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Validating the Utility of Super-Resolution for Downstream Tasks in Periapical Films
Junran Peng1, Meiyu Hu2, Qianli Zhang3
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China; Shunde Innovation School, University of Science and Technology Beijing, Foshan, China.
Deep learning-based super-resolution (SR) enhances degraded dental radiographs, significantly improving automated diagnostic accuracy for segmentation and reducing missed detections without extra radiation. This software solution offers cost-effective image standardization and precise clinical decision-making.
Area of Science:
- Artificial Intelligence in Dentistry
- Medical Image Analysis
- Deep Learning for Image Restoration
Background:
- Periapical radiography is crucial in dentistry but often suffers from image quality issues like motion artifacts and compression.
- These limitations compromise diagnostic yield and the accuracy of automated analysis tools.
- Enhancing image quality without increasing radiation exposure is a significant clinical need.
Purpose of the Study:
- To evaluate a deep-learning-based super-resolution (SR) framework for enhancing degraded periapical radiographs.
- To assess the impact of SR on automated diagnostic accuracy, focusing on semantic segmentation and object detection.
- To determine if SR can improve diagnostic performance without additional radiation exposure.
Main Methods:
- A clinical evaluation pipeline utilized 6283 periapical films.
- An AI-driven restoration model was trained on 5998 images and validated on 285 annotated images.
- Automated diagnostic performance (semantic segmentation and pathology/restoration detection) was compared between original low-resolution images and SR-enhanced reconstructions.
Main Results:
- SR-enhanced images significantly outperformed baseline images across all evaluated metrics.
- Mean Intersection over Union (IoU) for segmentation improved from 55.17% to 60.24% (P < .0001), with boundary precision increasing from 23.64% to 48.22% (P < .0001).
- SR increased detection recall by 4.24% (P = .0121), primarily by reducing missed detections, while mean Average Precision (mAP@50) showed a modest, nonsignificant increase.
Conclusions:
- Deep learning-driven SR effectively recovers diagnostic details from suboptimal periapical films, enhancing boundary sharpness and reducing missed detections.
- SR preprocessing improves the geometric precision and reliability of automated dental analyses.
- This software-based enhancement standardizes image quality, cost-effectively elevates diagnostic accuracy, and maximizes the utility of 2D radiographs for clinical decision-making.
