Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network.

ABSTRACT: After nasal bone fractures, fractures of the mandible are the most frequently encountered injuries of the facial skeleton. Accurate identification of fracture locations is critical for effectively managing these injuries. To address this need, JawFracNet, an innovative artificial intelligence method, has been developed to enable automated detection of mandibular fractures in cone-beam computed tomography (CBCT) scans. JawFracNet employs a 3-stage neural network model that processes 3-dimensional patches from a CBCT scan. Stage 1 predicts a segmentation mask of the mandible in a patch, which is subsequently used in stage 2 to predict a segmentation of the fractures and in stage 3 to classify whether the patch contains any fracture. The final output of JawFracNet is the fracture segmentation

SUBMITTER: van Nistelrooij N 

PROVIDER: S-EPMC11633064 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature