Automated ASPECT rating: comparison between the Frontier ASPECT Score software and the Brainomix software

Improving acute stroke assessment in non-enhanced computed tomography: automated tool for early ischemic lesion volume detection

BACKGROUND AND OBJECTIVES

ASPECTs is a widely used marker to identify early stroke signs on non-enhanced computed tomography (NECT), yet it presents interindividual variability and it may be hard to use for non-experts. We introduce an algorithm capable of automatically estimating the NECT volumetric extension of early acute ischemic changes in the 3D space. We compared the power of this marker with ASPECTs evaluated by experienced practitioner in predicting the clinical outcome.

METHODS

We analyzed and processed neuroimaging data of 153 patients admitted with acute ischemic stroke. All patients underwent a NECT at admission and on follow-up. The developed algorithm identifies the early ischemic hypodense region based on an automatic comparison of the gray level in the images of the two hemispheres, assumed to be an approximate mirror image of each other in healthy patients.

RESULTS

In the two standard axial slices used to estimate the ASPECTs, the regions identified by the algorithm overlap significantly with those identified by experienced practitioners. However, in many patients, the regions identified automatically extend significantly to other slices. In these cases, the volume marker provides supplementary and independent information. Indeed, the clinical outcome of patients with volume marker = 0 can be distinguished with higher statistical confidence than the outcome of patients with ASPECTs = 10.

CONCLUSION

The volumetric extension and the location of acute ischemic region in the 3D-space, automatically identified by our algorithm, provide data that are mostly in agreement with the ASPECTs value estimated by expert practitioners, and in some cases complementary and independent.

The Artificial Intelligence Revolution in Stroke Care: A Decade of Scientific Evidence in Review

BACKGROUND

The emergence of artificial intelligence (AI) has significantly influenced the diagnostic evaluation of stroke and has revolutionized acute stroke care delivery. The scientific evidence evaluating the role of AI, especially in areas of stroke treatment and rehabilitation is limited but continues to accumulate. We performed a systemic review of current scientific evidence evaluating the use of AI in stroke evaluation and care and examined the publication trends during the past decade.

METHODS

A systematic search of electronic databases was conducted to identify all studies published from 2012 to 2022 that incorporated AI in any aspect of stroke care. Studies not directly relevant to stroke care in the context of AI and duplicate studies were excluded. The level of evidence and publication trends were examined.

RESULTS

A total of 623 studies were examined, including 101 reviews (16.2%), 9 meta-analyses (1.4%), 140 original articles on AI methodology (22.5%), 2 case reports (0.3%), 2 case series (0.3%), 31 case-control studies (5%), 277 cohort studies (44.5%), 16 cross-sectional studies (2.6%), and 45 experimental studies (7.2%). The highest published area of AI in stroke was diagnosis (44.1%) and the lowest was rehabilitation (12%). A 10-year trend analysis revealed a significant increase in AI literature in stroke care.

CONCLUSIONS

Most research on AI is in the diagnostic area of stroke care, with a recent noteworthy trend of increased research focus on stroke treatment and rehabilitation.

Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis

Background

Artificial intelligence-derived software technologies have been developed that are intended to facilitate the review of computed tomography brain scans in patients with suspected stroke.

Objectives

To evaluate the clinical and cost-effectiveness of using artificial intelligence-derived software to support review of computed tomography brain scans in acute stroke in the National Health Service setting.

Methods

Twenty-five databases were searched to July 2021. The review process included measures to minimise error and bias. Results were summarised by research question, artificial intelligence-derived software technology and study type. The health economic analysis focused on the addition of artificial intelligence-derived software-assisted review of computed tomography angiography brain scans for guiding mechanical thrombectomy treatment decisions for people with an ischaemic stroke. The de novo model (developed in R Shiny, R Foundation for Statistical Computing, Vienna, Austria) consisted of a decision tree (short-term) and a state transition model (long-term) to calculate the mean expected costs and quality-adjusted life-years for people with ischaemic stroke and suspected large-vessel occlusion comparing artificial intelligence-derived software-assisted review to usual care.

Results

A total of 22 studies (30 publications) were included in the review; 18/22 studies concerned artificial intelligence-derived software for the interpretation of computed tomography angiography to detect large-vessel occlusion. No study evaluated an artificial intelligence-derived software technology used as specified in the inclusion criteria for this assessment. For artificial intelligence-derived software technology alone, sensitivity and specificity estimates for proximal anterior circulation large-vessel occlusion were 95.4% (95% confidence interval 92.7% to 97.1%) and 79.4% (95% confidence interval 75.8% to 82.6%) for Rapid (iSchemaView, Menlo Park, CA, USA) computed tomography angiography, 91.2% (95% confidence interval 77.0% to 97.0%) and 85.0 (95% confidence interval 64.0% to 94.8%) for Viz LVO (Viz.ai, Inc., San Fransisco, VA, USA) large-vessel occlusion, 83.8% (95% confidence interval 77.3% to 88.7%) and 95.7% (95% confidence interval 91.0% to 98.0%) for Brainomix (Brainomix Ltd, Oxford, UK) e-computed tomography angiography and 98.1% (95% confidence interval 94.5% to 99.3%) and 98.2% (95% confidence interval 95.5% to 99.3%) for Avicenna CINA (Avicenna AI, La Ciotat, France) large-vessel occlusion, based on one study each. These studies were not considered appropriate to inform cost-effectiveness modelling but formed the basis by which the accuracy of artificial intelligence plus human reader could be elicited by expert opinion. Probabilistic analyses based on the expert elicitation to inform the sensitivity of the diagnostic pathway indicated that the addition of artificial intelligence to detect large-vessel occlusion is potentially more effective (quality-adjusted life-year gain of 0.003), more costly (increased costs of £8.61) and cost-effective for willingness-to-pay thresholds of £3380 per quality-adjusted life-year and higher.

