Automated ASPECT scoring in acute ischemic stroke: comparison of three software tools

Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time

BACKGROUND AND PURPOSE

ASPECTS is a long-standing and well-documented selection criterion for acute ischemic stroke treatment; however, the interpretation of ASPECTS is a challenging and time-consuming task for physicians with notable interobserver variabilities. We conducted a multireader, multicase study in which readers assessed ASPECTS without and with the support of a deep learning (DL)-based algorithm to analyze the impact of the software on clinicians' performance and interpretation time.

MATERIALS AND METHODS

A total of 200 NCCT scans from 5 clinical sites (27 scanner models, 4 different vendors) were retrospectively collected. The reference standard was established through the consensus of 3 expert neuroradiologists who had access to baseline CTA and CTP data. Subsequently, 8 additional clinicians (4 typical ASPECTS readers and 4 senior neuroradiologists) analyzed the NCCT scans without and with the assistance of CINA-ASPECTS (Avicenna.AI), a DL-based, FDA-cleared, and CE-marked algorithm designed to compute ASPECTS automatically. Differences were evaluated in both performance and interpretation time between the assisted and unassisted assessments.

RESULTS

With software aid, readers demonstrated increased region-based accuracy from 72.4% to 76.5% (P < .05) and increased receiver operating characteristic area under the curve (ROC AUC) from 0.749 to 0.788 (P < .05). Notably, all readers exhibited an improved ROC AUC when utilizing the software. Moreover, the use of the algorithm improved the score-based interobserver reliability and correlation coefficient of ASPECTS evaluation by 0.222 and 0.087 (P < .0001), respectively. Additionally, the readers' mean time spent analyzing a case was significantly reduced by 6% (P < .05) when aided by the algorithm.

CONCLUSIONS

With the assistance of the algorithm, readers' analyses were not only more accurate but also faster. Additionally, the overall ASPECTS evaluation exhibited greater consistency, fewer variabilities, and higher precision compared with the reference standard. This novel tool has the potential to enhance patient selection for appropriate treatment by enabling physicians to deliver accurate and timely diagnoses of acute ischemic stroke.

Stroke Diagnosis and Prediction Tool Using ChatGLM: Development and Validation Study

BACKGROUND

Stroke is a globally prevalent disease that imposes a significant burden on health care systems and national economies. Accurate and rapid stroke diagnosis can substantially increase reperfusion rates, mitigate disability, and reduce mortality. However, there are considerable discrepancies in the diagnosis and treatment of acute stroke.

OBJECTIVE

The aim of this study is to develop and validate a stroke diagnosis and prediction tool using ChatGLM-6B, which uses free-text information from electronic health records in conjunction with noncontrast computed tomography (NCCT) reports to enhance stroke detection and treatment.

METHODS

A large language model (LLM) using ChatGLM-6B was proposed to facilitate stroke diagnosis by identifying optimal input combinations, using external tools, and applying instruction tuning and low-rank adaptation (LoRA) techniques. A dataset containing details of 1885 patients with and those without stroke from 2016 to 2024 was used for training and internal validation; another 335 patients from two hospitals were used as an external test set, including 230 patients from the training hospital but admitted at different periods, and 105 patients from another hospital.

RESULTS

The LLM, which is based on clinical notes and NCCT, demonstrates exceptionally high accuracy in stroke diagnosis, achieving 99% in the internal validation dataset and 95.5% and 79.1% in two external test cohorts. It effectively distinguishes between ischemia and hemorrhage, with an accuracy of 100% in the validation dataset and 99.1% and 97.1% in the other test cohorts. In addition, it identifies large vessel occlusions (LVO) with an accuracy of 80% in the validation dataset and 88.6% and 83.3% in the other test cohorts. Furthermore, it screens patients eligible for intravenous thrombolysis (IVT) with an accuracy of 89.4% in the validation dataset and 60% and 80% in the other test cohorts.

CONCLUSIONS

We developed an LLM that leverages clinical text and NCCT to identify strokes and guide recanalization therapy. While our results necessitate validation through widespread deployment, they hold the potential to enhance stroke identification and reduce reperfusion time.

Automated CT angiography collateral scoring in anterior large vessel occlusion stroke: A multireader study

BACKGROUND

Computed tomography (CT) angiography collateral score (CTA-CS) is an important clinical outcome predictor following mechanical thrombectomy for ischemic stroke with large vessel occlusion (LVO). The present multireader study aimed to evaluate the performance of e-CTA software for automated assistance in CTA-CS scoring.

