Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography.

Journal: BMC oral health
Published Date: Jun 6, 2025

Abstract

OBJECTIVES: Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms for detecting and diagnosing dental caries using cone-beam computed tomography (CBCT) with the Mask R-CNN architecture while comparing various hyperparameters to enhance detection.

Authors

  • Yujie Ma
    School of pharmacy, Nanjing medical university, Nanjing, Jiangsu, 211166, People's Republic of China.
  • Maged Ali Al-Aroomi
    Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, Hunan Province, 410008, China.
  • Yutian Zheng
    The College of Mechanical and Electrical Engineering, Central South University, Changsha, Hunan Province, China.
  • Wenjie Ren
    School of Food Science and Technology, Henan University of Technology, Zhengzhou, Henan 450001, P. R. China.
  • Peixuan Liu
    Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, Hunan Province, 410008, China.
  • Qing Wu
    5 Department of Environmental and Occupational Health, School of Community Health Sciences, University of Nevada , Las Vegas, Nevada.
  • Ye Liang
    Department of Statistics, Oklahoma State University, Stillwater, OK, USA.
  • CanHua Jiang
    Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan Province, China.

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