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Related Experiment Video

Updated: Sep 22, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Machine learning and acute stroke imaging.

Sunil A Sheth¹, Luca Giancardo², Marco Colasurdo^3,4

¹Department of Neurology, UTHealth McGovern Medical School, Houston, Texas, USA Sunil.A.Sheth@uth.tmc.edu.

Journal of Neurointerventional Surgery

|May 25, 2022

Summary

This summary is machine-generated.

Machine learning (ML) shows great promise in acute stroke imaging for tasks like detecting large vessel occlusion and infarct core. Understanding and evaluating ML algorithms is crucial for neurointerventional clinicians as integration into care grows.

Keywords:

Intervention Stroke

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Area of Science:

Neuroimaging
Artificial Intelligence
Stroke Imaging

Background:

Machine learning (ML) is increasingly successful in automated neuroimaging analysis.
Clinicians need to understand ML approaches, interpret results, and assess algorithm performance.

Purpose of the Study:

Provide an overview of ML in acute stroke imaging.
Discuss methods for evaluating ML algorithms.
Assess current ML approaches in the field.

Main Methods:

Review common ML techniques in medical imaging.
Search literature for AI/ML applications in acute ischemic stroke and mechanical thrombectomy.
Include studies with image-level data and sound ML approaches.

Main Results:

Numerous ML studies exist for acute stroke imaging: large vessel occlusion detection, hemorrhage quantification, infarct core detection.
Imaging modalities include CT and MRI.
Performance varies across studies and applications.

Conclusions:

ML has significantly advanced acute ischemic stroke imaging.
Further applications and clinical integration are expected.
Proficiency in ML is essential for neurointerventional clinicians.