MATERIALS AND METHODS

Brain CTA images of 56 patients with anterior LVO were retrospectively processed. Twelve readers of various clinical training, including junior neuroradiologists, senior neuroradiologists, and neurologists graded collateral flow using visual CTA-CS scale in two sessions separated by a washout period. Reference standard was the consensus of three expert readers. Duration of reading time, inter-rater reliability, and statistical comparison of readers' performance metrics were analyzed between the e-CTA assisted and unassisted sessions.

RESULTS

e-CTA assistance resulted in significant increase in mean accuracy (58.6% to 67.5%, p = 0.003), mean F1 score (0.574 to 0.676, p = 0.002), mean precision (58.8% to 68%, p = 0.007), and mean recall (58.7% to 69.9%, p = 0.002), especially with slight filling deficit (CTA-CS 2 and 3). Mean reading time was reduced across all readers (103.4 to 59.7 s, p = 0.001), and inter-rater agreement in CTA-CS assessment was increased (Krippendorff's alpha 0.366 to 0.676). Optimized occlusion laterality detection was also noted with mean accuracy (92.9% to 96.8%, p = 0.009).

CONCLUSION

Automated assistance for CTA-CS using e-CTA software provided helpful decision support for readers in terms of improving scoring accuracy and reading efficiency for physicians with a range of experience and training backgrounds and leading to significant improvements in inter-rater agreement.

Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic stroke: a multicenter study

OBJECTIVES

The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL).

METHODS

This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians' readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts.

RESULTS

The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system's diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency.

CONCLUSION

The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention.

CLINICAL RELEVANCE STATEMENT

The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications.

KEY POINTS

The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS. This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan. 94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.

Current Stroke Solutions Using Artificial Intelligence: A Review of the Literature

INTRODUCTION

In recent years, artificial intelligence (AI) has emerged as a transformative tool for enhancing stroke diagnosis, aiding treatment decision making, and improving overall patient care. Leading AI-driven platforms such as RapidAI, Brainomix®, and Viz.ai have been developed to assist healthcare professionals in the swift and accurate assessment of stroke patients.

METHODS

Following the PRISMA guidelines, a comprehensive systematic review was conducted using PubMed, Embase, Web of Science, and Scopus. Characteristic descriptive measures were gathered as appropriate from all included studies, including the sensitivity, specificity, accuracy, and comparison of the available tools.

RESULTS

A total of 31 studies were included, of which 29 studies focused on detecting acute ischemic stroke (AIS) or large vessel occlusions (LVOs), and 2 studies focused on hemorrhagic strokes. The four main tools used were Viz.ai, RapidAI, Brainomix®, and deep learning modules.

CONCLUSIONS

AI tools in the treatment of stroke have demonstrated usefulness for diagnosing different stroke types, providing high levels of accuracy and helping to make quicker and more precise clinical judgments.

Segmentation of infarct lesions and prognosis prediction for acute ischemic stroke using non-contrast CT scans

BACKGROUND AND PURPOSE

Ischemic stroke is the most common type of stroke and the second leading cause of global mortality. Prompt and accurate diagnosis is crucial for effective treatment. Non-contrast CT (NCCT) scans are commonly employed as the first-line imaging modality to identify the infarct lesion and affected brain areas, as well as to make prognostic predictions to guide the subsequent treatment planning. However, visual evaluation of infarct lesions in NCCT scans can be subjective and inconsistent due to reliance on expert experience.

METHODS

In this study, we propose an automatic method using VB-Net with dual-channel inputs to segment acute infarct lesions (AIL) on NCCT scans and extract affected ASPECTS (Alberta Stroke Program Early CT Score) regions. Secondly, we establish a prediction model to distinguish reperfused patients from non-reperfused patients after treatment, based on multi-dimensional radiological features of baseline NCCT and stroke onset time. Thirdly, we create a prediction model estimating the infarct volume after a period of time, by combining NCCT infarct volume, radiological features, and surgical decision.

RESULTS

The median Dice coefficient of the AIL segmentation network is 0.76. Based on this, the patient triage model has an AUC of 0.837 (95 % confidence interval [CI]: 0.734-0.941), sensitivity of 0.833 (95 % CI: 0.626-0.953). The predicted follow-up infarct volume correlates strongly with the DWI ground truth, with a Pearson correlation coefficient of 0.931.

CONCLUSIONS

Our proposed pipeline offers qualitative and quantitative assessment of infarct lesions based on NCCT scans, facilitating physicians in patient triage and prognosis prediction.

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.

