Performance of artificial intelligence-based diagnosis and classification of peri-implantitis compared with periodontal surgeon assessment: a pilot study of panoramic radiograph analysis
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Purpose: The aim of this study was to evaluate the diagnostic and classification performance of a deep learning (DL) model for peri-implantitis–related bone defects using panoramic radiographs, focusing on defect morphology and severity. Methods: A dataset comprising 1,075 panoramic radiographs from 426 patients with periimplantitis was analyzed. A total of 2,250 implant sites were annotated and categorized based on defect morphology (intraosseous [class I], supracrestal/horizontal [class II], or combined [class III]) and severity (slight, moderate, or severe). The ensemble-based YOLOv8 DL model was trained on 80% of the dataset, with the remaining 20% reserved for testing. Performance was assessed using classification metrics, including accuracy, precision, recall, and F1 score. The diagnostic accuracy of the DL model was also compared with that of 2 board-certified periodontal surgeons. Results: The DL model achieved an overall accuracy of 85.33%, significantly outperforming the periodontal surgeons, who exhibited a mean accuracy of 75.6%. The DL model performed especially well for slight class II defects, with precision and recall values of 100% and 98%, respectively. In contrast, the periodontal surgeons demonstrated higher accuracy in severe cases, particularly for class II defects. Conclusions: DL enables reliable and accurate detection of peri-implantitis bone defects. It outperformed periodontal surgeons in overall accuracy, demonstrating its potential as a valuable second-opinion tool to support clinical decision-making. Future research should focus on expanding datasets and incorporating multimodal imaging.
Abstract
Purpose: The aim of this study was to evaluate the diagnostic and classification performance of a deep learning (DL) model for peri-implantitis–related bone defects using panoramic radiographs, focusing on defect morphology and severity. Methods: A dataset comprising 1,075 panoramic radiographs from 426 patients with periimplantitis was analyzed. A total of 2,250 implant sites were annotated and categorized based on defect morphology (intraosseous [class I], supracrestal/horizontal [class II], or combined [class III]) and severity (slight, moderate, or severe). The ensemble-based YOLOv8 DL model was trained on 80% of the dataset, with the remaining 20% reserved for testing. Performance was assessed using classification metrics, including accuracy, precision, recall, and F1 score. The diagnostic accuracy of the DL model was also compared with that of 2 board-certified periodontal surgeons. Results: The DL model achieved an overall accuracy of 85.33%, significantly outperforming the periodontal surgeons, who exhibited a mean accuracy of 75.6%. The DL model performed especially well for slight class II defects, with precision and recall values of 100% and 98%, respectively. In contrast, the periodontal surgeons demonstrated higher accuracy in severe cases, particularly for class II defects. Conclusions: DL enables reliable and accurate detection of peri-implantitis bone defects. It outperformed periodontal surgeons in overall accuracy, demonstrating its potential as a valuable second-opinion tool to support clinical decision-making. Future research should focus on expanding datasets and incorporating multimodal imaging.