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

Topic: Brain proteins, microglia and lab models

Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival). Artificial intelligence techniques extracted important information from F-wave responses to estimate a patient's likelihood of ALS and their survival risks. Although the model can make predictions at a specific decision threshold as presented here, the true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS in comparison to other mimicking diagnoses, such as inclusion body myositis, cervical or lumbar radiculopathy or peripheral neuropathy. These probabilities provide clinicians with additional information that they can use to make the final diagnosis with greater confidence and precision. Integrating such a model into the clinical workflow could help clinicians to diagnose ALS sooner and manage treatment based on estimated survival, which might improve outcomes and the quality of life of patients.

Key findings

• Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival).
• Artificial intelligence techniques extracted important information from F-wave responses to estimate a patient's likelihood of ALS and their survival risks.
• Although the model can make predictions at a specific decision threshold as presented here, the true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS in comparison to other mimicking diagnoses, such as inclusion body myositis, cervical or lumbar radiculopathy or peripheral neuropathy.
• These probabilities provide clinicians with additional information that they can use to make the final diagnosis with greater confidence and precision.
• Integrating such a model into the clinical workflow could help clinicians to diagnose ALS sooner and manage treatment based on estimated survival, which might improve outcomes and the quality of life of patients.

Why it matters

An AI model using F-wave responses estimated the likelihood of ALS and survival risk, with model scores two standard deviations below the mean linked to 4 months increased survival and two standard deviations above linked to 3 months decreased survival. This concerns ALS, not Alzheimer's disease, and offers no treatment, cure, or prevention for dementia.

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Study details

• Participants802
• Evidence strengthLow

Read the original

• DOI: 10.1093/brain/awaf014
• PubMed: 39820267
• Original source →
• Free full text: Brain →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{martinezthompson2025-39820267,
  author  = {Martinez‐Thompson, Jennifer M. and Mazurek, Kevin A. and Cantu, Carolina Parra and Naddaf, Elie and Gogineni, Venkatsampath and Botha, Hugo and Jones, David Thomas and Laughlin, Ruple S. and others},
  title   = {{Artificial intelligence models using F-wave responses predict amyotrophic lateral sclerosis}},
  journal = {Brain},
  year    = {2025},
  doi     = {10.1093/brain/awaf014},
  pmid    = {39820267},
  url     = {https://doi.org/10.1093/brain/awaf014}
}

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