Limitations and conclusions

The available evidence is not suitable to determine the clinical effectiveness of using artificial intelligence-derived software to support the review of computed tomography brain scans in acute stroke. The economic analyses did not provide evidence to prefer the artificial intelligence-derived software strategy over current clinical practice. However, results indicated that if the addition of artificial intelligence-derived software-assisted review for guiding mechanical thrombectomy treatment decisions increased the sensitivity of the diagnostic pathway (i.e. reduced the proportion of undetected large-vessel occlusions), this may be considered cost-effective.

Future work

Large, preferably multicentre, studies are needed (for all artificial intelligence-derived software technologies) that evaluate these technologies as they would be implemented in clinical practice.

Study registration

This study is registered as PROSPERO CRD42021269609.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR133836) and is published in full in Health Technology Assessment; Vol. 28, No. 11. See the NIHR Funding and Awards website for further award information.

Performance evaluation of two different software programs for automated ASPECTS scoring in patients with suspected stroke

BACKGROUND

The range of software available to radiologists has increased enormously with the advancement of AI. A good example of this is software to determine ASPECTS in the treatment of potential stroke patients.

OBJECTIVE

In this study, two software packages (eASPECTS from Brainomix and VIA_ASPECTS from Siemens) were tested and compared for their performance in the daily clinical routine of a maximum care provider with a 24/7 stroke unit.

METHODS

A total of 637 noncontrast CT images were obtained from consecutive patients with suspected stroke, of whom 73 were finally diagnosed with MCA infarction. Differences in agreement and quantification of agreement were analysed, as well as the correlation and sensitivity, specificity and accuracy compared to raters.

RESULTS

Compared to VIA_ASPECTS, eASPECTS shows good agreement and strong correlation with the raters. VIA_ASPECTS has lower accuracy and low specificity than eASPECTS but a higher sensitivity.

CONCLUSION

Both software products have the potential to be decision support tools for radiologists. There are, however, differences between the two software products in terms of their intended use.

Real-world evaluation of Brainomix e-Stroke software

BACKGROUND AND PURPOSE

Brainomix e-Stroke is an artificial intelligence-based decision support tool that aids the interpretation of CT imaging in the context of acute stroke. While e-Stroke has the potential to improve the speed and accuracy of diagnosis, real-world validation is essential. The aim of this study was to prospectively evaluate the performance of Brainomix e-Stroke in an unselected cohort of patients with suspected acute ischaemic stroke.

METHODS

The study cohort included all patients admitted to the University College London Hospital Hyperacute Stroke Unit between October 2021 and April 2022. For e-ASPECTS and e-CTA, the ground truth was determined by a neuroradiologist with access to all clinical and imaging data. For e-CTP, the values of the core infarct and ischaemic penumbra were compared with those derived from syngo.via, an alternate software used at our institution.

RESULTS

1163 studies were performed in 551 patients admitted during the study period. Of these, 1130 (97.2%) were successfully processed by e-Stroke in an average of 4 min. For identifying acute middle cerebral artery territory ischaemia, e-ASPECTS had an accuracy of 77.0% and was more specific (83.5%) than sensitive (58.6%). The accuracy for identifying hyperdense thrombus was lower (69.1%), which was mainly due to many false positives (positive predictive value of 22.9%). Identification of acute haemorrhage was highly accurate (97.8%) with a sensitivity of 100% and a specificity of 97.6%; false positives were typically caused by areas of calcification. The accuracy of e-CTA for large vessel occlusions was 91.5%. The core infarct and ischaemic penumbra volumes provided by e-CTP strongly correlated with those provided by syngo.via (ρ=0.804-0.979).

CONCLUSION

Brainomix e-Stroke software provides rapid and reliable analysis of CT imaging in the acute stroke setting although, in line with the manufacturer's guidance, it should be used as an adjunct to expert interpretation rather than a standalone decision-making tool.

Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT

BACKGROUND

Automated measurement of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) can support clinical decision making. Based on a deep learning algorithm, we developed an automated ASPECTS scoring system (Heuron ASPECTS) and validated its performance in a prespecified clinical trial.

METHODS

For model training, we used non-contrast computed tomography images of 487 patients with acute ischemic stroke (AIS). For the clinical trial, 326 patients (87 with AIS, 56 with other acute brain diseases, and 183 with no brain disease) were enrolled. The results of Heuron ASPECTS were compared with the consensus generated by two stroke experts using the Bland-Altman agreement. A mean difference of less than 0.35 and a maximum allowed difference of less than 3.8 were considered the primary outcome target. The sensitivity and specificity of the model for the 10 regions of interest and dichotomized ASPECTS were calculated.

RESULTS

The Bland-Altman agreement had a mean difference of 0.03 [95% confidence interval (CI): -0.08 to 0.14], and the upper and lower limits of agreement were 2.80 [95% CI: 2.62 to 2.99] and -2.74 [95% CI: -2.92 to -2.55], respectively. For ASPECTS calculation, sensitivity and specificity to detect the early ischemic change for 10 ASPECTS regions were 62.78% [95% CI: 58.50 to 67.07] and 96.63% [95% CI: 96.18 to 97.09], respectively. Furthermore, in a dichotomized analysis (ASPECTS >4 vs. ≤4), the sensitivity and specificity were 94.01% [95% CI: 91.26 to 96.77] and 61.90% [95% CI: 47.22 to 76.59], respectively.

