Deep learning for automated alveolar cleft segmentation and bone graft volume estimation in cone-beam computed tomography imaging : a multicenter study
Objective To train and validate a deep learning-based diagnostic tool capable of accurately segmenting the alveolar cleft region and automatically estimating the required bone graft volume using cone-beam computed tomography (CBCT) imaging. Study Design Eighty-eight CBCT scans from patients with nonsyndromic unilateral clefts were divided into training (n = 45), validation (n = 10), and test (n =
