Kujawski, Sławomir, Tabisz, Hanna, Morten, Karl J et al. · Journal of translational medicine · 2025 · DOI
Researchers used artificial intelligence to analyze how the heart and nervous system behave differently in ME/CFS patients compared to healthy people. By measuring heart rate changes beat-by-beat, they found a pattern that could identify ME/CFS patients with 89% accuracy. The key differences included a less responsive vagal nerve (which normally calms the body), a more active stress response in blood vessels, and less efficient heart pumping.
This study provides the first objective, AI-based diagnostic tool with high accuracy for ME/CFS using non-invasive autonomic measurements, potentially enabling faster diagnosis and reducing diagnostic delays that patients typically experience. Understanding the specific autonomic dysfunction patterns may open new avenues for targeted treatment strategies. The findings validate long-standing clinical observations that autonomic dysfunction is central to ME/CFS pathology.
This study does not establish causation—it demonstrates that these autonomic patterns associate with ME/CFS but does not prove they cause the disease. The perfect AUC warrants caution regarding overfitting and requires external validation in independent cohorts before clinical implementation. The cross-sectional design cannot determine whether these autonomic changes precede disease onset or develop as a consequence of ME/CFS.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Kujawski, Sławomir, Tabisz, Hanna, Morten, Karl J, Modlińska, Aleksandra, Słomko, Joanna, & Zalewski, Paweł (2025). Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements.. Journal of translational medicine. https://doi.org/10.1186/s12967-025-07433-y
BibTeX
@article{mecfsatlas-kujawski-2025-diagnosis-chronic,
author = {Kujawski, Sławomir and Tabisz, Hanna and Morten, Karl J and Modlińska, Aleksandra and Słomko, Joanna and Zalewski, Paweł},
title = {Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements.},
journal = {Journal of translational medicine},
year = {2025},
doi = {10.1186/s12967-025-07433-y},
note = {PubMed: 41437251},
url = {https://www.mecfsatlas.com/evidence/kujawski-2025-diagnosis-chronic},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-26. https://www.mecfsatlas.com/evidence/kujawski-2025-diagnosis-chronic
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