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A Fully Automated Visual Grading System for White Matter Hyperintensities of T2-Fluid Attenuated Inversion Recovery
ZunHyan Rieu1, Regina Ey Kim1, Minho Lee1
1Research Institute, NEUROPHET Inc., 06234 Seoul, Republic of Korea.
Background:
The Fazekas scale is one of the most commonly used visual grading systems for white matter hyperintensity (WMH) for brain disorders like dementia from T2-fluid attenuated inversion recovery magnetic resonance (MR) images (T2-FLAIRs). However, the visual grading of the Fazekas scale suffers from low-intra and inter-rater reliability and high labor-intensive work. Therefore, we developed a fully automated visual grading system using quantifiable measurements.
Methods:
Our approach involves four stages: (1) the deep learning-based segmentation of ventricles and WMH lesions, (2) the categorization into periventricular white matter hyperintensity (PWMH) and deep white matter hyperintensity (DWMH), (3) the WMH diameter measurement, and (4) automated scoring, following the quantifiable method modified for Fazekas grading. We compared the performances of our method and that of the modified Fazekas scale graded by three neuroradiologists for 404 subjects with T2-FLAIR utilized from a clinical site in Korea.
Results:
The Krippendorff's alpha across our method and raters (A) versus those only between the radiologists (R) were comparable, showing substantial (0.694 vs. 0.732; 0.658 vs. 0.671) and moderate (0.579 vs. 0.586) level of agreements for the modified Fazekas, the DWMH, and the PWMH scales, respectively. Also, the average of areas under the receiver operating characteristic curve between the radiologists (0.80 ± 0.09) and the radiologists against our approach (0.80 ± 0.03) was comparable.
Conclusions:
Our fully automated visual grading system for WMH demonstrated comparable performance to the radiologists, which we believe has the potential to assist the radiologist in clinical findings with unbiased and consistent scoring.
Insights
This study presents an automated system for grading white matter hyperintensity (WMH) on brain MRIs, offering reliable and consistent scoring comparable to human experts.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Artificial Intelligence in Medicine
Background:
- The Fazekas scale is a standard for grading white matter hyperintensity (WMH) on T2-fluid attenuated inversion recovery (T2-FLAIR) MRI.
- Current visual grading of WMH has limitations in reliability and is labor-intensive.
- Automated, quantifiable methods are needed to improve WMH assessment.
Purpose of the Study:
- To develop and validate a fully automated visual grading system for WMH.
- To improve the reliability and efficiency of WMH scoring in brain imaging.
- To provide an unbiased tool for assisting radiologists in clinical assessments.
Main Methods:
- Developed a four-stage automated system: deep learning segmentation of WMH and ventricles, categorization into periventricular (PWMH) and deep (DWMH) WMH, WMH diameter measurement, and automated scoring.
- Quantifiable scoring method modified for Fazekas grading.
- Compared the automated system's performance against three neuroradiologists using T2-FLAIR images from 404 subjects.
Main Results:
- The automated system achieved comparable performance to neuroradiologists.
- Krippendorff's alpha indicated substantial and moderate agreement for the automated system and radiologists.
- Average areas under the receiver operating characteristic curve were similar between radiologists and the automated approach.
Conclusions:
- The fully automated WMH visual grading system shows performance comparable to human radiologists.
- This automated system has the potential to aid radiologists by providing unbiased and consistent clinical scoring.
- The developed system offers a reliable alternative for WMH assessment in clinical practice.
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