Journal of refractive surgery (Thorofare, N.J. : 1995) · 2026
Machine Learning Prediction Models for Classifying Myopic Versus Hyperopic Ablation Patterns in Eyes With Previous Refractive Surgery Undergoing Cataract Surgery.
СтатьяНаблюдательное исследованиеНазвание 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.
Bustos-Ampudia Carel, Maldonado-Aparicio Jair, De La Fuente-Batta Isabel, Peimbert-Franco Daniela A, Yáñez-Oviedo Guillermo E, Camacho-Ordonez Azyadeh, Chávez-Mondragón Eduardo, Irusteta-Jimenez Leire
Аннотация
PURPOSE: To develop a machine learning model to classify myopic or hyperopic ablation patterns in eyes that had previous refractive surgery and are now having cataract surgery using corneal topographic and tomographic parameters.
METHODS: This retrospective observational study was conducted at the Instituto de Oftalmología Conde de Valenciana, Mexico City, Mexico. Machine learning analyses were performed using Orange Data Mining software (Biometrics Laboratory, University of Ljubljana, Slovenia) with a 70/30 training-testing split. Predictive variables included corneal asphericity (Q-value), posterior-to-anterior curvature ratio (P/A ratio), sagittal map morphology, spherical aberration (Z04), mean keratometry, central corneal thickness, and thinnest pachymetry. Random Forest, Decision Tree, and Logistic Regression models were trained and compared. Model performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC).
RESULTS: Seventy-two eyes with a history of refractive surgery were analyzed: 16.67% (n = 12) had hyperopic and 83.33% (n = 60) had myopic ablations. P/A ratio and Z04 showed a strong negative correlation (r = -0.793, P < .001) for classifying the ablation type. The Random Forest model achieved the highest discriminative performance (AUC = 0.933), followed by Logistic Regression (AUC = 0.881) and Decision Tree (AUC = 0.828). Decision Tree modeling identified clinically interpretable cut-off values for classification, with a P/A ratio of 82.7% and a Z04 value of -0.144 emerging as key decision nodes for differentiating myopic from hyperopic ablation patterns.
CONCLUSIONS: Machine learning-based classification models distinguished myopic from hyperopic ablation patterns, with the Random Forest model demonstrating the highest performance using corneal topographic data. These models may serve as valuable adjuncts for intraocular lens power calculation in cataract surgery in eyes that had previous refractive surgery, especially when prior surgical records are unavailable.
METHODS: This retrospective observational study was conducted at the Instituto de Oftalmología Conde de Valenciana, Mexico City, Mexico. Machine learning analyses were performed using Orange Data Mining software (Biometrics Laboratory, University of Ljubljana, Slovenia) with a 70/30 training-testing split. Predictive variables included corneal asphericity (Q-value), posterior-to-anterior curvature ratio (P/A ratio), sagittal map morphology, spherical aberration (Z04), mean keratometry, central corneal thickness, and thinnest pachymetry. Random Forest, Decision Tree, and Logistic Regression models were trained and compared. Model performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC).
RESULTS: Seventy-two eyes with a history of refractive surgery were analyzed: 16.67% (n = 12) had hyperopic and 83.33% (n = 60) had myopic ablations. P/A ratio and Z04 showed a strong negative correlation (r = -0.793, P < .001) for classifying the ablation type. The Random Forest model achieved the highest discriminative performance (AUC = 0.933), followed by Logistic Regression (AUC = 0.881) and Decision Tree (AUC = 0.828). Decision Tree modeling identified clinically interpretable cut-off values for classification, with a P/A ratio of 82.7% and a Z04 value of -0.144 emerging as key decision nodes for differentiating myopic from hyperopic ablation patterns.
CONCLUSIONS: Machine learning-based classification models distinguished myopic from hyperopic ablation patterns, with the Random Forest model demonstrating the highest performance using corneal topographic data. These models may serve as valuable adjuncts for intraocular lens power calculation in cataract surgery in eyes that had previous refractive surgery, especially when prior surgical records are unavailable.