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Artificial intelligence models using F-wave responses predict amyotrophic lateral sclerosis.

Jennifer M Martinez-Thompson1, Kevin A Mazurek1, Carolina Parra Cantu2

  • 1Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA.

Brain : a Journal of Neurology
|January 17, 2025
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Summary
This summary is machine-generated.

Artificial intelligence (AI) analyzes nerve conduction F-wave studies to improve amyotrophic lateral sclerosis (ALS) diagnosis. AI models accurately differentiate ALS from mimic conditions and predict patient survival, aiding clinical decisions.

Keywords:
amyotrophic lateral sclerosisartificial intelligenceelectrodiagnosticmachine learningpredictionsurvival

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • F-wave studies are crucial for detecting subclinical motor dysfunction in amyotrophic lateral sclerosis (ALS).
  • F-wave waveform variability presents interpretation challenges for diagnosing ALS.
  • Artificial Intelligence (AI) offers potential for extracting complex features from F-wave data.

Purpose of the Study:

  • To develop and validate an AI model for enhanced ALS diagnosis using F-wave analysis.
  • To assess the AI model's ability to differentiate ALS from mimic conditions.
  • To investigate AI-driven insights into ALS prognosis and survival factors.

Main Methods:

  • Retrospective analysis of 46,802 F-wave studies.
  • Application of discrete wavelet transforms to extract time-frequency features from waveforms.
  • Training a Gradient Boosting Machine model using wavelet features, patient demographics, and clinical data.
  • Validation on ALS patients and age/sex-matched controls, with exploratory analysis on mimic conditions (IBM, radiculopathy, neuropathy).

Main Results:

  • The AI model achieved 90% recall, 87% precision, and 88% accuracy in ALS classification.
  • Model performance was consistent using features from the full waveform, M-Wave, or F-Wave.
  • AI-derived probabilities significantly differentiated ALS from mimic diagnoses (p<0.001).
  • Older age at onset, family history, and higher ALS probability decreased survival; longer diagnostic delay and upper limb onset increased survival.

Conclusions:

  • AI effectively extracts informative features from F-wave responses for ALS diagnosis and prognosis.
  • The AI model provides valuable probabilities to aid clinicians in differentiating ALS from mimic conditions.
  • Integrating AI into clinical workflows can lead to earlier ALS diagnosis and improved patient management based on survival predictions.