Automated detection of early signs of irreversible ischemic change on CTA source images in patients with large vessel occlusion

PURPOSE

To create and validate an automated pipeline for detection of early signs of irreversible ischemic change from admission CTA in patients with large vessel occlusion (LVO) stroke.

METHODS

We retrospectively included 368 patients for training and 143 for external validation. All patients had anterior circulation LVO stroke, endovascular therapy with successful reperfusion, and follow-up diffusion-weighted imaging (DWI). We devised a pipeline to automatically segment Alberta Stroke Program Early CT Score (ASPECTS) regions and extracted their relative Hounsfield unit (rHU) values. We determined the optimal rHU cut points for prediction of final infarction in each ASPECT region, performed 10-fold cross-validation in the training set, and measured the performance via external validation in patients from another institute. We compared the model with an expert neuroradiologist for prediction of final infarct volume and poor functional outcome.

RESULTS

We achieved a mean area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of 0.69±0.13, 0.69±0.09, 0.61±0.23, and 0.72±0.11 across all regions and folds in cross-validation. In the external validation cohort, we achieved a median [interquartile] AUC, accuracy, sensitivity, and specificity of 0.71 [0.68-0.72], 0.70 [0.68-0.73], 0.55 [0.50-0.63], and 0.74 [0.73-0.77], respectively. The rHU-based ASPECTS showed significant correlation with DWI-based ASPECTS (rS = 0.39, p<0.001) and final infarct volume (rS = -0.36, p<0.001). The AUC for predicting poor functional outcome was 0.66 (95%CI: 0.57-0.75). The predictive capabilities of rHU-based ASPECTS were not significantly different from the neuroradiologist's visual ASPECTS for either final infarct volume or functional outcome.

CONCLUSIONS

Our study demonstrates the feasibility of an automated pipeline and predictive model based on relative HU attenuation of ASPECTS regions on baseline CTA and its non-inferior performance in predicting final infarction on post-stroke DWI compared to an expert human reader.

Application of Artificial Intelligence in Acute Ischemic Stroke: A Scoping Review

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

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.

Automated ASPECTS calculation may equal the performance of experienced clinicians: a machine learning study based on a large cohort

OBJECTIVES

The Alberta Stroke Program Early CT Score (ASPECTS) is a semi-quantitative method to evaluate the severity of early ischemic change on non-contrast computed tomography (NCCT) in patients with acute ischemic stroke (AIS). In this work, we propose an automated ASPECTS method based on large cohort of data and machine learning.

METHODS

For this study, we collected 3626 NCCT cases from multiple centers and annotated directly on this dataset by neurologists. Based on image analysis and machine learning methods, we constructed a two-stage machine learning model. The validity and reliability of this automated ASPECTS method were tested on an independent external validation set of 300 cases. Statistical analyses on the total ASPECTS, dichotomized ASPECTS, and region-level ASPECTS were presented.

RESULTS

On an independent external validation set of 300 cases, for the total ASPECTS results, the intraclass correlation coefficient between automated ASPECTS and expert-rated was 0.842. The agreement between ASPECTS threshold of ≥ 6 versus < 6 using a dichotomized method was moderate (κ = 0.438, 0.391-0.477), and the detection rate (sensitivity) was 86.5% for patients with ASPECTS threshold of ≥ 6. Compared with the results of previous studies, our method achieved a slight lead in sensitivity (67.8%) and AUC (0.845), with comparable accuracy (78.9%) and specificity (81.2%).

CONCLUSION

The proposed automated ASPECTS method driven by a large cohort of NCCT images performed equally well compared with expert-rated ASPECTS. This work further demonstrates the validity and reliability of automated ASPECTS evaluation method.

CLINICAL RELEVANCE STATEMENT

The automated ASPECTS method proposed by this study may help AIS patients to receive rapid intervention, but should not be used as a stand-alone diagnostic basis.

KEY POINTS

NCCT-based manual ASPECTS scores were poorly consistent. Machine learning can automate the ASPECTS scoring process. Machine learning model design based on large cohort data can effectively improve the consistency of ASPECTS scores.

Artificial intelligence applied in acute ischemic stroke: from child to elderly

This review will summarize artificial intelligence developments in acute ischemic stroke in recent years and forecasts for the future. Stroke is a major healthcare concern due to its effects on the patient's quality of life and its dependence on the timing of the identification as well as the treatment. In recent years, attention increased on the use of artificial intelligence (AI) systems to help categorize, prognosis, and to channel these patients toward the right therapeutic procedure. Machine learning (ML) and in particular deep learning (DL) systems using convoluted neural networks (CNN) are becoming increasingly popular. Various studies over the years evaluated the use of these methods of analysis and prediction in the assessment of stroke patients, and at the same time, several applications and software have been developed to support the neuroradiologists and the stroke team to improve patient outcomes.

