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Related Experiment Video

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Computerized migraine diagnostic tools: a systematic review.

Yohannes W Woldeamanuel1, Robert P Cowan2

  • 1Division of Headache & Facial Pain, Department of Neurology & Neurological Sciences, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.

Therapeutic Advances in Chronic Disease
|January 31, 2022
PubMed
Summary
This summary is machine-generated.

Computerized migraine diagnostic tools show varying accuracy, with median concordance at 89%. Future research should focus on random sampling and head-to-head comparisons to enhance their clinical utility for migraine diagnosis.

Keywords:
automated migraine diagnosiscomputerized migraine diagnosisdigital healthmigrainesystematic review

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Area of Science:

  • Medical Informatics
  • Neurology
  • Artificial Intelligence in Healthcare

Background:

  • Computerized migraine diagnostic tools have been developed and validated since 1960.
  • A systematic review was conducted to summarize and appraise published studies on these tools.

Purpose of the Study:

  • To systematically review and critically appraise the quality of studies evaluating computerized migraine diagnostic tools.
  • To summarize the development, accuracy, and limitations of existing tools.

Main Methods:

  • Systematic literature search across multiple databases (PubMed, Web of Science, Scopus) up to June 1, 2021.
  • Inclusion of English articles evaluating computerized/automated migraine diagnostic tools.
  • Quality assessment using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool.

Main Results:

  • 41 studies included, with median participant age of 43 years (77% women).
  • Tools often based on International Classification of Headache Disorders criteria, with varying algorithms (e.g., machine learning, expert systems).
  • Median diagnostic accuracy: concordance 89%, sensitivity 87%, specificity 90%; however, 95% lacked random patient sampling.

Conclusions:

  • Diverse computerized migraine diagnostic tools exist with a range of accuracies.
  • Improvements in utility may come from random patient sampling, head-to-head comparisons, and broader generalizability.