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

Efeoğlu S., Karahan E., Gokdeniz S. T., Incebeyaz B., Gönüldaş F., Aksoy S., ...Daha Fazla

BMC ORAL HEALTH, cilt.1, sa.1, ss.1, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 1 Sayı: 1
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1186/s12903-026-08190-0
  • Dergi Adı: BMC ORAL HEALTH
  • Derginin Tarandığı İndeksler: Scopus, Science Citation Index Expanded (SCI-EXPANDED), CINAHL, MEDLINE, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1
  • Ankara Üniversitesi Adresli: Evet

Özet

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.