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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Explainable CNN-Radiomics Fusion and Ensemble Learning for Multimodal Lesion Classification in Dental Radiographs.

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  • 1Computer Engineering Department, Engineering and Architecture Faculty, Eskisehir Osmangazi University, Eskisehir 26040, Türkiye.

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Summary
This summary is machine-generated.

This study introduces an AI framework that fuses deep learning and radiomics for improved root-end disease detection in dental radiographs, achieving high accuracy and transparency.

Keywords:
CNN-radiomic fusionSHAPdeep learningdental radiographsdimensionality reductionexplainabilityperiapical lesion detectionradiomicstest-time augmentation

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Imaging Analysis
  • Radiomics and Deep Learning Fusion

Background:

  • Periapical radiographs are crucial for diagnosing root-end disease.
  • Interpretation errors in radiographs limit diagnostic accuracy.
  • Need for improved, reliable diagnostic tools in endodontics.

Purpose of the Study:

  • To develop an explainable, multimodal AI framework for enhanced root-end disease detection.
  • To fuse deep Convolutional Neural Network (CNN) embeddings with radiomic texture descriptors.
  • To provide dual-layer explainability using Grad-CAM and SHAP values.

Main Methods:

  • Utilized a dataset of 2285 periapical radiographs processed by six CNN architectures.
  • Extracted radiomic features from lesion-relevant pixels identified by Grad-CAM heatmaps.
  • Fused CNN embeddings and radiomic features, feeding them into Random Forest or XGBoost classifiers.
  • Employed five-view test-time augmentation (TTA) for enhanced inference.

Main Results:

  • Raw CNNs achieved ~52% accuracy and AUC ~0.60.
  • Multimodal fusion significantly improved performance, reaching 95.4% accuracy and 0.9867 AUC.
  • The top ensemble model with TTA achieved 97.16% accuracy and 0.9914 AUC.
  • Grad-CAM highlighted periapical regions, and SHAP values confirmed joint feature contributions.

Conclusions:

  • The proposed AI framework integrates CNN embeddings and mask-targeted radiomics for state-of-the-art lesion detection.
  • Dual-layer explainability (Grad-CAM + SHAP) enhances transparency and trust in AI predictions.
  • This multimodal approach addresses limitations in diagnostic accuracy and interpretability for root-end disease.