Possibilities of artificial intelligence with the use of a structured algorithm in the diagnosis of keratoconus (Ukrainian Journal of Ophthalmology, 2025)
Purpose: To assess the efficacy of using artificial intelligence (AI) in the diagnosis of keratoconus based on corneal topography maps, with subsequent determination of disease stage and examining the impact of the level of standardization of the clinical algorithm on the accuracy and consistency of results.Material and Methods: Fifty-nine corneal topography maps of 37 patients from the database of the ACE DIAGNOSTIC PLATFORM (Baush+Lomb) were retrospectively analyzed. The study was conducted in three stages which simulated various levels of AI integration, from a completely autonomous AI operation to the operation strictly according to a three-level structured algorithm that we have developed (Copyright for the Work No. 139012 issued 26.08.2025). The adapted language model of ChatGPT (OpenAI, 2023) was used for establishing the diagnosis and staging. Data were statistically analyzed using Microsoft Excel 2019 and Analysis ToolPak. Data analysis was conducted by solving binary and multi-class classification problems. Accuracy, sensitivity, specificity, precision and the F1-measure were assessed at each stage and compared across stages.Results: The overall accuracy and accuracy of staging were 79.7% and 33.3%, respectively, at stage 1, and improved to 93.2% and 74.36%, respectively, at stage 2. The highest values of characteristics were seen at stage 3 (sensitivity, 100%; specificity, 95%; overall accuracy, 98%; and accuracy of staging, 89.7%).Conclusion: AI demonstrated a potential in the automated diagnosis of keratoconus. However, the best efficacy was achieved only when AI was used in the presence of a clearly structured algorithm. Autonomous AI decision led to a high variation in results. Introduction of algorithmic logic enabled significant improvements in the accuracy and consistency of results.
Keywords: artificial intelligence; ChatGPT; keratoconus; corneal topography