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Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic

Makoto Murata¹, Yoshiko Ariji², Yasufumi Ohashi¹

¹Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.

|December 13, 2018

Summary

A deep-learning system demonstrated high diagnostic performance for maxillary sinusitis on panoramic radiography, comparable to experienced radiologists. This AI tool shows promise in supporting dental professionals in diagnosis.

Keywords:

Artificial intelligence Computed tomography Deep learning Maxillary sinusitis Panoramic radiography

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Maxillary sinusitis diagnosis on panoramic radiography can be challenging.
Accurate and efficient diagnostic tools are needed.

Purpose of the Study:

To develop and evaluate a deep-learning system for diagnosing maxillary sinusitis using panoramic radiographs.
To assess the diagnostic performance of this AI system.

Main Methods:

Data augmentation created 6000 samples per category (healthy/inflamed sinuses).
A deep-learning model was trained for 200 epochs.
The model's diagnostic performance was evaluated on new image patches and compared to human experts.

Main Results:

The deep-learning system achieved high accuracy (87.5%), sensitivity (86.7%), specificity (88.3%), and an AUC of 0.875.
Performance was comparable to experienced radiologists and superior to dental residents.

Conclusions:

The deep-learning system exhibits high diagnostic capability for maxillary sinusitis on panoramic radiographs.
This AI tool can serve as a valuable diagnostic aid, particularly for less experienced dentists.