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Automatic detection of image sharpening in maxillofacial radiology.

Lazar Kats¹, Yuli Goldman², Adrian Kahn³

¹Department of Oral Pathology, Oral Medicine and Maxillofacial Imaging, School of Dental Medicine, Tel Aviv University, 69978, Tel Aviv, Israel. lazarkat@tauex.tau.ac.il.

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Summary

This summary is machine-generated.

This study demonstrates a high-performance neural network for automatically detecting image sharpening artifacts in radiological scans. This technology can improve diagnostic accuracy and patient treatment by identifying unwanted image modifications.

Keywords:

Automatic sharpening detection Maxillofacial radiology Neural network Sharpening Sharpening artifacts Sharpening detection

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Area of Science:

Radiology
Artificial Intelligence
Image Processing

Background:

Image quality enhancement in radiology is crucial for accurate diagnosis.
Sharpening filters improve image perception but can introduce artifacts leading to misdiagnosis.
Automatic detection of these artifacts is needed to prevent improper treatment.

Purpose of the Study:

To evaluate the feasibility and effectiveness of a neural network for automatic sharpening detection in radiological images.
To develop a reliable method for identifying sharpening artifacts in medical imaging.

Main Methods:

A dataset of 4290 cone beam computed tomography (CBCT) X-ray slices was created and modified with sharpening filters.
The ResNet-50 neural network, pre-trained on ImageNet, was trained using Keras with a Tensorflow backend.
Receiver Operating Characteristic (ROC) analysis was performed to assess detection accuracy.

Main Results:

The neural network achieved high accuracy in detecting sharpening artifacts, with accuracy rates of 88.67% and 89% for moderate and high sharpening levels.
Sensitivity reached 93.33% and 93%, with specificity at 84% and 85.33% for these levels.
ROC analysis confirmed the model's effectiveness with Area Under Curve (AUC) values significantly different from 0.5.

Conclusions:

The study confirms the high performance of neural network technology for automatic sharpening detection in radiological images.
Further research into applying this technology to various radiological image types can significantly enhance diagnostic levels.
This approach promises to improve the accuracy of diagnosis and the appropriateness of patient treatment.