Deep learning derived automated ASPECTS on non‐contrast CT scans of acute ischemic stroke patients

Abstract

Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers.In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients.First, we proposed to use the CT perfusion (CTP) from one-stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings.Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of Zehong Cao, Jiaona Xu, and Bin Song contributed equally to this work.

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

“…DL methods are also being explored, with encouraging results [ 41 ]. Several studies have demonstrated AI ASPECTS scoring performance to be equal to that of experienced neuroradiologists [ 42 , 43 , 44 , 45 , 46 , 47 ]. Albers et al showed that RAPID ASPECTS was more accurate than experienced readers in identifying early ischemia when compared to the corresponding DWI results [ 48 ].…”

Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke

“…All images were processed using a research portal platform. 1 (1) NCCT images analysis: For patients with ischemic stroke, the Alberta stroke program early CT score (ASPECTS) could be used to evaluate the early changes in middle cerebral artery territory (MCAT) (Cao et al, 2022). Briefly, the brain was segmented into 20 ASPECTS regions.…”

“…Meanwhile, the mean CT intensity (unit: HU) was also collected for all regions. Note that the ASPECTS values were evaluated automatically based on our previous proposed deep learning algorithm (Cao et al, 2022). ( 2) CTP images analysis: The raw CTP images were calculated, and parameter maps were obtained.…”

Assessing variability in non-contrast CT for the evaluation of stroke: the effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume

“…3 To address this, artificial intelligence tools have been developed to assist with early stroke evaluation by automating determination of ASPECTS. [4][5][6] However, ASPECTS estimated using automated methods may be be sensitive to NCCT reconstruction method. 7 The purpose of this work was to perform initial investigations into the effects of CT reconstruction kernel and slice thickness on automated ASPECTS determination.…”