Hierarchical Multi-Label Classification Model for CBCT-Based Extraction Socket Healing Assessment and Stratified Diagnostic Decision-Making to Assist Implant Treatment Planning.
Authors
Affiliations (5)
Affiliations (5)
- The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, Guangdong, China.
- Faculty of Engineering, Architecture and Information Technology University of Queensland QLD, Australia.
- Department of Oral Implantology, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou 510182, Guangdong, China.
- School of Intelligent Vehicles, Guangzhou Panyu Polytechnic, Guangzhou 511483, China; Department of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand. Electronic address: [email protected].
- The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, Guangdong, China; Department of Oral Implantology, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou 510182, Guangdong, China. Electronic address: [email protected].
Abstract
Dental implant treatment planning requires assessing extraction socket healing, yet current methods face challenges distinguishing soft tissue from woven bone on cone beam computed tomography (CBCT) imaging and lack standardized classification systems. In this study, we propose a hierarchical multilabel classification model for CBCT-based extraction socket healing assessment. We established a novel classification system dividing extraction socket healing status into two levels: Level 1 distinguishes physiological healing (Type I) from pathological healing (Type II); Level 2 is further subdivided into 5 subtypes. The HierTransFuse-Net architecture integrates ResNet50 with a two-dimensional transformer module for hierarchical multilabel classification. Additionally, a stratified diagnostic principle coupled with random forest algorithms supported personalized implant treatment planning. The HierTransFuse-Net model performed excellently in classifying extraction socket healing, achieving an mAccuracy of 0.9705, with mPrecision, mRecall, and mF1 scores of 0.9156, 0.9376, and 0.9253, respectively. The HierTransFuse-Net model demonstrated superior diagnostic reliability (κω = 0.9234) significantly exceeding that of clinical practitioners (mean κω = 0.7148, range: 0.6449-0.7843). The random forest model based on stratified diagnostic decision indicators achieved an accuracy of 81.48% and an mF1 score of 82.55% in predicting 12 clinical treatment pathways. This study successfully developed HierTransFuse-Net, which demonstrated excellent performance in distinguishing different extraction socket healing statuses and subtypes. Random forest algorithms based on stratified diagnostic indicators have shown potential for clinical pathway prediction. The hierarchical multilabel classification system simulates clinical diagnostic reasoning, enabling precise disease stratification and providing a scientific basis for personalized treatment decisions.