Affiliation:
1. Department of Prosthodontics, Yonsei University College of Dentistry
2. Department of Preventive Dentistry & Public Oral Health, Yonsei University College of Dentistry
3. Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Dental Research Institute
4. School of Computer Science and Engineering, Pusan National University
Abstract
Abstract
This study investigated two artificial intelligence methods for automatically classifying dental implant size based on periapical radiographs. The first method, deep learning (DL), involved utilizing the pretrained VGG16 model and adjusting the fine-tuning degree to analyze image data obtained from periapical radiographs. The second method, cluster analysis, was accomplished by analyzing the implant-specific feature vector derived from three key-point coordinates of the dental implant using the k-means + + algorithm and adjusting the weight of the feature vector. DL and clustering model classified dental implant size into nine groups. The performance metrics of AI models were accuracy, sensitivity, specificity, F1-score, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC). The final DL model yielded performances above 0.994, 0.950, 0.994, 0.974, 0.952, 0.994, and 0.975, respectively, and the final clustering model yielded performances above 0.983, 0.900, 0.988, 0.923, 0.909, 0.988, and 0.947, respectively. When comparing the AI model before tuning and the final AI model, statistically significant performance improvements were observed in six out of nine groups for DL models and four out of nine groups for clustering models based on AUC. For clinical applications, AI models require validation on various multicenter data.
Publisher
Research Square Platform LLC
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