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Deep learning-based AI model for sinusitis diagnosis.

Jingfei Zhang¹, Dianyi Wang¹, Wentao Li¹

¹The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|February 20, 2025

Summary

This summary is machine-generated.

An AI model achieved 85.8% accuracy in diagnosing chronic sinusitis from CT scans, surpassing human doctors. This deep learning approach offers a more accessible and accurate method for sinusitis diagnosis.

Keywords:

CT examination artificial intelligence assisted diagnostic model deep learning treatment of sinusitis

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Area of Science:

Medical Imaging
Artificial Intelligence
Otolaryngology

Background:

Computed Tomography (CT) is standard for chronic sinusitis diagnosis, but its accuracy is debated, and high costs limit routine use.
There is a significant need for improved, accessible diagnostic tools for sinusitis.

Purpose of the Study:

To develop an AI-assisted diagnostic model for sinusitis.
To enhance diagnostic accuracy and accessibility compared to conventional CT methods.

Main Methods:

A retrospective study analyzed 5000 sinus CT images from patients with chronic sinusitis and normal controls.
A deep learning model was trained on CT images, classifying sphenoid, frontal, ethmoid, and maxillary sinusitis.
Sigmoid and binary cross-entropy functions were employed for model training and accuracy assessment.

Main Results:

The AI model achieved an overall accuracy of 85.8% in diagnosing chronic sinusitis.
The model's performance exceeded that of doctors with varying experience levels (71.7% to 78.4%).
The AI demonstrated superior feature extraction and image resolution capabilities.

Conclusions:

AI-assisted diagnosis shows promise for improving chronic sinusitis detection.
The developed model offers a potentially more accurate and accessible alternative to traditional CT interpretation.
Further research can explore AI integration into routine clinical practice for sinusitis management.