A support vector machine-based algorithm to identify bisphosphonate-related osteonecrosis throughout the mandibular bone by using cone beam computerized tomography images.

Objective: This study aimed to develop an algorithm to distinguish the patients with bisphosphonate-related osteonecrosis of the jaws (BRONJ) from healthy controls using CBCT images by evaluating both trabecular and cortical bone changes through the whole body of the mandibular bone.

Methods: Patient data set was created from axial CBCT images of 7 BRONJ patients (28 slices) and 8 healthy controls (27 slices). The healthy bone of healthy controls, bone sclerosis of BRONJ patients, bone necrosis of BRONJ patients, and normal appearing bone of BRONJ patients (NBP) were labeled on CBCT images by three maxillofacial radiologists. Proposed algorithm had preparation and background cancellation, mandibular bone segmentation and centerline determination, spatial transformation of gray values, and classification steps.

Results: Significant differences between the statistical moments (mean, variance, skewness, kurtosis, standard error, median, mode and coefficient of variance) of healthy and diseased (bone sclerosis and necrosis) groups were observed ( = 0.000, < 0.05). Also, variations were noted between healthy controls and NBP of BRONJ patients ( = 0.000, < 0.05).The statistical moments were utilized to develop the algorithm which has resulted with accuracy of 0.999, sensitivity of 0.998, specificity of 0.998, precision of 1, recall of 0.998, AUC of 1, and F1 score of 0.999 in identification of BRONJ patients from healthy ones.

Conclusion: The proposed algorithm differentiated the mandibular bones of the healthy and the BRONJ patients with high accuracy in the present test sample.