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KMID : 1036420230160020156
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대한치의학회 영문학회지
2023 Volume.16 No. 2 p.156 ~ p.163
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Deep Learning Based Radiographic Classification of Morphology and Severity of Peri-implantitis Bone Defects: A Preliminary Pilot Study
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Lee Jae-Hong
Yun Jeong-Ho
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Abstract
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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.
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KEYWORD
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Artificial intelligence, Deep learning, Dental implants, Dental radiography, Peri-implantitis
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