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Functional Classification of Joints

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Sacroiliitis diagnosis based on interpretable features and multi-task learning.

Lei Liu¹, Haoyu Zhang², Weifeng Zhang²

¹Medical College, Shantou University, Shantou, Guangdong, 515041, People's Republic of China.

Physics in Medicine and Biology

|January 18, 2024

Summary

A new radiomics and deep learning algorithm accurately diagnoses sacroiliitis from CT scans. This method visualizes grading features, improving interpretability and diagnostic accuracy for early ankylosing spondylitis detection.

Keywords:

FFT multi-task learning radiomics sacroiliitis

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Sacroiliitis is an early indicator of ankylosing spondylitis (AS).
Accurate imaging diagnosis of sacroiliitis is crucial for early AS detection.
Current deep learning models require extensive labeled data and lack feature visualization.

Purpose of the Study:

To propose a radiomics and deep learning algorithm for diagnosing sacroiliitis on CT scans.
To enable visualization of grading features for enhanced clinical interpretability.
To improve the accuracy and reduce inter-observer variability in sacroiliitis diagnosis.

Main Methods:

Segmentation of sacroiliac joint (SIJ) 3D CT images using U-net and statistical methods.
Extraction of radiomics features combined with spatial and frequency domain features.
Application of multi-task learning with five-class labels for diagnosis.

Main Results:

Achieved an accuracy rate of 87.3% on a private dataset.
Demonstrated a 9.8% improvement in accuracy compared to the baseline.
Results are consistent with assessments by qualified medical professionals.

Conclusions:

The proposed radiomics and deep learning method offers an interpretable and portable solution for automatic sacroiliitis diagnosis.
Feature visualization enhances clinical understanding and aids in diagnosis and treatment planning.
The algorithm shows significant potential for improving early detection of ankylosing spondylitis.