PMID- 35096362
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231105
IS  - 2040-6223 (Print)
IS  - 2040-6231 (Electronic)
IS  - 2040-6223 (Linking)
VI  - 13
DP  - 2022
TI  - Computerized migraine diagnostic tools: a systematic review.
PG  - 20406223211065235
LID - 10.1177/20406223211065235 [doi]
LID - 20406223211065235
AB  - BACKGROUND: Computerized 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. METHODS: We 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. RESULTS: A 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%. CONCLUSION: Different 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.
CI  - (c) The Author(s), 2022.
FAU - Woldeamanuel, Yohannes W
AU  - Woldeamanuel YW
AUID- ORCID: 0000-0003-4879-6098
AD  - Division of Headache & Facial Pain, Department of Neurology & Neurological 
      Sciences, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 
      94305, USA.
FAU - Cowan, Robert P
AU  - Cowan RP
AD  - Division of Headache & Facial Pain, Department of Neurology & Neurological 
      Sciences, Stanford University School of Medicine, Stanford, CA, USA.
LA  - eng
GR  - K01 NS124911/NS/NINDS NIH HHS/United States
GR  - R01 CA239714/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20220124
PL  - United States
TA  - Ther Adv Chronic Dis
JT  - Therapeutic advances in chronic disease
JID - 101532140
PMC - PMC8793115
OTO - NOTNLM
OT  - automated migraine diagnosis
OT  - computerized migraine diagnosis
OT  - digital health
OT  - migraine
OT  - systematic review
COIS- Conflict of interest statement: The authors declared no potential conflicts of 
      interest with respect to the research, authorship, and/or publication of this 
      article.
EDAT- 2022/02/01 06:00
MHDA- 2022/02/01 06:01
PMCR- 2022/01/24
CRDT- 2022/01/31 05:58
PHST- 2021/09/14 00:00 [received]
PHST- 2021/11/18 00:00 [accepted]
PHST- 2022/01/31 05:58 [entrez]
PHST- 2022/02/01 06:00 [pubmed]
PHST- 2022/02/01 06:01 [medline]
PHST- 2022/01/24 00:00 [pmc-release]
AID - 10.1177_20406223211065235 [pii]
AID - 10.1177/20406223211065235 [doi]
PST - epublish
SO  - Ther Adv Chronic Dis. 2022 Jan 24;13:20406223211065235. doi: 
      10.1177/20406223211065235. eCollection 2022.