design: Retrospective study. Methods: 281 eyes of 281 patients were included and divided into 3 groups: N (n = 156), FFKC (n = 43), and KC (n = 82). Eyes were included in each group based on objective evaluation using Nidek Corneal Navigator, and subjective evaluation by authors. The SS-OCT system provided anterior and posterior corneal surface and pachymetry derived variables. The training set was composed of 143 eyes (95 N, 43 FFKC). Discriminant analysis was used to determine the group of an observation based on a set of variables. The obtained formula was tested in the validation set composed of 61 N and 82 KC. Results: Among curvature parameters, the FFKC had significantly higher irregularity index at 3 mm and 5 mm, higher inferior-superior index, higher SteepK-OppositeK index and inferiorly decentered posterior steepest keratometry. Among thickness parameters: central pachymetry, thinnest pachymetry, percentage of thickness increase from center to periphery, and inferior decentration of the thinnest point were statistically different between groups. Combination of multiple variables into a discriminant function (F1) included 5 parameters and reached an area under the receiver operating characteristic curve (AUROC) of 0.95 (sensitivity = 75%, specificity = 98.5%) for detection of FFKC. F1 differentiates N from KC with AUROC = 0.99 (sensitivity = 99%, specificity = 99%). Conclusions: Combining anterior and posterior curvatures variables along with pachymetric data obtained from SS-OCT allowed automated detection of early KC and KC with very good accuracy (87% and 99.5% respectively).

Purpose: To assess a new objective deep learning model cataract grading method based on swept-source optical coherence tomography (SS-OCT) scans provided by the Anterion. Setting: Single-center study at the Rothschild Foundation, Paris, France. Design: Prospective cross-sectional study. Methods: All patients consulting for cataract evaluation and consenting to study participation were included. History of previous ocular surgery, cornea or retina disorders, and ocular dryness were exclusion criteria. Our CATALYZE pipeline was applied to Anterion image providing layerwise cataract metrics and an overall clinical significance index (CSI) of cataract. Ocular scatter index (OSI) was also measured with a double-pass aberrometer (OQAS) and compared with our CSI. Results: 548 eyes were included, 331 in the development set (48 with cataract and 283 controls) and 217 in the validation set (85 with cataract and 132 controls) of 315 patients aged 19 to 85 years (mean ± SD: 50 ± 21 years). The CSI correlated with the OSI (r ² = 0.87, P < .01). CSI area under the receiver operating characteristic curve (AUROC) was comparable with OSI AUROC (0.985 vs 0.981 respectively, P > .05) with 95% sensitivity and 95% specificity. Conclusions: The deep learning pipeline CATALYZE based on Anterion SS-OCT may be a reliable and comprehensive objective cataract grading method.

The accurate detection of corneal edema has become a topic of growing interest with the generalization of endothelial keratoplasty. Despite recent advances in deep learning for corneal edema detection, the problem of minimal edema remains challenging. Using transfer learning and a limited training set of 11 images, we built a model to segment the corneal epithelium, which is part of a three-model pipeline to detect corneal edema. A second and a third model are used to detect edema on the stroma alone and on the epithelium. A validation set of 233 images from 30 patients consisting of three groups (Normal, Minimal Edema and important Edema) was used to compare the results of our new pipeline to our previous model. The mean edema fraction (EF), defined as the number of pixels detected as edema divided by the total number of pixels of the cornea, was calculated for each image. With our previous model, the mean EF was not statistically different between the Normal and Minimal Edema groups (p = 0.24). With the current pipeline, the mean EF was higher in the Minimal Edema group compared to the Normal group (p < 0.01). The described pipeline constitutes an adjustable framework for the detection of corneal edema based on optical coherence tomography and yields better performances in cases of minimal or localized edema.