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X-ray Imaging

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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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Related Experiment Video

Updated: Jun 14, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Transfer Learning-Based Classifier to Automate the Extraction of False X-Ray Images From Hospital's Database.

Manar Abu Talib¹, Mohammad Adel Moufti², Qassim Nasir³

¹Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates.

International Dental Journal

|September 5, 2024

Summary

This summary is machine-generated.

This study developed an artificial intelligence (AI) system to automatically distinguish between real patient dental radiographs and fake training images on acrylic blocks. The AI achieved high accuracy, improving data management in digital dental archiving.

Keywords:

Dental radiographs images Image processing Transfer learning VGG16

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Area of Science:

Dental Radiology
Artificial Intelligence in Medicine
Medical Image Analysis

Background:

Dental students use acrylic blocks with extracted teeth for preclinical radiography training.
Digitization of dental records led to mixed storage of training and patient radiographic images.
Manual filtering of these mixed images is inefficient and problematic.

Purpose of the Study:

To develop an automated method for differentiating intraoral radiographs from artificial training images.
To improve the accuracy and efficiency of digital dental image archiving.

Main Methods:

Utilized an artificial intelligence (AI) solution for automated image differentiation.
Applied the concept of transfer learning using a VGG16 pre-trained model.
Trained and tested the model on a dataset from a Dental Hospital.

Main Results:

The VGG16 model achieved 98.8% accuracy, 99.2% F1 score, and 100% recall.
These results significantly outperformed the initial baseline model (96.5% accuracy, 97.5% F1 score, 98.9% recall).
The AI system demonstrated high sensitivity in identifying artificial images.

Conclusions:

The transfer learning-based AI system effectively distinguishes between real intraoral radiographs and artificial training images.
The proposed system offers an accurate and automated solution for cleaning and managing digital dental image archives.