Assessment of Anterior Uveitis Through Anterior-Segment Optical Coherence Tomography and Artificial Intelligence-Based Image Analyses

• Main Authors: Alberti, M. (Author), Cabrerizo, J. (Author), Ilginis, T. (Author), Potapenko, I.O (Author), Sorkhabi, M.A (Author)
• Format: Article
• Language: English
• Published: NLM (Medline) 2022
• Online Access: View Fulltext in Publisher
• ISBN: 21642591 (ISSN)
• DOI: 10.1167/tvst.11.4.7

Purpose: The purpose of this study was to develop an automated artificial intelligence (AI) based method to quantify inflammation in the anterior chamber (AC) using anterior-segment optical coherence tomography (AS-OCT) and to explore the correlation between AI assisted AS-OCT based inflammation analyses and clinical grading of anterior uveitis by Standardization of Uveitis Nomenclature (SUN). Methods: A prospective double blinded study of AS-OCT images of 32 eyes of 19 patients acquired by Tomey CASIA-II. OCT images were analyzed with proprietary AI-based software. Anatomic boundaries of the AC were segmented automatically by the AI software and Spearman's rank correlation between parameters related to AC cellular inflammation were calculated. Results: No significant (p = 0.6602) differences were found between the analyzed AC areas between samples of the different SUN grading, suggesting accurate and unbiased border detection/AC segmentation. Segmented AC areas were processed by the AI software and particles within the borders of AC were automatically counted by the software. Statistical analysis found significant (p < 0.001) correlation between clinical SUN grading and AI software detected particle count (Spearman ρ = 0.7077) and particle density (Spearman ρ = 0.7035). Significant (p < 0.001) correlation (Pearson's r = 0.9948) between manually and AI detected particles was found. No significant (p = 0.8080) difference was found between the sizes of the AI detected particles for all studies. Conclusions: AI-based image analysis of AS-OCT slides show significant and independent correlation with clinical SUN assessment. Translational Relevance: Automated AI-based AS-OCT image analysis suggests a noninvasive and quantitative assessment of AC inflammation with clear potential application in early detection and management of anterior uveitis.