CONCLUSIONS

The current trial results show that Heuron ASPECTS reliably measures the ASPECTS for use in clinical practice.

Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach

Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt treatment to improve stroke outcome. Our previous study has shown a good performance in the identification of ischemic injury on NCCT. In the present study, we developed a deep learning (DL) localization model to help localize the early infarction sign on NCCT. This retrospective study included consecutive 517 ischemic stroke (IS) patients who received NCCT within 12 h after stroke onset. A total of 21,436 infarction patches and 20,391 non-infarction patches were extracted from the slice pool of 1,634 NCCT according to brain symmetricity property. The generated patches were fed into different pretrained convolutional neural network (CNN) models such as Visual Geometry Group 16 (VGG16), GoogleNet, Residual Networks 50 (ResNet50), Inception-ResNet-v2 (IR-v2), Inception-v3 and Inception-v4. The selected VGG16 model could detect the early infarction in both supratentorial and infratentorial regions to achieve an average area under curve (AUC) 0.73 after extensive customization. The properly tuned-VGG16 model could identify the early infarction in the cortical, subcortical and cortical plus subcortical areas of supratentorial region with the mean AUC > 0.70. Further, the model could attain 95.6% of accuracy on recognizing infarction lesion in 494 out of 517 IS patients.

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Introduction

Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms.

Methods

We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice.

Results

The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.

Conclusion

The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

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

There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.

Performance of Automated ASPECTS Software and Value as a Computer-Aided Detection Tool

BACKGROUND AND PURPOSE

ASPECTS quantifies early ischemic changes in anterior circulation stroke on NCCT but has interrater variability. We examined the agreement of conventional and automated ASPECTS and studied the value of computer-aided detection.

MATERIALS AND METHODS

We retrospectively collected imaging data from consecutive patients with acute ischemic stroke with large-vessel occlusion undergoing thrombectomy. Five raters scored conventional ASPECTS on baseline NCCTs, which were also processed by RAPID software. Conventional and automated ASPECTS were compared with a consensus criterion standard. We determined the agreement over the full ASPECTS range as well as dichotomized, reflecting thrombectomy eligibility according to the guidelines (ASPECTS 0-5 versus 6-10). Raters subsequently scored ASPECTS on the same NCCTs with assistance of the automated ASPECTS outputs, and agreement was obtained.

RESULTS

For the total of 175 cases, agreement among raters individually and the criterion standard varied from fair to good (weighted κ = between 0.38 and 0.76) and was moderate (weighted κ = 0.59) for the automated ASPECTS. The agreement of all raters individually versus the criterion standard improved with software assistance, as did the interrater agreement (overall Fleiss κ = 0.15-0.23; P < .001 and .39 to .55; P = .01 for the dichotomized ASPECTS).

CONCLUSIONS

Automated ASPECTS had agreement with the criterion standard similar to that of conventional ASPECTS. However, including automated ASPECTS during the evaluation of NCCT in acute stroke improved the agreement with the criterion standard and improved interrater agreement, which could, therefore, result in more uniform scoring in clinical practice.

Automatic Alberta Stroke Program Early Computed Tomographic Scoring in patients with acute ischemic stroke using diffusion-weighted imaging

Alberta Stroke Program Early Computed Tomographic Scoring (ASPECTS) is a reliable method for assessing early ischemic changes in the blood supply area of the middle cerebral artery in patients with acute ischemic stroke. This study aims to propose a deep learning based automatic evaluation strategy for DWI-ASPECTS to serve as a reference for clinicians in urgent decision making for endovascular thrombectomy. Ten ASPECTS regions are extracted from the DWI series to train the independent classification network for each region, the accurate training labels of which are confirmed by neuroradiologists. Two classical convolutional neural networks (VGG-16 and ResNet-50) are validated. Subsequently, the innovative CBAM-VGG is designed to improve the accurate scoring of four small-volume DWI-ASPECTS regions, including caudate nucleus, lenticular nucleus, internal capsule, and insular lobe. Average F1-score of 0.929 and 0.840 and the average accuracy of 94.75% and 84.99% are obtained when scoring on six cortical regions M1-M6 and four small ASPECTS regions, respectively. In addition, the modified algorithm CBAM-VGG shows a significant improvement in the accuracy of estimating the four ASPECTS regions with smaller volumes. The experimental results demonstrate that the deep learning methods facilitate the efficiency and robustness of automatic DWI-ASPECTS scoring, which can provide a reference for clinical decision-making.

The diagnostic performance of artificial intelligence algorithms for identifying M2 segment middle cerebral artery occlusions: A systematic review and meta-analysis

BACKGROUND

Artificial intelligence (AI)-based algorithms have been developed to facilitate rapid and accurate computed tomography angiography (CTA) assessment in proximal large vessel occlusion (LVO) acute ischemic stroke, including internal carotid artery and M1 occlusions. In clinical practice, however, the detection of medium vessel occlusion (MeVO) represents an ongoing diagnostic challenge in which the added value of AI remains unclear.

PURPOSE

To assess the diagnostic performance of AI platforms for detecting M2 occlusions.

METHODS

Studies that report the diagnostic performance of AI-based detection of M2 occlusions were screened, and sensitivity and specificity data were extracted using the semi-automated AutoLit software (Nested Knowledge, MN) platform. STATA (version 16 IC; Stata Corporation, College Station, Texas, USA) was used to conduct all analyses.

