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Differentiation of Early Sacroiliitis Using Machine-Learning- Supported Texture Analysis.

Qingqing Zhu¹, Qi Wang¹, Xi Hu¹

¹Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.

Diagnostics (Basel, Switzerland)

|January 25, 2025

Summary

This summary is machine-generated.

Texture analysis (TA) models using MRI show promise for early diagnosis of sacroiliac joint arthritis. The T1WI-TA model significantly improved diagnostic accuracy compared to visual assessment, aiding in distinguishing early and established disease.

Keywords:

magnetic resonance imaging sacroiliitis texture analysis

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Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Early diagnosis of non-radiographic axial spondyloarthritis (nr-axSpA) is crucial for timely intervention.
Magnetic Resonance Imaging (MRI) is a key modality for detecting sacroiliitis.
Texture analysis (TA) offers a quantitative approach to image assessment.

Purpose of the Study:

To compare the diagnostic performance of TA against visual qualitative assessment for early sacroiliitis (nr-axSpA).
To evaluate the efficacy of T1-weighted (T1WI) and fluid-sensitive, fat-saturated (Fs) T2-weighted (T2WI) MRI sequences using TA models.
To differentiate between healthy controls, nr-axSpA, and radiographic axSpA (r-axSpA) patients.

Main Methods:

Retrospective analysis of 92 participants (30 controls, 32 nr-axSpA, 30 r-axSpA) with 3T MRI of sacroiliac joints.
Qualitative scoring by two independent readers.
Development and evaluation of T1WI-TA and FsT2WI-TA models.
Comparison of TA models and qualitative scores against a clinical reference standard.

Main Results:

Both TA models and qualitative scores significantly distinguished between groups (p < 0.05).
TA models showed superior performance compared to qualitative scores in differentiating healthy controls from nr-axSpA (p < 0.05).
The T1WI-TA model outperformed qualitative scores in distinguishing nr-axSpA from r-axSpA (p < 0.05).

Conclusions:

MRI-based T1WI-TA and FsT2WI-TA models are effective for early sacroiliac joint arthritis diagnosis.
The T1WI-TA model enhances early diagnostic efficacy compared to qualitative assessment.
The FsT2WI-TA model's efficacy is comparable to qualitative reader scores.