Maxillofacial Fracture Detection Using Transfer Learning Models : A Review

Abstract

Early detection and treatment of face bone fractures reduce long-term problems. Fracture identification needs CT scan interpretation, but there aren't enough experts. To address these issues, researchers are classifying and identifying objects. Categorization-based studies can't pinpoint fractures. Proposed Study Convolutional neural networks with transfer learning may detect maxillofacial fractures. CT scans were utilized to retrain and fine-tune a convolutional neural network trained on non-medical images to categorize incoming CTs as "Positive" or "Negative." Model training employed maxillofacial fractogram data. If two successive slices had a 95% fracture risk, the patient had a fracture. In terms of sensitivity/person for facial fractures, the recommended strategy beat the machine learning model. The recommended approach may minimize physicians' effort identifying facial bone fractures in face CT. Even though technology can't fully replace a radiologist, the recommended technique may be helpful. It reduces human error, diagnostic delays, and hospitalization costs.