Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data
Artykuł w czasopiśmie
MNiSW
140
Lista 2024
| Status: | |
| Autorzy: | Syta Arkadiusz, Podkowiński Arkadiusz, Chorągiewicz Tomasz, Karpiński Robert, Gęca Jakub, Wróbel-Dudzińska Dominika, Jonak Katarzyna E., Głuchowski Dariusz, Maciejewski Marcin, Rejdak Robert, Jonak Kamil |
| Dyscypliny: | |
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| Rok wydania: | 2025 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 15 |
| Numer artykułu: | 24399 |
| Strony: | 1 - 15 |
| Impact Factor: | 3,9 |
| Web of Science® Times Cited: | 1 |
| Scopus® Cytowania: | 1 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | This research was supported by Polish Ministry of Education and Science, grant no. MEiN/2023/DPI/2194, project title: Lublin Digital Union—Use of Digital Solutions and Artificial Intelligence in Medicine—Research Project. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 8 lipca 2025 |
| Abstrakty: | angielski |
| Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of machine learning models to identify keratoconus based on corneal topography and biomechanical data. We collected a dataset comprising 144 corneal scans from adults aged 18–35, including an equal proportion of keratoconus and normal cases. Various machine learning algorithms were trained and evaluated on datasets containing different parameters obtained using the Pentacam device. The Random Forest algorithm demonstrated the highest reliability, achieving an accuracy of 98% during training and 96% on the test set, while also identifying the most diagnostically relevant measurements. Unlike prior studies, our approach enables detailed comparison between model-selected features and clinically recognized diagnostic parameters. This interpretability provides a clinically meaningful bridge between AI-driven predictions and expert-based decision-making. The results suggest that machine learning models, particularly Random Forest, can effectively aid in the early detection of keratoconus in young individuals, potentially improving patient outcomes through timely intervention. |
