DEEP LEARNING-DRIVEN SEGMENTATION OF DENTAL IMPLANTS AND PERI-IMPLANTITIS DETECTION IN ORTHOPANTOMOGRAPHS: A NOVEL DIAGNOSTIC TOOL

Abstract

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.