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Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: a Systematic Review.

Karan Daga^1,2, Siddharth Agarwal¹, Zaeem Moti²

¹School of Biomedical Engineering & Imaging Sciences, King's College London, BMEIS, King's College London. 1 Lambeth Palace Road, UK SE1 7EU, London, UK.

Clinical Neuroradiology

|November 15, 2024

Summary

This summary is machine-generated.

Machine learning shows potential for predicting intracranial aneurysm rupture risk. However, current evidence doesn't prove it surpasses existing methods, requiring more validation for clinical use.

Keywords:

Aneurysm Cerebrovascular Intracranial Aneurysm Machine Learning Subarachnoid Haemorrhage

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Area of Science:

Neurosurgery
Medical Artificial Intelligence
Biostatistics

Background:

Subarachnoid hemorrhage, a life-threatening condition, results from intracranial aneurysm rupture.
Predicting aneurysm rupture is challenging, yet crucial for guiding prophylactic treatment decisions.
Identifying rupture-prone aneurysms is clinically significant due to treatment risks.

Purpose of the Study:

To systematically review and evaluate the performance of machine learning (ML) algorithms in predicting intracranial aneurysm rupture risk.
To assess the accuracy and clinical applicability of ML models for aneurysm rupture prediction.

Main Methods:

A systematic literature search was conducted across MEDLINE, Embase, Cochrane Library, and Web of Science up to December 2023.
Studies utilizing any ML algorithm for intracranial aneurysm rupture risk prediction were included.
Risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST).

Main Results:

Twenty studies, encompassing 20,286 aneurysm cases, met the eligibility criteria.
Machine learning models demonstrated performance accuracy ranging from 0.66 to 0.90.
Comparisons with clinical standards yielded mixed results, and most studies had high or unclear risks of bias, limiting generalizability.

Conclusions:

Machine learning is applicable for predicting intracranial aneurysm rupture risk.
Current evidence does not conclusively demonstrate ML superiority over existing clinical practices.
Further prospective, multicenter studies are necessary to validate recent ML tools for clinical implementation.