Neurointervention.  2023 Nov;18(3):149-158. 10.5469/neuroint.2023.00339.

Proposed Protocols for Artificial Intelligence Imaging Database in Acute Stroke Imaging

• Kim M
• Jung SC
• Kim SC
• Kim BJ
• Seo WK
• Kim B

• KMID: 2547276
• DOI: http://doi.org/10.5469/neuroint.2023.00339

Abstract

Purpose
To propose standardized and feasible imaging protocols for constructing artificial intelligence (AI) database in acute stroke by assessing the current practice at tertiary hospitals in South Korea and reviewing evolving AI models.
Materials and Methods
A nationwide survey on acute stroke imaging protocols was conducted using an electronic questionnaire sent to 43 registered tertiary hospitals between April and May 2021. Imaging protocols for endovascular thrombectomy (EVT) in the early and late time windows and during follow-up were assessed. Clinical applications of AI techniques in stroke imaging and required sequences for developing AI models were reviewed. Standardized and feasible imaging protocols for data curation in acute stroke were proposed.
Results
There was considerable heterogeneity in the imaging protocols for EVT candidates in the early and late time windows and posterior circulation stroke. Computed tomography (CT)-based protocols were adopted by 70% (30/43), and acquisition of noncontrast CT, CT angiography and CT perfusion in a single session was most commonly performed (47%, 14/30) with the preference of multiphase (70%, 21/30) over single phase CT angiography. More hospitals performed magnetic resonance imaging (MRI)-based protocols or additional MRI sequences in a late time window and posterior circulation stroke. Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) were most commonly performed MRI sequences with considerable variation in performing other MRI sequences. AI models for diagnostic purposes required noncontrast CT, CT angiography and DWI while FLAIR, dynamic susceptibility contrast perfusion, and T1-weighted imaging (T1WI) were additionally required for prognostic AI models.
Conclusion
Given considerable heterogeneity in acute stroke imaging protocols at tertiary hospitals in South Korea, standardized and feasible imaging protocols are required for constructing AI database in acute stroke. The essential sequences may be noncontrast CT, DWI, CT/MR angiography and CT/MR perfusion while FLAIR and T1WI may be additionally required.

Keyword

Cerebrovascular stroke; Acute stroke; Artificial intelligence

Figure

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  Fig. 1. Proposed protocols for AI imaging database in stroke. AI, artificial intelligence; NCCT, noncontrast computed tomography; CTA, CT angiography; CTP, CT perfusion; MRI, magnetic resonance imaging; EVT, endovascular thrombectomy; DWI, diffusion-weighted imaging; FLAIR, fluid-attenuated inversion recovery; T2WI, T2-weighted imaging; T1WI, T1-weighted imaging; TOF MRA, time of flight MR angiography; CE-MRA, contrast-enhanced MRA; DSC PWI, dynamic susceptibility contrast perfusion-weighted imaging; SWI, susceptibility-weighted imaging; GRE, gradient echo; VW-MRI, vessel wall MRI; ASL, arterial spin labeling; ASPECTS, Alberta Stroke Program Early CT Score; LVO, large vessel occlusion.

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