American journal of ophthalmology · 2026
Optical Coherence Tomography Radiomics and Machine Learning Enable Accurate Detection of Forme Fruste Keratoconus.
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Luo Shenglong, Li Xuefei, Lin Kuangching, Bao Fangjun, Chen Shihao, Lu Fan, Wang Junjie
Аннотация
PURPOSE: To evaluate the diagnostic performance of a radiomics-based machine learning approach applied to corneal optical coherence tomography (OCT) images for detecting forme fruste keratoconus (FFKC).
DESIGN: Evaluation of machine learning diagnostic algorithms.
METHODS: OCT images from 307 eyes (234 normal, 73 FFKC) were acquired along eight meridians (M1-M8). All images underwent preprocessing before texture-based radiomics feature extraction. Three machine learning classifiers-Random Forest, C5.0, and XGBoost-were trained using a feature subset selected by recursive feature elimination (RFE). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: A total of 3752 features were extracted per eye, of which 41 were selected for model training. All three models demonstrated strong diagnostic performance in the test set (AUCs > 0.92), with no significant differences between models (P > .05). The XGBoost model achieved the highest performance (AUC = 0.93, 95% CI: 0.829-1.0, sensitivity 0.857, specificity = 0.978, accuracy = 0.950). Among the top 10 XGBoost features ranked by importance, a preferred meridional distribution was observed, with most features concentrated along M1 to M3, corresponding to the inferotemporal corneal region.
CONCLUSION: Radiomics analysis of corneal OCT images combined with machine learning enables accurate FFKC detection using a single imaging device, providing diagnostic information beyond conventional morphological assessment and suggesting a potential imaging biomarker for early keratoconus screening.
DESIGN: Evaluation of machine learning diagnostic algorithms.
METHODS: OCT images from 307 eyes (234 normal, 73 FFKC) were acquired along eight meridians (M1-M8). All images underwent preprocessing before texture-based radiomics feature extraction. Three machine learning classifiers-Random Forest, C5.0, and XGBoost-were trained using a feature subset selected by recursive feature elimination (RFE). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: A total of 3752 features were extracted per eye, of which 41 were selected for model training. All three models demonstrated strong diagnostic performance in the test set (AUCs > 0.92), with no significant differences between models (P > .05). The XGBoost model achieved the highest performance (AUC = 0.93, 95% CI: 0.829-1.0, sensitivity 0.857, specificity = 0.978, accuracy = 0.950). Among the top 10 XGBoost features ranked by importance, a preferred meridional distribution was observed, with most features concentrated along M1 to M3, corresponding to the inferotemporal corneal region.
CONCLUSION: Radiomics analysis of corneal OCT images combined with machine learning enables accurate FFKC detection using a single imaging device, providing diagnostic information beyond conventional morphological assessment and suggesting a potential imaging biomarker for early keratoconus screening.