Evidence map›Paper›PMID 35280303›Full record

ReviewFrontiers in neurology2022

Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview.

Anurag Marasini, Alisha Shrestha, Subash Phuyal, Osama O Zaidat, Junaid Siddiq Kalia

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 10% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. 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

5 authors at 3 institutions in 2 countries.

Anurag MarasiniAINeuroCare Academy, Dallas, TX, United States.
Alisha ShresthaTravel and Mountain Medicine Center, Kathmandu, Nepal.
Subash PhuyalDepartment of Neurointerventional Radiology, Upendra Devkota Memorial National Institute of Neurological and Allied Sciences, Kathmandu, Nepal.
Osama O ZaidatDepartments of Endovascular Neurosurgery and Stroke, St. Vincent Mercy Medical Center, Toledo, OH, United States.
Junaid Siddiq KaliaAINeuroCare Academy, Dallas, TX, United States.
Annapurna Neurological Institute and Allied Sciences · NPMercy St. Vincent Medical Center · USMountain Medicine Society of Nepal · NP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intracranial aneurysms (IAs) are a significant public health concern. In populations without comorbidity and a mean age of 50 years, their prevalence is up to 3.2%. An efficient method for identifying subjects at high risk of an IA is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. Artificial intelligence (AI) has received worldwide attention for its impressive performance in image-based tasks. It can serve as an adjunct to physicians in clinical settings, improving diagnostic accuracy while reducing physicians' workload. AI can perform tasks such as pattern recognition, object identification, and problem resolution with human-like intelligence. Based on the data collected for training, AI can assist in decisions in a semi-autonomous manner. Similarly, AI can identify a likely diagnosis and also, select a suitable treatment based on health records or imaging data without any explicit programming (instruction set). Aneurysm rupture prediction is the holy grail of prediction modeling. AI can significantly improve rupture prediction, saving lives and limbs in the process. Nowadays, deep learning (DL) has shown significant potential in accurately detecting lesions on medical imaging and has reached, or perhaps surpassed, an expert-level of diagnosis. This is the first step to accurately diagnose UIAs with increased computational radiomicis. This will not only allow diagnosis but also suggest a treatment course. In the future, we will see an increasing role of AI in both the diagnosis and management of IAs.

Indexed as

aneurysm ruptureartificial intelligencedeep learningmachine learningunruptured intracranial aneurysms (UIAs)

Identifiers

PMID35280303
PMCPMC8904392
OpenAlexW4214494098

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.