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  1. Home
  3. Methodological Challenges In Deep Learning-based Detection Of Intracranial Aneurysms: A Scoping Review.
  1. Home
  3. Methodological Challenges In Deep Learning-based Detection Of Intracranial Aneurysms: A Scoping Review.

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Methodological Challenges in Deep Learning-Based Detection of Intracranial Aneurysms: A Scoping Review.

Bio Joo1

  • 1Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

Neurointervention
|May 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Deep learning shows promise for detecting intracranial aneurysms on medical imaging. However, current studies have methodological limitations, hindering clinical use of artificial intelligence (AI) for aneurysm diagnosis.

Keywords:
Artificial intelligenceDeep learningIntracranial aneurysmMethodology

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Deep learning (DL) models demonstrate high diagnostic performance for intracranial aneurysm detection using computed tomography angiography (CTA) and magnetic resonance angiography (MRA).
  • Clinical translation of these AI technologies is limited by methodological weaknesses and generalizability concerns.

Purpose of the Study:

  • To conduct a scoping review of studies applying DL to intracranial aneurysm detection on CTA or MRA.
  • To evaluate study design, validation strategies, reporting practices, and reference standards in this field.

Main Methods:

  • Comprehensive review of 36 studies.
  • Analysis focused on study design, validation, reporting, and reference standards.
  • Evaluation of methodological limitations and clinical applicability.

Main Results:

  • Inconsistent handling of ruptured/treated aneurysms and underreporting of comorbidities were noted.
  • Limited external validation and prospective study designs were observed.
  • Few studies used representative cohorts or reported comprehensive performance metrics.

Conclusions:

  • Current DL studies for aneurysm detection are primarily in technical validation, posing risks of bias and limiting clinical applicability.
  • Future research requires rigorous designs, diverse validation cohorts, standardized reporting, and focus on human-AI interaction for real-world implementation.