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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 =
