Table 1.

Summary of some studies showing the application of AI for initial neuroimaging in AIS [Acute Ischemic Stroke] between 2000 and 2023.

Year	Authors	Research Question	Outcomes Measures/Conclusions
2023 [12]	Field N. et al.	Does supplying an LVO detection algorithm notification to the thrombectomy team’s cell phone improve ischemic stroke workflow?	Transfer time and Mechanical Thrombectomy [MT] Initiation time decreased.
2023 [13]	Zhaou X. et al.	Does CTA derived from CT Perfusion [CTA-DF-CTP] give better image quality and diagnostic accuracy than traditional CTA in AIS?	CTA derived from CTA-DF-CTP had diagnostic accuracy comparable to traditional CTA and CTA-DF-CTP.
2023 [14]	Xiang et al.	Is it feasible to apply computed tomography perfusion [CTP] imaging-guided mechanical thrombectomy in acute ischemic stroke patients with LVO beyond the therapeutic time window?	NIHSS of MT group-CTP guided [at 6 h, 24 h, 7 days, and 30 days] was significantly better [p < 0.05]; however, infarct core volume approximation was too high or too low for this group.
2023 [15]	Du B. et al.	In patients with ICAS [Intracranial Atherosclerotic Stenosis] in the anterior circulation, is AI based on CBF [Cerebral Blood Flow] or sCoV [Spatial Coefficient of Variation] better for predicting vascular cognitive impairment?	Cognitive impairment seems better predicted by AI analysis of sCoV than CBF.
2023 [16]	Farsani S. et al.	Can AG-DCNN [Attention Gated Deep Convoluted Neural Network] predict infarct volume and size?	AG-DCNN, using only admission DWI, predicted infarct volumes at 3–7 days after stroke onset with accuracy like models using DWI and PWI.
2022 [17]	Kossen T. et al.	How can modern machine learning methods such as generative adversarial networks [GANs] automate perfusion map generation from [DSC-MR] Dynamic Susceptibility Contrasted MR in AIS on an expert level without manual validation?	DSC-MR using machine learning can speed up patient stratification by perfusion mapping in AIS.
2022 [18]	Long Le et al.	Can an advanced deep learning-based method accurately and rapidly assess collateral perfusion in AIS by automatically generating a multiphase collateral imaging map from dynamic susceptibility contrast-enhanced MR perfusion [DSC-MRP] images?	DSC-Enhanced MR Perfusion improved accuracy and sped the assessment of the collateral perfusion.
2021 [19]	Neeves G et al.	Can a machine-learning [ML] algorithm grade digital subtraction angiograms [DSA] by the mTICI scale?	ML of complete cerebral DSA predicted mTICI scores following EVT of MCA occlusions.
2020 [20]	Grosser M. et al.	In AIS patients, how do predictions of machine learning models based on local [regional] tissue susceptibility to ischemia compare with those of machine learning models based on global brain imaging?	Compared to single global machine learning models, locally trained machine learning models can lead to better prediction of lesion outcomes in AIS patients.
2019 [21]	Satish R. et al.	Can Convolutional Neural Network analysis of Multisequence MRI in AIS predict the ischemic core and penumbra?	CNN analysis experimentally confirmed local changes.
2019 [22]	Reid M. et al.	For detecting early severe ischemia, how does NCCT compare with multiphase computed tomography angiography [mCTA] regional leptomeningeal score [mCTA-rLMC] and an mCTA venous [mCTA-venous] perfusion lesion?	An assessment blinded to clinical information in patients undergoing endovascular therapy [EVT] showed that mCTA-venous more accurately detected early ischemia and predicted clinical outcomes than NCCT and the mCTA-rLMC score.
2018 [23]	Nielsen A. et al.	In AIS, can Deep Learning improve Tissue Outcome and Treatment Effect predictions?	Deep Learning improves predictions of final neurological outcome and lesion volume.
2018 [24]	Chung-Ho. et al.	Can imaging features and advanced machine learning use the TSS [Time Since Stroke] classification to characterize the Acute Ischemic Stroke Onset Time?	Demonstrates the potential benefit of using advanced machine learning methods in TSS classification.
2017 [25]	Yu. Y. et al.	Can machine learning models trained on perfusion-weighted magnetic resonance imaging [PWI] and diffusion-weighted MRI scans predict HT [hemorrhagic transformation] occurrence and location in AIS?	HT prediction was a machine-learning problem. Specifically, the model learned to extract imaging markers of HT directly from source PWI images.
2016 [26]	Tian X. et al.	Can clinically acceptable PCT [dynamic cerebral Perfusion Computed Tomography] images be created from low-dose CT images restored with a coupled dictionary learning [CDL] method in chronic and AIS patients?	CDL increased kinetic enhanced details and improved diagnostic hemodynamic parameter maps
2013 [27]	Fang R. et al.	Will the robust sparse perfusion deconvolution method [SPD] accurately estimate cerebral blood flow [CBF] in CTP performed at a low radiation dose?	SPD was superior to existing methods for CBF and helped differentiate normal and ischemic brain tissue.
2010 [28]	Mendrick A. et al.	Can the diagnostic yield of CTP in cerebrovascular diseases be expanded by combining arterial and venous segmentation and vessel-enhanced volume?	This artery and vein segmentation method was accurate for arteries and veins with normal perfusion. Combining the artery and vein segmentation with the vessel-enhanced volume produced an arteriogram and venogram, extending the diagnostic yield of CTP scans and making a CTA scan unnecessary.
2007 [29].	Meyer-Baese A. et al.	Do five unsupervised clustering techniques help analyze dynamic susceptibility contrast MRI time series?	Clustering is a valuable tool for analyzing and visualizing brain regional perfusion properties.