Search

center dot PURPOSE: To optimize artificial intelligence (AI) algo-rithms to integrate Scheimpflug-based corneal tomogra-phy and biomechanics to enhance ectasia detection.center dot DESIGN: Multicenter cross-sectional case-control ret-rospective study.center dot METHODS: A total of 3886 unoperated eyes from 3412 patients had Pentacam and Corvis ST (Oculus Op-tikgerate GmbH) examinations. The database included 1 eye randomly selected from 1680 normal patients (N) and from 1181 "bilateral" keratoconus (KC) patients, along with 551 normal topography eyes from patients with very asymmetric ectasia (VAE-NT), and their 474 unoperated ectatic (VAE-E) eyes. The current TBIv1 (tomographic-biomechanical index) was tested, and an optimized AI algorithm was developed for augmenting accuracy.center dot RESULTS: The area under the receiver operating char-acteristic curve (AUC) of the TBIv1 for discriminating clinical ectasia (KC and VAE-E) was 0.999 (98.5% sen-sitivity; 98.6% specificity [cutoff: 0.5]), and for VAE-NT, 0.899 (76% sensitivity; 89.1% specificity [cutoff: 0.29]). A novel random forest algorithm (TBIv2), devel-oped with 18 features in 156 trees using 10-fold cross -validation, had a significantly higher AUC (0.945; De -Long, P < .0001) for detecting VAE-NT (84.4% sen-sitivity and 90.1% specificity; cutoff: 0.43; DeLong, P < .0001) and a similar AUC for clinical ectasia (0.999; DeLong, P = .818; 98.7% sensitivity; 99.2% specificity [cutoff: 0.8]). Considering all cases, the TBIv2 had a higher AUC (0.985) than TBIv1 (0.974; DeLong, P < .0001).center dot CONCLUSIONS: AI optimization to integrate Scheimpflug-based corneal tomography and biome-chanical assessments augments accuracy for ectasia detection, characterizing ectasia susceptibility in the diverse VAE-NT group. Some patients with VAE may have true unilateral ectasia. Machine learning consider-ing additional data, including epithelial thickness or other parameters from multimodal refractive imaging, will con-tinuously enhance accuracy. NOTE: Publication of this article is sponsored by the American Ophthalmological Society. (Am J Ophthalmol 2023;251: 126-142. (c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ))

To test the ability of the corneal epithelial pattern standard deviation to distinguish between normal and cases with corneal ectatic condition.Instituto de Olhos Renato Ambrósio, Rio de janeiro, Brazil.Cross-sectional retrospective study.Patients were stratified into four groups based on clinical data and.(Groups 1 and 2 comprised one eye randomly selected from 105 patients with normal corneas (N) and 86 patients with bilateral keratoconus (KC). Groups 3 and 4, respectively comprised 11 ectatic eyes with no surgical treatment for keratoconus (VAE-E) from patients whose fellow eyes (61) presented with normal topographic patterns (VAE-NT). Corneas were scanned using an OCT system (RT Vue) and Scheimpflug corneal tomography (Pentacam), and also had biomechanical assessment through the Corvis ST. Corneal epithelial thickness maps were analyzed and the pattern standard deviation (PSD) value was calculated. The area under the ROC curve analysis was used to evaluate the diagnostic accuracy of the indices.Epithelial PSD was significantly different across the four groups (p0.0001). The pairwise comparison revealed that the normal group presented significantly lower values than both ectasia groups (KC and VAE-E, p0.0001) and the VAE-NT group (p=0.0008). There was no significant difference between KC and VAE-E (p=0.4284), while they were significantly higher than the VAE-NT group (p0.0001 and p=0.0004).Epithelial PSD can be used to detect abnormal epithelial thickness patterns. Corneal epithelial thickness changes could be detected accurately in keratoconus patients, even in the form fruste of the disease.