Radiómica y aprendizaje automático en la detección temprana de trastornos orales potencialmente malignos

Oral potentially malignant disorders (OPMDs) are a diverse group of conditions that carry a risk of transformation into oral squamous cell carcinoma (OSCC). Early detection is vital to improving prognosis and survival. Recent advancements in radiomics and machine learning (ML) have shown promise in enhancing the diagnostic accuracy and early detection of OPMDs. Radiomics enables the extraction of quantitative data from medical images, while ML algorithms analyze patterns and correlations for predictive modeling. This review highlights the application of radiomics and ML in early OPMD detection, focusing on imaging modalities, data processing techniques, current research findings, and future challenges.