RESULTS

Eight studies with a low risk of bias and significant heterogeneity were included in the quantitative and qualitative synthesis. The pooled estimates of sensitivity and specificity of AI platforms for M2 occlusion detection were 64% (95% CI, 53 to 74%) and 97% (95% CI, 84 to 100%), respectively. The area under the curve (AUC) in the SROC curve was 0.79 (95% CI, 0.74 to 0.83).

CONCLUSION

The current performance of the AI-based algorithm makes it more suitable as an adjunctive confirmatory tool rather than as an independent one for M2 occlusions. With the rapid development of such algorithms, it is anticipated that newer generations will likely perform much better.

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.

Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study

OBJECTIVE

This study aimed to develop a deep learning (DL) model to improve the diagnostic performance of EIC and ASPECTS in acute ischemic stroke (AIS).

METHODS

Acute ischemic stroke patients were retrospectively enrolled from 5 hospitals. We proposed a deep learning model to simultaneously segment the infarct and estimate ASPECTS automatically using baseline CT. The model performance of segmentation and ASPECTS scoring was evaluated using dice similarity coefficient (DSC) and ROC, respectively. Four raters participated in the multi-reader and multicenter (MRMC) experiment to fulfill the region-based ASPECTS reading under the assistance of the model or not. At last, sensitivity, specificity, interpretation time and interrater agreement were used to evaluate the raters' reading performance.

RESULTS

In total, 1391 patients were enrolled for model development and 85 patients for external validation with onset to CT scanning time of 176.4 ± 93.6 min and NIHSS of 5 (IQR 2-10). The model achieved a DSC of 0.600 and 0.762 and an AUC of 0.876 (CI 0.846-0.907) and 0.729 (CI 0.679-0.779), in the internal and external validation set, respectively. The assistance of the DL model improved the raters' average sensitivities and specificities from 0.254 (CI 0.22-0.26) and 0.896 (CI 0.884-0.907), to 0.333 (CI 0.301-0.345) and 0.915 (CI 0.904-0.926), respectively. The average interpretation time of the raters was reduced from 219.0 to 175.7 s (p = 0.035). Meanwhile, the interrater agreement increased from 0.741 to 0.980.

CONCLUSIONS

With the assistance of our proposed DL model, radiologists got better performance in the detection of AIS lesions on NCCT.

External Validation of e-ASPECTS Software for Interpreting Brain CT in Stroke

OBJECTIVE

The purpose of this study was to test e-ASPECTS software in patients with stroke. Marketed as a decision-support tool, e-ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using Alberta Stroke Program Early CT Score (ASPECTS).

METHODS

Using CT from 9 stroke studies, we compared software with masked experts. As per indications for software use, we assessed e-ASPECTS results for patients with/without middle cerebral artery (MCA) ischemia but no other cause of stroke. In an analysis outside the intended use of the software, we enriched our dataset with non-MCA ischemia, hemorrhage, and mimics to simulate a representative "front door" hospital population. With final diagnosis as the reference standard, we tested the diagnostic accuracy of e-ASPECTS for identifying stroke features (ischemia, hyperattenuated arteries, and hemorrhage) in the representative population.

RESULTS

We included 4,100 patients (51% women, median age = 78 years, National Institutes of Health Stroke Scale [NIHSS] = 10, onset to scan = 2.5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3,035 CTs with expert-rated ASPECTS, most (2084/3035, 69%) e-ASPECTS results were within one point of experts. In the representative population, the diagnostic accuracy of e-ASPECTS was 71% (95% confidence interval [CI] = 70-72%) for detecting ischemic features, 85% (83-86%) for hemorrhage. Software identified more false positive ischemia (12% vs 2%) and hemorrhage (14% vs <1%) than experts.

INTERPRETATION

On independent testing, e-ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of artificial intelligence (AI) software on patient care and outcome are required before widespread implementation of stroke decision-support software. ANN NEUROL 2022;92:943-957.

The Utility of Automated ASPECTS in Acute Ischemic Stroke for Intravenous Recombinant Tissue Plasminogen Activator (IV-rtPA) Therapy

PURPOSE

This study aimed to investigate the accuracy and clinical significance of an artificial intelligence (AI)-based automated Alberta Stroke Program Early Computed Tomography (ASPECT) scoring software of head CT for the indication of intravenous recombinant tissue plasminogen activator (rt-PA) therapy.

METHODS

This study included two populations of acute ischemic stroke: one comprised patients who had undergone head CT within 48 h of presentation (Population #1, n = 448), while the other included patients within 4.5 h from onset (Population #2, n = 132). The primary endpoint was the concordance rate of ASPECTS of the neurologists and AI software against the benchmark score. The secondary endpoints were to validate the accuracy of the neurologist and AI software in assessing the ability to rule out extensive infarction (ASPECTS of 0-5) in population #2.

RESULTS

The reading accuracy of AI software was comparable to that of the board-certified vascular neurologists. The detection rate of cardiogenic cerebral embolism was better than that of atherothrombotic cerebral infarction. By excluding extensive infarction, AI-software showed a higher specificity and equivalent sensitivity compared to those of experts.

CONCLUSIONS

The AI software for ASPECTS showed convincing agreement with expert evaluation and would be supportive in determining the indications of intravenous rt-PA therapy.

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.

Automated ASPECTS in acute ischemic stroke: comparison of the overall scores and Hounsfield unit values of two software packages and radiologists with different levels of experience

BACKGROUND

ASPECTS is a simple, rapid, and semi-quantitative method for detecting early ischemic changes (EIC). However, the agreement between software applications and neuroradiologists varies greatly.

PURPOSE

To compare ASPECTS calculated by using automated software tools to neuroradiologists evaluation in patients with acute ischemic stroke (AIS).

