ArticleTherapeutic advances in chronic disease2022
Computerized migraine diagnostic tools: a systematic review.
Article in Therapeutic advances in chronic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction models for treatment response in migraine: a systematic review and meta-analysis.The journal of headache and pain · 2025Pooled it
- Validation studies on migraine diagnostic tools for use in nonclinical settings: a systematic review.Arquivos de neuro-psiquiatria · 2023Pooled it
- Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi-center study.Headache · 2022Trial
- [Development and validation of Cefalytics: an artificial intelligence-based application for the rapid diagnosis of primary headaches].Atencion primaria · 2026Article
- Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated sorting genetic algorithm.BMC medical informatics and decision making · 2026Article
- Trends in self-reported headache disorder in Spain between 2006 and 2023: the role of sociodemographic factors.BMC public health · 2026Article
- Co-production and validation of an online resource to support the diagnosis of migraine.BMC neurology · 2025Article
- Review
- Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management.Current pain and headache reports · 2024Review
- Preliminary External Validation Results of the Artificial Intelligence-Based Headache Diagnostic Model: A Multicenter Prospective Observational Study.Life (Basel, Switzerland) · 2024Article
- Developing an Artificial Intelligence-Based Pediatric and Adolescent Migraine Diagnostic Model.Cureus · 2023Article
- The First Case Series From Japan of Primary Headache Patients Treated by Completely Online Telemedicine.Cureus · 2022Article
- Artificial Intelligence-Aided Headache Classification Based on a Set of Questionnaires: A Short Review.Cureus · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundComputerized migraine diagnostic tools have been developed and validated since 1960. We conducted a systematic review to summarize and critically appraise the quality of all published studies involving computerized migraine diagnostic tools.
methodsWe performed a systematic literature search using PubMed, Web of Science, Scopus, snowballing, and citation searching. Cutoff date for search was 1 June 2021. Published articles in English that evaluated a computerized/automated migraine diagnostic tool were included. The following summarized each study: publication year, digital tool name, development basis, sample size, sensitivity, specificity, reference diagnosis, strength, and limitations. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was applied to evaluate the quality of included studies in terms of risk of bias and concern of applicability.
resultsA total of 41 studies (median sample size: 288 participants, median age = 43 years; 77% women) were included. Most (60%) tools were developed based on International Classification of Headache Disorders criteria, half were self-administered, and 82% were evaluated using face-to-face interviews as reference diagnosis. Some of the automated algorithms and machine learning programs involved case-based reasoning, deep learning, classifier ensemble, ant-colony, artificial immune, random forest, white and black box combinations, and hybrid fuzzy expert systems. The median diagnostic accuracy was concordance = 89% [interquartile range (IQR) = 76-93%; range = 45-100%], sensitivity = 87% (IQR = 80-95%; range = 14-100%), and specificity = 90% (IQR = 77-96%; range = 65-100%). Lack of random patient sampling was observed in 95% of studies. Case-control designs were avoided in all studies. Most (76%) reference tests exhibited low risk of bias and low concern of applicability. Patient flow and timing showed low risk of bias in 83%.
conclusionDifferent computerized and automated migraine diagnostic tools are available with varying accuracies. Random patient sampling, head-to-head comparison among tools, and generalizability to other headache diagnoses may improve their utility.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.