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X-ray Imaging

X-ray Imaging

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 X-rays, and by 1900, X-ray was widely...

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A Lightweight and Explainable AI Framework Toward Automated Infraocclusion Detection in Pediatric Panoramic

Zeliha Hatipoglu Palaz¹, Ecem Elif Cege², Bamoye Maiga³

¹Department of Pediatric Dentistry, Faculty of Dentistry, Gazi University, 06490 Ankara, Turkey.

Diagnostics (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

A new deep learning framework accurately detects infraocclusion in pediatric dental X-rays. This AI tool is efficient and interpretable, aiding early diagnosis and treatment planning.

Keywords:

artificial intelligence classification deep learning detection infraocclusion panoramic radiographs

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Area of Science:

Artificial Intelligence in Dentistry
Medical Imaging Analysis
Pediatric Dentistry

Background:

Infraocclusion in children can lead to malocclusion and require complex orthodontic treatment.
Early detection is crucial but challenging due to human error in conventional diagnosis.
Automated detection systems are needed for pediatric panoramic radiographs.

Purpose of the Study:

To design and evaluate a lightweight, two-stage deep learning framework for automated infraocclusion detection.
To integrate explainable AI (XAI) techniques for enhanced interpretability.
To assess the diagnostic accuracy and computational efficiency of the proposed system.

Main Methods:

A two-stage deep learning model using MobileNet V2 Lite for region detection and a custom CNN for classification.
Training and validation on annotated pediatric panoramic radiographs (ages 7-11).
Incorporation of XAI techniques to visualize model attention and clinical relevance.

Main Results:

The detection stage achieved high reliability (precision, recall, F1-score, AP50=0.99).
The classification stage demonstrated high accuracy (98.78% overall, 99.25% for infraocclusion).
The framework is computationally efficient (1.88M parameters, 7.19 MB) with low latency and XAI confirmed clinical relevance.

Conclusions:

The proposed framework offers an accurate, efficient, and interpretable solution for infraocclusion detection in pediatric patients.
Its modular design facilitates integration into clinical workflows, even in resource-limited settings.
Lightweight AI with XAI can improve early infraocclusion detection while ensuring clinical transparency.