A Meta-Analysis of the Diagnosis of Condylar and Mandibular Fractures Based on 3-dimensional Imaging and Artificial Intelligence.

Journal: The Journal of craniofacial surgery
Published Date: Jul 8, 2025

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.

Authors

  • Fan Wang
    Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
  • Xuejiao Jia
    Department of CT, Faculty of Medicine, Shanxi Medical University Second Affiliated Hospital.
  • Zhao Meiling
    Department of Stomatology, Sinochem Second Construction Group Hospital, Taiyuan, China.
  • Fahmi Oscandar
    Department of Oral and Maxillofacial Radiology-Forensic Odontology, Faculty of Dentistry. Universitas Padjadjaran, Bandung, West Java, Indonesia.
  • Hadhrami Ab Ghani
    Faculty of Data Science and Computing, Universiti Malaysia Kelantan.
  • Marzuki Omar
    School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kota Bharu, Kelantan, Malaysia.
  • Su Li
    School of Automation, Chongqing University, Chongqing, China.
  • Li Sha
    Department of Community Health, Advanced Medical & Dental Institute, Universiti Sains Malaysia, Pulau Pinang, Malaysia.
  • Junping Zhen
    Department of CT, Faculty of Medicine, Shanxi Medical University Second Affiliated Hospital.
  • Yuan Yuan
    Department of Geriatrics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
  • Bin Zhao
    University of Michigan Medical School, Ann Arbor, MI 48109, USA.
  • Johari Yap Abdullah
    Craniofacial Imaging Laboratory, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, 16150 Kubang Kerian, Kota Bharu, Kelantan, Malaysia. johariyap@usm.my.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals