Evidence map›Paper›PMID 36299975›Full record

ReviewCureus2022

Artificial Intelligence-Aided Headache Classification Based on a Set of Questionnaires: A Short Review.

Bob Daripa, Scott Lucchese

Abstract readReview
In one paragraph

Review in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Bob DaripaInternal Medicine/Neurology, Singapore General Hospital, Singapore, SGP.
Scott LuccheseNeurology, Headache, University of Arkansas for Medical Sciences, Little Rock, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wielding modern technology in the form of artificial intelligence (AI) or deep learning (DL) can utilize the best possible latest computer application in intricate decision-making and enigmatic problem-solving. It has been recommended in many fields. However, it is a long way from achieving an ambitious genuine intention when it comes to understanding and identifying any headache condition or classification, and using it error-free. No studies hitherto formalized any headache AI models to accurately classify headaches.  A machine's job can be arduous when incorporating an emotional dimension in decision making, re-challenging its own diagnosis by keeping a differential at all times, where even experienced neurologists or headache experts sometimes find it demanding to make a precise analysis and formulate a methodical plan. This could be because of spanning clinical presentation at a given moment of time or a change in clinical pattern over time which apparently could be due to intercrossing multiple pathophysiologies. We did a short literature review on the role of artificial intelligence and machine learning in headache classification. This brings forth a minuscule insight into the vastness of headaches and the perpetual effort and exploration headache may demand from AI when trying to scrutinize its classification. Undoubtedly, AI or DL could better be utilized in identifying the red flags of headache, as it might help our patients at home or the primary care physicians/practicing doctors/non- neurologists in their clinic to triage the headache patients if they need an imperative higher center referral to a neurologist for advanced evaluation. This outlook can limit the burden on a handful of headache specialists by minimizing the referrals to a tertiary care setting.

Indexed as

applicationsartificial intelligencedeep learningheadache classificationmachine learning

Identifiers

PMID36299975
PMCPMC9588408

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.