MATERIAL AND METHODS

Retrospectively, 61 patients with large vessel occlusion (LVO) who underwent multimodal stroke computed tomography (CT) were evaluated using two automated ASPECTS software tools (NSK and RAPID) and three neuroradiologists with different experiences (two senior neuroradiologists and one junior neuroradiologist). Four weeks later, the same three neuroradiologists re-evaluated the ASPECTS in consensus using the baseline CT and follow-up non-contrast CT (NCCT). Interclass correlation coefficients (ICCs) and Pearson correlation coefficients were applied for statistical analysis.

RESULTS

The HU value exhibited the greatest correlation in the insular lobe (r = 0.81; P < 0.001) and the lowest correlation in the internal capsule (r = 0.65; P < 0.001) between NSK and RAPID. Software analysis and human readers showed excellent agreement with the consensus reading. Compared with the consensus reading, the correlation of the two senior radiologists (ICC = 0.975 and 0.969, respectively) were higher than that of junior radiologist (ICC = 0.869), and the consistency values of the NSK and RAPID software tools after 6 h of onset to imaging (ICC = 0.894 and 0.874, respectively) were greater than those within 6 h of onset (ICC = 0.746 and 0.828, respectively).

CONCLUSION

For patients experiencing AIS due to LVO, the ASPECTS calculated with automated software agrees well with the predefined consensus score but is inferior to that of senior radiologists.

Impact of Encephalomalacia and White Matter Hyperintensities on ASPECTS in Patients With Acute Ischemic Stroke: Comparison of Automated- and Radiologist-Derived Scores

Background: Automated software-based Alberta Stroke Program Early CT Score (ASPECTS) on unenhanced CT is associated with clinical outcomes after acute stroke. However, encephalomalacia or white matter hyperintensities (WMHs) may result in a falsely low automated ASPECTS if such findings are interpreted as early ischemia. Objective: To assess the impact of encephalomalacia and WMH on automated ASPECTS in patients with acute stroke, in comparison with radiologist-derived ASPECTS and clinical outcomes. Methods: This retrospective three-center study included 459 patients (322 men, 137 women; median age, 65 years) with acute ischemic stroke treated by IV thrombolysis who underwent baseline unenhanced CT within 6 hours after symptom onset and MRI within 24 hours after treatment. ASPECTS was determined by automated software and by three radiologists in consensus. Presence of encephalomalacia and extent of WMHs [categorized using the modified Scheltens scale (mSS)] were also determined using MRI. Kappa coefficients were used to compare ASPECTS between automated and radiologist-consensus methods. Multivariable logistic regression analyses and ROC analyses were performed to explore the predictive utility of baseline ASPECTS for unfavorable clinical outcome (90-day modified Rankin Scale score of 3-6) after thrombolysis. Results: Median automated ASPECTS was 9, and median radiologist-consensus ASPECTS was 10. Agreement between automated and radiologist-consensus ASPECTS, expressed as kappa, was 0.68, though was 0.76 in patients without encephalomalacia and 0.08 in patients with encephalomalacia. In patients without encephalomalacia, agreement decreased as the mSS score increased (e.g., 0.78 in subgroup with mSS score <10 vs 0.19 in subgroup with mSS >20). By anatomic region, agreement was highest for M5 (κ=0.52) and lowest for internal capsule (κ=0.18). In multivariable analyses, both automated (odds ratio=0.69) and radiologist-consensus (odds ratio=0.57) ASPECTS independently predicted unfavorable clinical outcome. For unfavorable outcome, automated ASPECTS had AUC of 0.70, sensitivity of 60.4%, and specificity of 71.0%, while radiologist-consensus ASPECTS had AUC of 0.72, sensitivity of 60.4%, and specificity of 80.5%. Conclusion: Presence of encephalomalacia or extensive WMH results in lower automated ASPECTS than radiologist-consensus ASPECTS, which may impact predictive utility of automated ASPECTS. Clinical Impact: When using automated ASPECTS, radiologists should manually confirm the score in patients with encephalomalacia or extensive leukoencephalopathy.

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients

Objectives

Artif icial intelligence (AI)–based image analysis is increasingly applied in the acute stroke field. Its implementation for the detection and quantification of hemorrhage suspect hyperdensities in non-contrast-enhanced head CT (NCCT) scans may facilitate clinical decision-making and accelerate stroke management.

Methods

NCCTs of 160 patients with suspected acute stroke were analyzed regarding the presence or absence of acute intracranial hemorrhages (ICH) using a novel AI-based algorithm. Read was performed by two blinded neuroradiology residents (R1 and R2). Ground truth was established by an expert neuroradiologist. Specificity, sensitivity, and area under the curve were calculated for ICH and intraparenchymal hemorrhage (IPH) detection. IPH-volumes were segmented and quantified automatically by the algorithm and semi-automatically. Intraclass correlation coefficient (ICC) and Dice coefficient (DC) were calculated.

Results

In total, 79 of 160 patients showed acute ICH, while 47 had IPH. Sensitivity and specificity for ICH detection were 0.91 and 0.89 for the algorithm; 0.99 and 0.98 for R1; and 1.00 and 0.98 for R2. Sensitivity and specificity for IPH detection were 0.98 and 0.89 for the algorithm; 0.83 and 0.99 for R1; and 0.91 and 0.99 for R2. Interreader reliability for ICH and IPH detection showed strong agreements for the algorithm (0.80 and 0.84), R1 (0.96 and 0.84), and R2 (0.98 and 0.92), respectively. ICC indicated an excellent (0.98) agreement between the algorithm and the reference standard of the IPH-volumes. The mean DC was 0.82.

