Detection of Missing Tooth Regions Using Deep Learning in Panoramic Radiographs for Dental Implant Planning

Authors

  • Rajashree Nambiar Department of Robotics & AI, NMAM Institute of Technology (NMAMIT), Nitte (Deemed to be University), Nitte, Karnataka, India | Faculty of Engineering and Technology, JAIN (Deemed to be University), Bengaluru, India
  • Raghu Nanjundegowda Faculty of Engineering and Technology, Department of Electrical and Electronics Engineering, JAIN (Deemed to be University), Bengaluru, India
Volume: 15 | Issue: 5 | Pages: 28071-28076 | October 2025 | https://doi.org/10.48084/etasr.13101

Received: 1 July 2025 | Revised: 22 July 2025, 3 August 2025, and 17 August 2025 | Accepted: 20 August 2025 | Online: 22 September 2025


Corresponding author: Rajashree Nambiar

Abstract

The advances in dental radiology, particularly the utilization of panoramic radiographs, have significantly enhanced the precision of dental implant planning. This study introduces a novel deep learning-based approach for detecting missing tooth regions in panoramic radiographs, leveraging a region-based Convolution Neural Network (Mask R-CNN) with a Residual Neural Network (ResNet-101) to enhance the extraction of features from input data, such as the backbone for tooth segmentation and numbering. By integrating a heuristic algorithm, the proposed method improves detection accuracy and addresses common challenges such as multiple numbering errors and misalignments. The model was evaluated using a robust dataset, demonstrating superior performance metrics, including a precision of 0.9566, a recall of 0.9635, and a mean Average Precision (mAP) of 0.9241, compared to conventional methods. The results affirm the potential of this automated system to streamline dental implant planning, reduce clinician workload, and support advanced diagnostic and educational tools.

Keywords:

artificial intelligence, deep learning, dental implant, dentistry, image segmentation, Mask R-CNN, pragmatic algorithm

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How to Cite

[1]
R. Nambiar and R. Nanjundegowda, “Detection of Missing Tooth Regions Using Deep Learning in Panoramic Radiographs for Dental Implant Planning”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 5, pp. 28071–28076, Oct. 2025.

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