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Anatomically Based Multitask Deep Learning Radiomics Nomogram Predicts the Implant Failure Risk in Sinus Floor

Yujie Zhu1,2,3, Yang Liu1,2,3,4, Yue Zhao4

  • 1Stomatological Hospital of Chongqing Medical University, Chongqing, China.

Clinical Oral Implants Research
|July 24, 2025
PubMed
Summary

This study introduces an AI system for predicting dental implant failure before sinus floor elevation surgery. The anatomically based multitask deep learning radiomics nomogram (AMDRN) system accurately segments key structures and predicts failure risk, aiding personalized treatment.

Keywords:
artificial intelligencecone‐beam computed tomographydeep learningdental implantearly diagnosissinus floor augmentation

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Area of Science:

  • Biomedical Engineering
  • Radiology
  • Artificial Intelligence

Background:

  • Maxillary sinus floor elevation (MSFE) is crucial for dental implant placement.
  • Predicting implant failure risk preoperatively is essential for successful outcomes.
  • Current methods lack precision in identifying risk factors associated with MSFE.

Purpose of the Study:

  • To develop and evaluate an anatomically based multitask deep learning radiomics nomogram (AMDRN) system.
  • To predict the risk of implant failure before MSFE.
  • To incorporate automated segmentation of critical anatomical structures for enhanced prediction.

Main Methods:

  • Retrospective collection of cone beam computed tomography (CBCT) images and electronic medical records (EMRs).
  • Utilized nn-UNet v2 for automated segmentation of maxillary sinus, Schneiderian membrane, and residual alveolar bone.
  • Developed 3D-Attention-ResNet and radiomics models for feature extraction, integrated with clinical data into the AMDRN model.

Main Results:

  • High segmentation accuracy achieved for maxillary sinus (99.50%), residual alveolar bone (92.53%), and Schneiderian membrane (91.58%).
  • The AMDRN model demonstrated superior prediction accuracy (90%) and AUC (93%) compared to individual models.
  • Clinical, Radiomics, and 3D-DL models showed prediction accuracies of 60%, 76%, and 82%, respectively.

Conclusions:

  • The AMDRN system provides efficient and accurate preoperative prediction of implant failure risk in MSFE.
  • Automated segmentation of critical anatomical structures enhances predictive capabilities.
  • The system supports personalized treatment planning and improved clinical risk management for MSFE procedures.