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Core-Penumbra Hyperacute Ischemic Stroke Dataset.

D Umerenkov^1,2, S Kudin³, M Peksheva⁴

¹AIRI, Moscow, Russia. umerenkov@airi.net.

Scientific Data

|April 29, 2025

Summary

We present the Core-Penumbra hyperAcute Ischemic Stroke Dataset (CPAISD) for early ischemic stroke detection using Non-Contrast Computed Tomography (NCCT) scans. This dataset aids in developing machine learning models for rapid diagnosis in the critical hyperacute phase.

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Area of Science:

Neurology
Radiology
Medical Imaging

Background:

Hyperacute ischemic stroke presents subtle or absent findings on NCCT scans, delaying diagnosis.
Timely detection is crucial for effective intervention and improved patient outcomes.

Purpose of the Study:

Introduce the Core-Penumbra hyperAcute Ischemic Stroke Dataset (CPAISD).
Facilitate the development of machine learning models for early ischemic stroke detection using NCCT.
Enhance diagnostic capabilities during the critical hyperacute stroke phase.

Main Methods:

Collected and segmented NCCT scans from patients in the hyperacute ischemic stroke phase.
Annotated ischemic core and penumbra regions within the NCCT images.
Developed a baseline model to demonstrate dataset utility.

Main Results:

The CPAISD provides a valuable resource for training AI models.
The dataset enables the identification of subtle stroke indicators in NCCT scans.
A baseline model demonstrated the potential for rapid stroke assessment.

Conclusions:

The CPAISD is a significant resource for advancing early ischemic stroke detection.
Facilitates the development of AI-powered diagnostic tools for hyperacute stroke.
Aims to improve patient care and outcomes through enhanced stroke management.