A Meta-Analysis of the Diagnosis of Condylar and Mandibular Fractures Based on 3-dimensional Imaging and Artificial Intelligence.
Authors
Affiliations (11)
Affiliations (11)
- Shanxi Medical University School and Hospital of Stomatology.
- School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kota Bharu, Kelantan, Malaysia.
- Department of CT, Faculty of Medicine, Shanxi Medical University Second Affiliated Hospital.
- Department of Stomatology, Sinochem Second Construction Group Hospital, Taiyuan, China.
- Department of Oral and Maxillofacial Radiology-Forensic Odontology, Faculty of Dentistry. Universitas Padjadjaran, Bandung, West Java, Indonesia.
- Faculty of Data Science and Computing, Universiti Malaysia Kelantan.
- Department of Stomatology, Beijing Xuanwu Traditional Chinese Medicine Hospital, Beijing, China.
- Department of Community Health, Advanced Medical & Dental Institute, Universiti Sains Malaysia, Pulau Pinang, Malaysia.
- School of Basic Medical Sciences, Shanxi Medical University.
- Shanxi Medical University School and Hospital of Stomatology, Taiyuan, Shanxi, China.
- Dental Research Unit, Center for Transdisciplinary Research (CFTR), Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College, Saveetha University, Chennai, India.
Abstract
This article aims to review the literature, study the current situation of using 3D images and artificial intelligence-assisted methods to improve the rapid and accurate classification and diagnosis of condylar fractures and conduct a meta-analysis of mandibular fractures. Mandibular condyle fracture is a common fracture type in maxillofacial surgery. Accurate classification and diagnosis of condylar fractures are critical to developing an effective treatment plan. With the rapid development of 3-dimensional imaging technology and artificial intelligence (AI), traditional x-ray diagnosis is gradually replaced by more accurate technologies such as 3-dimensional computed tomography (CT). These emerging technologies provide more detailed anatomic information and significantly improve the accuracy and efficiency of condylar fracture diagnosis, especially in the evaluation and surgical planning of complex fractures. The application of artificial intelligence in medical imaging is further analyzed, especially the successful cases of fracture detection and classification through deep learning models. Although AI technology has demonstrated great potential in condylar fracture diagnosis, it still faces challenges such as data quality, model interpretability, and clinical validation. This article evaluates the accuracy and practicality of AI in diagnosing mandibular fractures through a systematic review and meta-analysis of the existing literature. The results show that AI-assisted diagnosis has high prediction accuracy in detecting condylar fractures and significantly improves diagnostic efficiency. However, more multicenter studies are still needed to verify the application of AI in different clinical settings to promote its widespread application in maxillofacial surgery.