Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) shows high accuracy in detecting and classifying amyotrophic lateral sclerosis (ALS), a rare neurological disease. Further research is needed to address the quality of evidence for AI applications in ALS diagnosis.
Area Of Science
- Neurology
- Medical Informatics
- Artificial Intelligence
Background
- Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive motor neuron loss.
- Current diagnosis of ALS is challenging due to the lack of specific biomarkers or tests.
- Artificial intelligence (AI) offers potential for improved ALS investigation and early detection.
Purpose Of The Study
- To systematically review and meta-analyze the diagnostic performance of AI models for early detection and screening of ALS.
- To evaluate the sensitivity and specificity of AI in classifying ALS based on various input data.
Main Methods
- A comprehensive literature search was conducted across seven databases.
- Meta-analysis using random-effects summary receiver operating characteristic (sROC) curves.
- Risk of bias assessment performed using QUADAS-2 or QUADAS-C tools.
Main Results
- 34 studies were analyzed, with a meta-prevalence of 47% for ALS.
- AI models demonstrated high pooled sensitivity (94.3%) and specificity (98.9%) for ALS detection.
- For ALS classification, pooled sensitivity was 90.9% and specificity was 92.3%, with variations based on input data (gait, EMG, MRI).
Conclusions
- AI demonstrates significant potential in the screening and diagnosis of ALS, exhibiting high sensitivity and specificity.
- Despite promising results, concerns persist regarding the overall quality of evidence in published literature.
- Further high-quality research is warranted to validate AI's role in clinical ALS diagnostics.
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