J Korean Dent Sci.  2023 Dec;16(2):156-163. 10.5856/JKDS.2023.16.2.156.

Deep Learning Based Radiographic Classification of Morphology and Severity of Peri-implantitis Bone Defects: A Preliminary Pilot Study

• Lee JH
• Yun JH

• KMID: 2549610
• DOI: http://doi.org/10.5856/JKDS.2023.16.2.156

Abstract

Purpose
The aim of this study was to evaluate the feasibility of deep learning techniques to classify the morphology and severity of peri-implantitis bone defects based on periapical radiographs.
Materials and Methods
Based on a pre-trained and fine-tuned ResNet-50 deep learning algorithm, the morphology and severity of peri-implantitis bone defects on periapical radiographs were classified into six groups (class I/II and slight/moderate/severe). Accuracy, precision, recall, and F1 scores were calculated to measure accuracy. Result: A total of 971 dental images were included in this study. Deep-learning-based classification achieved an accuracy of 86.0% with precision, recall, and F1 score values of 84.45%, 81.22%, and 82.80%, respectively. Class II and moderate groups had the highest F1 scores (92.23%), whereas class I and severe groups had the lowest F1 scores (69.33%).
Conclusion
The artificial intelligence-based deep learning technique is promising for classifying the morphology and severity of peri-implantitis. However, further studies are required to validate their feasibility in clinical practice.

Keyword

Artificial intelligence; Deep learning; Dental implants; Dental radiography; Peri-implantitis