To Develop a Machine Learning Model to Diagnose Acute Ischemic Stroke, Large Vessel Occlusion and Cerebral Collaterals (P8-4.015)

Dulari Gupta; Dhiraj Dhane; Sreehari Dinesh; shankar gorthi

doi:10.1212/wnl.0000000000216253

June 9, 2026Neurology

To Develop a Machine Learning Model to Diagnose Acute Ischemic Stroke, Large Vessel Occlusion and Cerebral Collaterals (P8-4.015)

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Key Points

The aim is to develop a machine learning model for diagnosing acute ischemic stroke and related conditions.
Developed an AI-based diagnostic model.
Utilized Non-Contrast CT and CT Angiography for detection.
The model demonstrated effective detection capabilities for acute ischemic stroke and large vessel occlusion.
Successful identification of cerebral collateral status in imaging data.

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Implication

AI-based model detects acute ischemic stroke, large vessel occlusion, and collateral status using imaging techniques.

Abstract

We aim to develop an AI-based model to detect Acute Ischemic Stroke, Large Vessel Occlusion and cerebral collateral status using Non-Contrast CT (NCCT) and CT Angiography (CTA).

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Cite This Study

Gupta et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf393faef96ed7f056042 https://doi.org/10.1212/wnl.0000000000216253

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To Develop a Machine Learning Model to Diagnose Acute Ischemic Stroke, Large Vessel Occlusion and Cerebral Collaterals (P8-4.015)

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

To Develop a Machine Learning Model to Diagnose Acute Ischemic Stroke, Large Vessel Occlusion and Cerebral Collaterals (P8-4.015)

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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