Eye & contact lens · 2026
Supervised Machine Learning Models for Ocular Sagittal Height Prediction Incorporating Corneoscleral Profile Data.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
González-Cruces Timoteo, Carrillo-Pulido Miriam, Aguilar-Salazar Francisco Javier, Cano-Ortiz Antonio, Ionut Ghinea Razvan, Ortiz-Peregrina Sonia
Аннотация
OBJECTIVE: This study aimed to develop supervised machine learning (ML) models to predict ocular sagittal height (OC-SAG), using anterior eye data derived from topography and tomography.
METHOD: ology: A retrospective cohort of 100 eyes provided data such as keratometry, eccentricity, white-to-white distance, and corneoscleral junction (CSJ) metrics through anterior segment optical coherence tomography (CASIA 2). Four supervised ML models were developed to predict OC-SAG at 10 mm and 14 mm chord lengths, using either topographical data alone or both topographical and tomographical data.
RESULTS: The model with the strongest predictive accuracy for the validation sample was the Random Forest, both for the 10-mm chord (r=0.88; MAE=28.16±26.81 μm) and the 14-mm chord (r=0.77; MAE=73.56±61.31 μm; flat corneal meridian, topography-based model). The inclusion of tomographic data, such as measurements related to the CSJ, did not significantly improve the performance of the models compared with using topographic data alone.
CONCLUSIONS: Adding tomographic predictors did not significantly improve model performance. Despite reduced performance for longer chords, the observed error could be considered clinically acceptable for soft contact lens fitting. The study demonstrated the potential of ML in predicting OC-SAG, providing an accessible estimation method for all clinicians to support decision-making.
METHOD: ology: A retrospective cohort of 100 eyes provided data such as keratometry, eccentricity, white-to-white distance, and corneoscleral junction (CSJ) metrics through anterior segment optical coherence tomography (CASIA 2). Four supervised ML models were developed to predict OC-SAG at 10 mm and 14 mm chord lengths, using either topographical data alone or both topographical and tomographical data.
RESULTS: The model with the strongest predictive accuracy for the validation sample was the Random Forest, both for the 10-mm chord (r=0.88; MAE=28.16±26.81 μm) and the 14-mm chord (r=0.77; MAE=73.56±61.31 μm; flat corneal meridian, topography-based model). The inclusion of tomographic data, such as measurements related to the CSJ, did not significantly improve the performance of the models compared with using topographic data alone.
CONCLUSIONS: Adding tomographic predictors did not significantly improve model performance. Despite reduced performance for longer chords, the observed error could be considered clinically acceptable for soft contact lens fitting. The study demonstrated the potential of ML in predicting OC-SAG, providing an accessible estimation method for all clinicians to support decision-making.