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Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
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The diagnostic performance of AI-based algorithms to discriminate between NMOSD and MS using MRI features: A
Masoud Etemadifar1, Mahdi Norouzi1, Seyyed-Ali Alaei1
1School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Background:
Magnetic resonance imaging [MRI] findings in Neuromyelitis optica spectrum disorder [NMOSD] and Multiple Sclerosis [MS] patients could lead us to discriminate toward them. For instance, U-fiber and Dawson's finger-type lesions are suggestive of MS, however linear ependymal lesions raise the possibility of NMOSD. Recently, artificial intelligence [AI] models have been used to discriminate between NMOSD and MS based on MRI features. In this study, we aim to systematically review the capability of AI algorithms in NMOSD and MS discrimination based on MRI features.
Method:
We searched PubMed, Scopus, Web of Sciences, Embase, and IEEE databases up to August 2023. All studies that used AI-based algorithms to discriminate between NMOSD and MS using MRI features were included, without any restriction in time, region, race, and age. Data on NMOSD and MS patients, Aquaporin-4 antibodies [AQP4-Ab] status, diagnosis criteria, performance metrics (accuracy, sensitivity, specificity, and AUC), artificial intelligence paradigm, MR imaging, and used features were extracted. This study is registered with PROSPERO, CRD42023465265.
Results:
Fifteen studies were included in this systematic review, with sample sizes ranging between 53 and 351. 1,362 MS patients and 1,118 NMOSD patients were included in our systematic review. AQP4-Ab was positive in 94.9% of NMOSD patients in 9 studies. Eight studies used machine learning [ML] as a classifier, while 7 used deep learning [DL]. AI models based on only MRI or MRI and clinical features yielded a pooled accuracy of 82% (95% CI: 78-86%), sensitivity of 83% (95% CI: 79-88%), and specificity of 80% (95% CI: 75-86%). In subgroup analysis, using only MRI features yielded an accuracy, sensitivity, and specificity of 83% (95% CI: 78-88%), 81% (95% CI: 76-87%), and 84% (95% CI: 79-89%), respectively.
Conclusion:
AI models based on MRI features showed a high potential to discriminate between NMOSD and MS. However, heterogeneity in MR imaging, model evaluation, and reporting performance metrics, among other confounders, affected the reliability of our results. Well-designed studies on multicentric datasets, standardized imaging and evaluation protocols, and detailed transparent reporting of results are needed to reach optimal performance.
Insights
Artificial intelligence (AI) models show promise in distinguishing Neuromyelitis Optica Spectrum Disorder (NMOSD) from Multiple Sclerosis (MS) using MRI scans. Further research with standardized protocols is needed to optimize diagnostic accuracy.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Neurology
Background:
- Differentiating Neuromyelitis Optica Spectrum Disorder (NMOSD) from Multiple Sclerosis (MS) is crucial for effective treatment, as their MRI features can overlap.
- Distinct MRI lesion patterns, such as U-fiber and Dawson's finger-type lesions (MS) versus linear ependymal lesions (NMOSD), aid in diagnosis.
- Artificial intelligence (AI) is increasingly utilized to analyze complex MRI data for improved diagnostic discrimination.
Approach:
- A systematic review was conducted by searching major scientific databases up to August 2023.
- Included studies employed AI algorithms to differentiate NMOSD and MS using MRI features, irrespective of time, region, race, or age.
- Data extraction focused on patient demographics, Aquaporin-4 antibody status, diagnostic criteria, AI methods (machine learning/deep learning), MRI details, and performance metrics.
Key Points:
- Fifteen studies involving 1,362 MS and 1,118 NMOSD patients were analyzed.
- AI models utilizing MRI and clinical features achieved a pooled accuracy of 82%, sensitivity of 83%, and specificity of 80%.
- Models using only MRI features demonstrated high performance with 83% accuracy, 81% sensitivity, and 84% specificity.
Conclusions:
- AI models leveraging MRI features demonstrate significant potential for discriminating between NMOSD and MS.
- Heterogeneity in imaging protocols, model evaluation, and reporting metrics currently impacts the reliability of AI-driven discrimination.
- Future research should prioritize multicentric datasets, standardized protocols, and transparent reporting to enhance the performance and reliability of AI diagnostic tools.