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.

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.

Revolutionizing the Management of Large-Core Ischaemic Strokes: Decoding the Success of Endovascular Therapy in the Recent Stroke Trials

Endovascular therapy (EVT) has revolutionized the management of acute ischaemic strokes with large vessel occlusion, with emerging evidence suggesting its benefit also in large infarct core volume strokes. In the last two years, four randomised controlled trials have been published on this topic-RESCUE-Japan LIMIT, ANGEL-ASPECT, SELECT2 and TENSION, with overall results showing that EVT improves functional and neurological outcomes compared to medical management alone. This review aims to summarise the recent evidence presented by these four trials and highlight some of the limitations in our current understanding of this topic.

Deep Learning-Based Extraction of Biomarkers for the Prediction of the Functional Outcome of Ischemic Stroke Patients

Accurately predicting functional outcomes in stroke patients remains challenging yet clinically relevant. While brain CTs provide prognostic information, their practical value for outcome prediction is unclear. We analyzed a multi-center cohort of 743 ischemic stroke patients (<72 h onset), including their admission brain NCCT and CTA scans as well as their clinical data. Our goal was to predict the patients' future functional outcome, measured by the 3-month post-stroke modified Rankin Scale (mRS), dichotomized into good (mRS ≤ 2) and poor (mRS > 2). To this end, we developed deep learning models to predict the outcome from CT data only, and models that incorporate other patient variables. Three deep learning architectures were tested in the image-only prediction, achieving 0.779 ± 0.005 AUC. In addition, we created a model fusing imaging and tabular data by feeding the output of a deep learning model trained to detect occlusions on CT angiograms into our prediction framework, which achieved an AUC of 0.806 ± 0.082. These findings highlight how further refinement of prognostic models incorporating both image biomarkers and clinical data could enable more accurate outcome prediction for ischemic stroke patients.

Artificial intelligence-driven ASPECTS for the detection of early stroke changes in non-contrast CT: a systematic review and meta-analysis

BACKGROUND

Recent advances in machine learning have enabled development of the automated Alberta Stroke Program Early CT Score (ASPECTS) prediction algorithms using non-contrast enhanced computed tomography (NCCT) scans. The applicability of automated ASPECTS in daily clinical practice is yet to be established. The objective of this meta-analysis was to directly compare the performance of automated and manual ASPECTS predictions in recognizing early stroke changes on NCCT.

METHODS

The MEDLINE, Scopus, and Cochrane databases were searched. The last database search was performed on March 10, 2022. Studies reporting the diagnostic performance and validity of automated ASPECTS software compared with expert readers were included. The outcomes were the interobserver reliability of outputs between ASPECTS versus expert readings, experts versus reference standard, and ASPECTS versus reference standard by means of pooled Fisher's Z transformation of the interclass correlation coefficients (ICCs).

RESULTS

Eleven studies were included in the meta-analysis, involving 1976 patients. The meta-analyses showed good interobserver reliability between experts (ICC 0.72 (95% CI 0.63 to 0.79); p<0.001), moderate reliability in the correlation between automated and expert readings (ICC 0.54 (95% CI 0.40 to 0.67); p<0.001), good reliability between the total expert readings and the reference standard (ICC 0.62 (95% CI 0.52 to 0.71); p<0.001), and good reliability between the automated predictions and the reference standard (ICC 0.72 (95% CI 0.61 to 0.80); p<0.001).

CONCLUSIONS

Artificial intelligence-driven ASPECTS software has comparable or better performance than physicians in terms of recognizing early stroke changes on NCCT.

Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study

Background

The Alberta Stroke Program Early CT Score (ASPECTS) is used to quantify the extent of injury to the brain following acute ischemic stroke (AIS) and to inform treatment decisions. The e-ASPECTS software uses artificial intelligence methods to automatically process non-contrast CT (NCCT) brain scans from patients with AIS affecting the middle cerebral artery (MCA) territory and generate an ASPECTS. This study aimed to evaluate the impact of e-ASPECTS (Brainomix, Oxford, UK) on the performance of US physicians compared to a consensus ground truth.

