Journal of dentistry · 2025
Hierarchical multi-label classification model for cbct-based extraction socket healing assessment and stratified diagnostic decision-making to assist implant treatment planning.
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Li Qing, Han Ruitong, Huang Jieying, Liu Chang-Bo, Zhao Shiyong, Ge Linhu, Zheng Haoran, Huang Zelun
Аннотация
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
CLINICAL SIGNIFICANCE: The hierarchical multilabel classification system simulates clinical diagnostic reasoning, enabling precise disease stratification and providing a scientific basis for personalized treatment decisions.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
CLINICAL SIGNIFICANCE: The hierarchical multilabel classification system simulates clinical diagnostic reasoning, enabling precise disease stratification and providing a scientific basis for personalized treatment decisions.