Conclusion

The AI-based algorithm reliably assessed the presence or absence of acute ICHs in this dataset and quantified IPH volumes precisely.

Key Points

• Artificial intelligence (AI) is able to detect hyperdense volumes on brain CTs reliably.

• Sensitivity and specificity are highest for the detection of intraparenchymal hemorrhages.

• Interreader reliability for hemorrhage detection shows strong agreement for AI and human readers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08352-4.

Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans

PURPOSE

Non-contrast CT head scans provide rapid and accurate diagnosis of acute head injury; however, increased utilisation of CT head scans makes it difficult to prioritise acutely unwell patients and places pressure on busy emergency departments (EDs). This study validates an AI algorithm to triage patients presenting with Intracranial Haemorrhage (ICH) or Acute Infarct whilst also identifying a subset of patients as Normal, with the potential to function as a rule-out test.

METHODS

In total, 390 CT head scans were collected from 3 institutions in the UK, US and India. Ground-truth labels were assigned by 3 FRCR consultant radiologists. AI performance, as well as the performance of 3 independent radiologists, was measured against ground-truth labels.

RESULTS

The algorithm showed AUC values of 0.988 (0.978-0.994), 0.933 (0.901-0.961) and 0.939 (0.919-0.958) for ICH, Acute Infarct and Normal, respectively. Sensitivity/specificity for ICH and Acute Infarct were 0.988/0.925 and 0.833/0.927, respectively, compared to 0.907/0.991 and 0.618/0.977 for radiologists. AI rule-out of Normal scans achieved 0.93% negative predictive value (NPV) for the removal of 54.3% of Normal cases, compared to 86.8% NPV for radiologists.

CONCLUSION

We show our algorithm can provide effective triage of ICH and Acute Infarct to prioritise acutely unwell patients. AI can also benefit clinical accuracy, with the algorithm identifying 91.3% of radiologist false negatives for ICH and 69.1% for Acute Infarct. Rule-out of Normal scans has huge potential for workload management in busy EDs, in this case removing 27.4% of all scans with no acute findings missed.

Artificial intelligence in clinical decision support and outcome prediction - applications in stroke

Artificial intelligence (AI) is making a profound impact in healthcare, with the number of AI applications in medicine increasing substantially over the past five years. In acute stroke, it is playing an increasingly important role in clinical decision-making. Contemporary advances have increased the amount of information - both clinical and radiological - which clinicians must consider when managing patients. In the time-critical setting of acute stroke, AI offers the tools to rapidly evaluate and consolidate available information, extracting specific predictions from rich, noisy data. It has been applied to the automatic detection of stroke lesions on imaging and can guide treatment decisions through the prediction of tissue outcomes and long-term functional outcomes. This review examines the current state of AI applications in stroke, exploring their potential to reform stroke care through clinical decision support, as well as the challenges and limitations which must be addressed to facilitate their acceptance and adoption for clinical use.

Leveraging artificial intelligence in ischemic stroke imaging

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.

Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm

OBJECTIVES

The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a promising tool for the evaluation of stroke expansion to determine suitability for reperfusion therapy. The aim of this study was to validate deep learning-based ASPECTS calculation software that utilizes a three-dimensional fully convolutional network-based brain hemisphere comparison algorithm (3D-BHCA).

MATERIALS AND METHODS

We retrospectively collected head non-contrast computed tomography (CT) data from 71 patients with acute ischemic stroke and 80 non-stroke patients. The results for ASPECTS on CT assessed by 5 stroke neurologists and by the 3D-BHCA model were compared with the ground truth by means of region-based and score-based analyses.

RESULTS

In total, 151 patients and 3020 (151 × 20) ASPECTS regions were investigated. Median time from onset to CT was 195 min in the stroke patients. In region-based analysis, the sensitivity (0.80), specificity (0.97), and accuracy (0.96) of the 3D-BHCA model were superior to those of stroke neurologists. The sensitivity (0.98), specificity (0.92), and accuracy (0.97) of dichotomized ASPECTS > 5 analysis and the intraclass correlation coefficient (0.90) in total score-based analysis of the 3D-BHCA model were superior to those of stroke neurologists overall. When patients with stroke were stratified by onset-to-CT time, the 3D-BHCA model exhibited the highest performance to calculate ASPECTS, even in the earliest time period.

CONCLUSIONS

The automated ASPECTS calculation software we developed using a deep learning-based algorithm was superior or equal to stroke neurologists in performing ASPECTS calculation in patients with acute stroke and non-stroke patients.

EIS-Net: Segmenting early infarct and scoring ASPECTS simultaneously on non-contrast CT of patients with acute ischemic stroke

Detecting early infarct (EI) plays an essential role in patient selection for reperfusion therapy in the management of acute ischemic stroke (AIS). EI volume at acute or hyper-acute stage can be measured using advanced pre-treatment imaging, such as MRI and CT perfusion. In this study, a novel multi-task learning approach, EIS-Net, is proposed to segment EI and score Alberta Stroke Program Early CT Score (ASPECTS) simultaneously on baseline non-contrast CT (NCCT) scans of AIS patients. The EIS-Net comprises of a 3D triplet convolutional neural network (T-CNN) for EI segmentation and a multi-region classification network for ASPECTS scoring. T-CNN has triple encoders with original NCCT, mirrored NCCT, and atlas as inputs, as well as one decoder. A comparison disparity block (CDB) is designed to extract and enhance image contexts. In the decoder, a multi-level attention gate module (MAGM) is developed to recalibrate the features of the decoder for both segmentation and classification tasks. Evaluations using a high-quality dataset comprising of baseline NCCT and concomitant diffusion weighted MRI (DWI) as reference standard of 260 patients with AIS show that the proposed EIS-Net can accurately segment EI. The EIS-Net segmented EI volume strongly correlates with EI volume on DWI (r=0.919), and the mean difference between the two volumes is 8.5 mL. For ASPECTS scoring, the proposed EIS-Net achieves an intraclass correlation coefficient of 0.78 for total 10-point ASPECTS and a kappa of 0.75 for dichotomized ASPECTS (≤ 4 vs. >4). Both EI segmentation and ASPECTS scoring tasks achieve state-of-the-art performances.

