3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke

Abstract

Background The aim of this study was to investigate the feasibility of ischemic stroke detection from computed tomography angiography source images (CTA-SI) using three-dimensional convolutional neural networks. Methods CTA-SI of 60 patients with a suspected acute ischemic stroke of the middle cerebral artery were randomly selected for this study; 30 patients were used in the neural network training, and the subsequent testing was performed using the remaining 30 patients. The training and testing were based on manually segmented lesions. Cerebral hemispheric comparison CTA and non-contrast computed tomography (NCCT) were studied as additional input features. Results All ischemic lesions in the testing data were correctly lateralized, and a high correspondence to manual segmentations was achieved. Patients with a diagnosed stroke had clinically relevant regions labeled infarcted with a 0.93 sensitivity and 0.82 specificity. The highest achieved voxel-wise area under receiver operating characteristic curve was 0.93, and the highest Dice similarity coefficient was 0.61. When cerebral hemispheric comparison was used as an input feature, the algorithm performance improved. Only a slight effect was seen when NCCT was included. Conclusion The results support the hypothesis that an acute ischemic stroke lesion can be detected with 3D convolutional neural network-based software from CTA-SI. Utilizing information from the contralateral hemisphere appears to be beneficial for reducing false positive findings.

Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review

“…The 10 studies reviewed that use RFL show that the AI often out-performs single radiologist ASPECTS and is non-inferior or even better than consensus ASPECTS. The reported sensitivity of AI algorithm ASPECTS range from 45% to 98%, mean 68%, and specificity ranges from 57% to 95%, mean 81% 9 10 14 16 17 22–27. Use of a convolutional neural network (CNN) for a combined asymmetric middle cerebral artery territory hypodensity and dense vessel detection may have higher performance; however, only area under the curve (AUC) metrics are reported (receiver operating characteristic AUC 92–96%) 28.…”

“…Many AI algorithms for different types of LVO characterization have been described. These include detection of vessel density asymmetry on CTA, presence of middle cerebral artery dot sign, bioimpedance asymmetry from phase shift spectroscopy, and true intravascular thrombus versus chronic atherosclerotic plaque 14 30–33. These LVO detection studies variably report algorithm performance compared with individual humans, with broad sensitivities of 67–98% and AUC ranging from 85% to 93%.…”

“…Of the studies reviewed for AI-mediated LVO detection, four clearly describe use of CNNs, and this ML type is the most common AI used. These studies using a CNN to directly detect LVOs from CTA images, report a sensitivity range from 67% to 94%, mean 85%, and specificity from 52% to 94%, mean 91% 12 14 29…”

“…The reported reasons for failure of the algorithms are often similar. These include radiologic scan abnormalities from pre-existing central nervous system injury, inadequate contrast boluses, inability to correct for patient motion, and tortuous vessels that preclude evaluation of the contrast column 12 14 24. Despite this, some versions of AI have good predictive power to increase standardized accurate stroke diagnosis and LVO triage.…”

“…ML has been well described in ASPECTS and perfusion to diffusion mismatch,2 9–11 but there are no published studies on ML use in the diagnosis of ischemic stroke by directly identifying LVOs from source images. Although abstracts describing this have been published,12 13 one retrospective study applying AI to vessel densities as an indirect measure of LVO presence,14 and a single retrospective study using AI analysis of patient demographics and clinical examination characteristics to predict LVO,15 to our knowledge there are no studies describing AI for LVO detection from source CT or MR image files.…”