Author:
Park Ji-Hyun,Moon Hong Seok,Jung Hoi-In,Hwang JaeJoon,Choi Yoon-Ho,Kim Jong-Eun
Abstract
AbstractThis study investigated two artificial intelligence (AI) methods for automatically classifying dental implant diameter and length based on periapical radiographs. The first method, deep learning (DL), involved utilizing the pre-trained VGG16 model and adjusting the fine-tuning degree to analyze image data obtained from periapical radiographs. The second method, clustering analysis, was accomplished by analyzing the implant-specific feature vector derived from three key points 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-ROC). 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-ROC. Two AI models showed reliable classification performances. For clinical applications, AI models require validation on various multicenter data.
Funder
Ministry of Health & Welfare, Republic of Korea
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Howe, M. S., Keys, W. & Richards, D. Long-term (10-year) dental implant survival: A systematic review and sensitivity meta-analysis. J. Dent. 84, 9–21 (2019).
2. Albrektsson, T. & Donos, N. Implant survival and complications. The Third EAO consensus conference 2012. Clin. Oral Implants Res. 23(Suppl 6), 63–65 (2012).
3. Jung, R. E., Zembic, A., Pjetursson, B. E., Zwahlen, M. & Thoma, D. S. Systematic review of the survival rate and the incidence of biological, technical, and aesthetic complications of single crowns on implants reported in longitudinal studies with a mean follow-up of 5 years. Clin. Oral Implants Res. 23(Suppl 6), 2–21 (2012).
4. Papaspyridakos, P., Chen, C. J., Singh, M., Weber, H. P. & Gallucci, G. O. Success criteria in implant dentistry: A systematic review. J. Dent. Res. 91, 242–248 (2012).
5. Berglundh, T. et al. Peri-implant diseases and conditions: Consensus report of workgroup 4 of the 2017 world workshop on the classification of periodontal and peri-implant diseases and conditions. J. Periodontol. 89, S286–S291 (2018).