Acute ischemic stroke management: concepts and controversies.A narrative review

INTRODUCTION

Amongst the 25.7 million survivors and 6.5 million deaths from stroke between 1990 and 2013, ischemic strokes accounted for approximately 70% and 50% of the cases, respectively. With patients still suffering from complications and stroke recurrence, more questions have been raised as to how we can better improve patient management.

AREAS COVERED

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and Newcastle-Ottawa Scale (NOS) were adopted to ensure a comprehensive inclusion of quality literature from various sources. PubMed and Embase were searched for evidence on thrombolysis, mechanical thrombectomy, artificial intelligence (AI), antiplatelet therapy, anticoagulation and hypertension management.

EXPERT OPINION

The directions of future research in these areas are dependent on the current level of validation. Endovascular therapy and applications of AI are relatively new compared to the other areas discussed in this review. As such, future studies need to focus on validating their efficacy. As for thrombolysis, antiplatelet and anticoagulation therapy, their efficacy has been well-established and future research efforts should be directed toward adjusting its use according to patient-specific factors, starting with factors with the most clinical relevance and prevalence.

Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier

There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially available tools for the neuroradiologist. In this narrative review, recent publications exploring ML in neuroradiology are assessed with a focus on several key clinical domains. In particular, major advances are reviewed in the context of: (1) intracranial hemorrhage detection, (2) stroke imaging, (3) intracranial aneurysm screening, (4) multiple sclerosis imaging, (5) neuro-oncology, (6) head and tumor imaging, and (7) spine imaging.

Validation of automated Alberta Stroke Program Early CT Score (ASPECTS) software for detection of early ischemic changes on non-contrast brain CT scans

Purpose

In ASPECTS, 10 brain regions are scored visually for presence of acute ischemic stroke damage. We evaluated automated ASPECTS in comparison to expert readers.

Methods

Consecutive, baseline non-contrast CT-scans (5-mm slice thickness) from the prospective MR CLEAN trial (n = 459, MR CLEAN Netherlands Trial Registry number: NTR1804) were evaluated. A two-observer consensus for ASPECTS regions (normal/abnormal) was used as reference standard for training and testing (0.2/0.8 division). Two other observers provided individual ASPECTS-region scores. The Automated ASPECTS software was applied. A region score specificity of ≥ 90% was used to determine the software threshold for detection of an affected region based on relative density difference between affected and contralateral region. Sensitivity, specificity, and receiver-operating characteristic curves were calculated. Additionally, we assessed intraclass correlation coefficients (ICCs) for automated ASPECTS and observers in comparison to the reference standard in the test set.

Results

In the training set (n = 104), with software thresholds for a specificity of ≥ 90%, we found a sensitivity of 33–49% and an area under the curve (AUC) of 0.741–0.785 for detection of an affected ASPECTS region. In the test set (n = 355), the results for the found software thresholds were 89–89% (specificity), 41–57% (sensitivity), and 0.750–0.795 (AUC). Comparison of automated ASPECTS with the reference standard resulted in an ICC of 0.526. Comparison of observers with the reference standard resulted in an ICC of 0.383–0.464.

Conclusion

The performance of automated ASPECTS is comparable to expert readers and could support readers in the detection of early ischemic changes.

Electronic supplementary material

The online version of this article (10.1007/s00234-020-02533-6) contains supplementary material, which is available to authorized users.

Innovative use of artificial intelligence and digital communication in acute stroke pathway in response to COVID-19

Acute stroke care demands real-time, specialist-led treatment decisions, including thrombolysis and referral for mechanical thrombectomy. Pathways designed to deliver time-critical interventions for stroke patients are under intense pressure due to the impact of COVID-19 pandemic. In response to this unprecedented burden on acute care services, stroke clinicians are having to reconfigure existing clinical pathways both within and between hospitals. Incorporating artificial intelligence and digital communication support into clinical pathways offers an opportunity to mitigate the disruption to acute stroke care. In this case study we describe how Royal Berkshire Hospital, working collaboratively with Brainomix, a UK-based artificial intelligence software company, adopted technological innovation and integrated it into the hyperacute stroke pathway. A case is presented to demonstrate how this innovation can support patient care and deliver successful patient outcomes. We believe this model can be adopted in other hospitals and networks to deliver safe and efficient hyperacute stroke care.

Comparison of the performance between Frontier ASPECTS software and different levels of radiologists on assessing CT examinations of acute ischaemic stroke patients

AIM

To compare the performance of Frontier Alberta Stroke Program Early CT Score (ASPECTS) software with different levels of radiologists in assessing computed tomography (CT) examinations of patients with early acute ischaemic stroke (AIS), and to evaluate whether this software can improve the performance of rating by less experienced radiologists.

