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Accuracy of Deep Learning Models in Detecting Mandibular Furcation Defects on Panoramic Radiographs.

Meric Kurumlu1, Fatma Karacaoglu1, Mürüvvet Kalkan2

  • 1Department of Periofontology, Faculty of Dentistry, Ankara University, 06560 Ankara, Turkey.

Diagnostics (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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This summary is machine-generated.

Artificial intelligence (AI) shows high accuracy in detecting furcation defects (FD) on panoramic radiographs of mandibular molars. AI algorithms like Xception and ENet offer promising results for improved diagnosis and treatment planning in periodontal disease.

Area of Science:

  • Dentistry
  • Radiology
  • Artificial Intelligence

Background:

  • Furcation defects present diagnostic and treatment challenges in periodontal disease.
  • Accurate identification of furcation involvement is crucial for effective treatment.
  • This study focuses on artificial intelligence for detecting furcation defects in mandibular molars.

Purpose of the Study:

  • To evaluate the accuracy and effectiveness of AI algorithms in detecting furcation defects (FD).
  • To assess AI performance in identifying FD in mandibular molars using panoramic radiographs.

Main Methods:

  • 654 panoramic radiographs of mandibular molars were analyzed.
  • Images were labeled as healthy or with furcation defects (FD) and preprocessed.
  • Deep learning algorithms (Xception, ENet) were employed for classification and segmentation.
Keywords:
artificial intelligencecomputer-assisted diagnosisdental radiographydiagnostic imagingperiodontal diseaseperiodontitis

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Main Results:

  • The Xception model achieved 97.9% accuracy, 97.10% precision, 97.08% sensitivity, and 97.09% F1 score for classification.
  • The ENet model achieved 99.96% accuracy, 99.26% precision, 97.57% sensitivity, and 98.41% F1 score for segmentation.
  • Both models demonstrated high performance in detecting furcation defects.

Conclusions:

  • AI systems show significant promise for detecting furcation involvement in mandibular molars on panoramic radiographs.
  • Further research with larger datasets and inclusion of maxillary molars may enhance detection success rates.