Deep learning and clustering approaches for dental implant size classification based on periapical radiographs.

ABSTRACT: This 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

SUBMITTER: Park JH 

PROVIDER: S-EPMC10558577 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature