Development of a highly accurate machine learning model for the differential diagnosis of ameloblastoma and odontogenic keratocyst on computed tomography images.

OBJECTIVE: To develop a machine learning model using computed tomography (CT) images for preoperative differential diagnosis of ameloblastoma (AM) and odontogenic keratocyst (OKC), and to compare its performance with that of an oral and maxillofacial radiologist. STUDY DESIGN: This retrospective study analyzed CT images from 154 patients (70 AM, 84 OKC). Seventeen clinical and imaging features were extracted and compiled into a CSV file. The data were randomly divided into five subsets for five-fold cross-validation. In each fold, 80% of the data were used for training and 20% for testing. Two machine learning models-Prediction One and Random Forest-were trained on the training data and evaluated on the test data. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves, and the AUC were calculated. The performance of each model was compared with that of a radiologist blinded to clinical data. Variable importance was also analyzed to identify key diagnostic features. RESULTS: Prediction One achieved an AUC of 0.95. Random Forest achieved an AUC of 0.96. Both models significantly outperformed the radiologist (AUC: 0.80, P < .05). The most influential diagnostic features were root resorption and locularity. CONCLUSION: Machine learning models accurately distinguished AM from OKC and may assist preoperative diagnosis and planning.