Related Experiment Video
Updated: May 28, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
DEEP LEARNING-DRIVEN SEGMENTATION OF DENTAL IMPLANTS AND PERI-IMPLANTITIS DETECTION IN ORTHOPANTOMOGRAPHS: A NOVEL
Erdoğan Kibcak1, Oğuz Buhara1, Ali Temelci2
1Near East University, Department of Oral and Maxillofacial Surgery, Nicosia, Cyprus.
Introduction And Objective:
Dental implants are well-established for restoring partial or complete tooth loss, with osseointegration being essential for their long-term success. Peri-implantitis, marked by inflammation and bone loss, compromises implant longevity. Current diagnostic methods for peri-implantitis face challenges such as subjective interpretation and time consumption. Our deep learning-based approach aims to address these limitations by providing a more accurate and efficient solution. This study aims to develop a deep learning-based approach for segmenting dental implants and detecting peri-implantitis in orthopantomographs (OPGs), enhancing diagnostic accuracy and efficiency.
Materials And Methods:
After applying exclusion criteria, 7696 OPGs were used in the study, which was ethically authorized by the Near East University Ethics Review Board. Using the Python-implemented U-Net architecture, the DICOM-formatted images were segmented and converted into PNG files. The classification model used a convolutional neural network (CNN) for distinguishing between healthy implants and those affected by peri-implantitis, leveraging features extracted from the segmented regions to enhance diagnostic accuracy. The model was trained for 500 epochs using the Adam optimizer, with the dataset split into training (70%), validation (15%), and test (15%) sets. Dice similarity coefficient (DSC) and accuracy were used to assess segmentation performance. Three medical professionals used precision, recall, and F1-score to assess the classification model after segmentation, which determined whether implants were showing signs of peri-implantitis.
Results:
The segmentation model achieved a test accuracy of 0.999, Dice Similarity Coefficient (DSC) of 0.986, and Intersection over Union (IoU) of 0.974. For classification, out of 3693 implants, 638 were clinically identified as having peri-implantitis. The model correctly identified 576 of these, with 165 false positives. Performance metrics included a precision of 0.777, recall of 0.903, and F1-score of 0.835.
Conclusion:
The deep learning-based approach for segmentation and classification of dental implants and peri-implantitis in OPGs is highly effective, providing reliable tools for enhancing clinical diagnosis and treatment planning.