MATERIALS AND METHODS

Unenhanced brain CT examinations of 55 patients with acute middle cerebral artery ischaemia were scored separately by Frontier, two senior radiologists, and two junior radiologists retrospectively and blinded to any clinical information. Two junior radiologists then scored again with the assist of Frontier. The reference standard was defined as the ASPECTS on Follow-up unenhanced CT scored by another two non-blinded independent experts on a consensus basis. Statistical analysis was performed using intraclass correlation coefficient (ICC) analysis and Bland-Altman plots.

RESULTS

Frontier and senior radiologists in ASPECTS reading have excellent agreement with the reference standard (r=0.842 and 0.803, respectively), while only a good agreement was found between junior radiologists and reference standard (r=0.680). Bland-Altman analysis revealed the mean ASPECTS difference and SD difference of junior radiologists were larger (mean difference=1.35; SD=1.42) than that of Frontier and senior radiologists with reference standard (mean difference=0.16, 0.22; SD=1.24, 1.13, respectively). However, with the assist of Frontier, the agreement between junior radiologists and reference standard was improved from good (r=0.680) to excellent (r =0.852), and the mean ASPECTS difference and SD difference were reduced.

CONCLUSION

High agreement in ASPECTS rating between senior radiologists, Frontier, and expert consensus reading was found. Moreover, Frontier can improve the performance of less experienced radiologists to assess the ASPECTS of patients with AIS.

Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients

Background

The Alberta Stroke Program Early CT Score (ASPECTS) is a systematic method of assessing the extent of early ischemic change on non-contrast computed tomography in patients with acute ischemic stroke. Our objective was to validate an automated ASPECTS scoring method we recently developed on a large data set.

Materials and methods

We retrospectively collected 602 acute ischemic stroke patients’ non-contrast computed tomography scans. Expert ASPECTS readings on non-contrast computed tomography were compared to automated ASPECTS. Statistical analyses on the total ASPECTS, region level ASPECTS, and dichotomized ASPECTS (≤4 vs. >4) score were conducted.

Results

In total, 602 scans were evaluated and 6020 (602 × 10) ASPECTS regions were scored. Median time from stroke onset to computed tomography was 114 min (interquartile range: 73–183 min). Total ASPECTS for the 602 patients generated by the automated method agreed well with expert readings (intraclass correlation coefficient): 0.65 (95% confidence interval (CI): 0.60–0.69). Region level analysis showed that the automated method yielded accuracy of 81.25%, sensitivity of 61.13% (95% CI: 58.4%–63.8%), specificity of 86.56% (95% CI: 85.6%–87.5%), and area under curve of 0.74 (95% CI: 0.73–0.75). For dichotomized ASPECTS (≤4 vs. >4), the automated method demonstrated sensitivity 97.21% (95% CI: 95.4%–98.4%), specificity 57.81% (95% CI: 44.8%–70.1%), accuracy 93.02%, and area under the curve of 0.78 (95% CI: 0.74–0.81). For each individual region (M1–6, lentiform, insula, and caudate), the automated method demonstrated acceptable performance.

Conclusion

The automated system we developed approached the stroke expert in performance when scoring ASPECTS on non-contrast computed tomography scans of acute ischemic stroke patients.

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

Background and purpose

Acute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, resulting in morbidity. Imaging software using artificial intelligence (AI) and machine learning (ML), a branch of AI, may improve rapid frontline detection of LVO strokes. This report is a systematic review of AI in acute LVO stroke identification and triage, and characterizes LVO detection software.

Methods

A systematic review of acute stroke diagnostic-focused AI studies from January 2014 to February 2019 in PubMed, Medline, and Embase using terms: ‘artificial intelligence’ or ‘machine learning or deep learning’ and ‘ischemic stroke’ or ‘large vessel occlusion’ was performed.

Results

Variations of AI, including ML methods of random forest learning (RFL) and convolutional neural networks (CNNs), are used to detect LVO strokes. Twenty studies were identified that use ML. Alberta Stroke Program Early CT Score (ASPECTS) commonly used RFL, while LVO detection typically used CNNs. Image feature detection had greater sensitivity with CNN than with RFL, 85% versus 68%. However, AI algorithm performance metrics use different standards, precluding ideal objective comparison. Four current software platforms incorporate ML: Brainomix (greatest validation of AI for ASPECTS, uses CNNs to automatically detect LVOs), General Electric, iSchemaView (largest number of perfusion study validations for thrombectomy), and Viz.ai (uses CNNs to automatically detect LVOs, then automatically activates emergency stroke treatment systems).

Conclusions

AI may improve LVO stroke detection and rapid triage necessary for expedited treatment. Standardization of performance assessment is needed in future studies.

Computer-aided imaging analysis in acute ischemic stroke - background and clinical applications

Tools for medical image analysis have been developed to reduce the time needed to detect abnormalities and to provide more accurate results. Particularly, tools based on artificial intelligence and machine learning techniques have led to significant improvements in medical imaging interpretation in the last decade. Automatic evaluation of acute ischemic stroke in medical imaging is one of the fields that witnessed a major development. Commercially available products so far aim to identify (and quantify) the ischemic core, the ischemic penumbra, the site of arterial occlusion and the collateral flow but they are not (yet) intended as standalone diagnostic tools. Their use can be complementary; they are intended to support physicians' interpretation of medical images and hence standardise selection of patients for acute treatment. This review provides an introduction into the field of computer-aided diagnosis and focuses on the automatic analysis of non-contrast-enhanced computed tomography, computed tomography angiography and perfusion imaging. Future studies are necessary that allow the evaluation and comparison of different imaging strategies and post-processing algorithms during the diagnosis process in patients with suspected acute ischemic stroke; which may further facilitate the standardisation of treatment and stroke management.