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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Effect of Deep Learning-Based Artificial Intelligence on Radiologists' Performance in Identifying Nigrosome 1

Jiyeon Park¹, Chae Young Lim¹, So Yeon Won¹

¹Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Korean Journal of Radiology

|July 30, 2025

Summary

Deep learning artificial intelligence (AI) software significantly improved radiologist performance in detecting nigrosome 1 abnormalities on susceptibility map-weighted imaging (SMwI), especially for less experienced readers, aiding Parkinson's disease diagnosis.

Keywords:

Artificial intelligence Magnetic resonance imaging Parkinson disease Substantia nigra

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Area of Science:

Radiology
Artificial Intelligence
Neuroimaging

Background:

Nigrosome 1 (N1) abnormalities on susceptibility map-weighted imaging (SMwI) are indicators for neurodegenerative diseases.
Deep learning (DL)-based artificial intelligence (AI) offers potential for enhancing diagnostic accuracy.

Purpose of the Study:

To assess the impact of DL-based AI software on the diagnostic performance of radiologists with varying experience levels.
To evaluate AI's effectiveness in detecting N1 abnormalities on SMwI.

Main Methods:

A retrospective case-control study analyzed 139 SMwI scans (59 Parkinson's disease patients, 80 controls) using 3T MRI.
An AI model (YOLOX and SparseInst) provided N1 abnormality assessments.
Four radiologists (two experienced, two residents) evaluated scans with and without AI assistance.

Main Results:

AI significantly improved diagnostic performance and specificity across most readers.
Inter-reader agreement improved with AI (Fleiss' kappa from 0.73 to 0.87).
Less experienced readers showed greater diagnostic improvement (NRI 12.8%) compared to experienced readers (NRI 0.8%).

Conclusions:

DL-based AI enhances diagnostic performance for N1 abnormalities on SMwI.
The technology particularly benefits less experienced radiologists, suggesting potential for improved Parkinson's disease diagnostic workflows.