Scholarlink

Background <p>To develop and validate a deep-learning detection model (Mask R-CNN) and a complementary radiomics-based machine-learning analysis for peri-implantitis detection on panoramic radiographs.</p> Methods <p>Panoramic radiographs from 144 patients (mean age 57.2 ± 11.7 years) were retrospectively collected. The peri-implantitis regions surrounding the implants of 144 patients were semi-automatically segmented by two dentomaxillofacial radiology residents. A total of 7,045 radiomic features peri-implant were extracted; 6 key features were selected via variance thresholding, SelectKBest, and LASSO regression. A Mask R-CNN (ResNet-50 backbone) was trained (80% train, 20% validation) with data augmentation. Diagnostic performance was assessed by FROC analysis and compared against six machine-learning classifiers.</p> Results <p>The Mask R-CNN achieved an F1-score of 0.84 (95% CI 0.80–0.88) and AUC of 0.86 (95% CI 0.82–0.90) on the validation set. The best radiomics-based classifier (XGBoost) reached an F1-score of 0.84. Inter-observer ICC for segmentation was 0.97.</p> Conclusions <p>Radiomics-enhanced deep learning can reliably detect peri-implantitis on panoramic radiographs. Prospective multicenter validation is warranted before clinical deployment.</p> Trial registration <p>Not applicable.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Radiomics analysis of panoramic radiographs using machine learning for the detection of peri-implantitis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月6日

查看全文
  • Serhat Efeoglu,
  • Emre Karahan,
  • S. Tugce Gokdeniz,
  • Burak Incebeyaz,
  • Fehmi Gonuldas,
  • Secil Aksoy,
  • Kaan Orhan

摘要

Background

To develop and validate a deep-learning detection model (Mask R-CNN) and a complementary radiomics-based machine-learning analysis for peri-implantitis detection on panoramic radiographs.

Methods

Panoramic radiographs from 144 patients (mean age 57.2 ± 11.7 years) were retrospectively collected. The peri-implantitis regions surrounding the implants of 144 patients were semi-automatically segmented by two dentomaxillofacial radiology residents. A total of 7,045 radiomic features peri-implant were extracted; 6 key features were selected via variance thresholding, SelectKBest, and LASSO regression. A Mask R-CNN (ResNet-50 backbone) was trained (80% train, 20% validation) with data augmentation. Diagnostic performance was assessed by FROC analysis and compared against six machine-learning classifiers.

Results

The Mask R-CNN achieved an F1-score of 0.84 (95% CI 0.80–0.88) and AUC of 0.86 (95% CI 0.82–0.90) on the validation set. The best radiomics-based classifier (XGBoost) reached an F1-score of 0.84. Inter-observer ICC for segmentation was 0.97.

Conclusions

Radiomics-enhanced deep learning can reliably detect peri-implantitis on panoramic radiographs. Prospective multicenter validation is warranted before clinical deployment.

Trial registration

Not applicable.