Methods

The study used a multi-reader, multi-case design. A total of 10 US board-certified physicians (neurologists and neuroradiologists) scored 54 NCCT brain scans of patients with AIS affecting the MCA territory. Each reader scored each scan on two occasions: once with and once without reference to the e-ASPECTS software, in random order. Agreement with a reference standard (expert consensus read with reference to follow-up imaging) was evaluated with and without software support.

Results

A comparison of the area under the curve (AUC) for each reader showed a significant improvement from 0.81 to 0.83 (p = 0.028) with the support of the e-ASPECTS tool. The agreement of reader ASPECTS scoring with the reference standard was improved with e-ASPECTS compared to unassisted reading of scans: Cohen's kappa improved from 0.60 to 0.65, and the case-based weighted Kappa improved from 0.70 to 0.81.

Conclusion

Decision support with the e-ASPECTS software significantly improves the accuracy of ASPECTS scoring, even by expert US neurologists and neuroradiologists.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

Minimal Imaging Requirements

The minimal requirements for imaging studies prior to endovascular treatment (EVT) of acute ischemic stroke are those that can provide the information necessary to determine the indication for treatment (treatment triage) and procedural strategies without being time-consuming. An important notion is to determine whether the patient can benefit from EVT. We should recognize that the perfect diagnostic imaging technique does not yet exist, and each has advantages and disadvantages. Generally, stroke imaging protocols to triage for EVT include the following three options: 1) non-contrast CT and CTA, 2) CT perfusion and CTA, and 3) MRI and MRA. It is not known if perfusion imaging or MRI is mandatory for patients with stroke presenting within 6 hours of onset, although non-contrast CT alone has less power to obtain the necessary information. Dual-energy CT can distinguish between post-EVT hemorrhage and contrast agent leakage immediately after EVT.

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.

Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies

Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain imaging for more immediate diagnosis or promote post-stroke neurological/functional recovery. Technologies that assist with diagnosis predominantly use convolutional neural networks to identify abnormal brain images (e.g., CT perfusion). These technologies perform comparably to neuroradiologists, improve clinical workflows (e.g., time from scan acquisition to reading), and improve patient outcomes (e.g., days spent in the neurological ICU). Two devices are indicated for post-stroke rehabilitation by leveraging neuromodulation techniques. Multiple FDA-approved technologies exist that can help clinicians better diagnose and manage stroke. This review summarizes the most up-to-date literature regarding the functionality, performance, and utility of these technologies so clinicians can make informed decisions when using them in practice.

Machine-learning algorithm in acute stroke: real-world experience

AIM

To assess the clinical performance of a commercially available machine learning (ML) algorithm in acute stroke.

MATERIALS AND METHODS

CT and CT angiography (CTA) studies of 104 consecutive patients (43 females, age range 19-93, median age 62) performed for suspected acute stroke at a single tertiary institution with real-time ML software analysis (RAPID™ ASPECTS and CTA) were included. Studies were retrospectively reviewed independently by two neuroradiologists in a blinded manner.

RESULTS

The cohort included 24 acute infarcts and 16 large vessel occlusions (LVO). RAPID™ ASPECTS interpretation demonstrated high sensitivity (87.5%) and NPV (87.5%) but very poor specificity (30.9%) and PPV (30.9%) for detection of acute ischaemic parenchymal changes. There was a high percentage of false positives (51.1%). In cases of proven LVO, RAPID™ ASPECTS showed good correlation with neuroradiologists' blinded independent interpretation, Pearson correlation coefficient = 0.96 (both readers), 0.63 (RAPID™ vs reader 1), 0.69 (RAPID™ vs reader 2). RAPID™ CTA interpretation demonstrated high sensitivity (92.3%), specificity (85.3%), and negative predictive (NPV) (98.5%) with moderate positive predictive value (PPV) (52.2%) for detection of LVO (N=13). False positives accounted for 12.5% of cases, of which 27.3% were attributed to arterial stenosis.

CONCLUSION

RAPID™ CTA was robust and reliable in detection of LVO. Although demonstrating high sensitivity and NPV, RAPID™ ASPECTS interpretation was associated with a high number of false positives, which decreased clinicians' confidence in the algorithm. However, in cases of proven LVO, RAPID™ ASPECTS performed well and had good correlation with neuroradiologists' blinded interpretation.

Machine learning and acute stroke imaging

BACKGROUND

In recent years, machine learning (ML) has had notable success in providing automated analyses of neuroimaging studies, and its role is likely to increase in the future. Thus, it is paramount for clinicians to understand these approaches, gain facility with interpreting ML results, and learn how to assess algorithm performance.

OBJECTIVE

To provide an overview of ML, present its role in acute stroke imaging, discuss methods to evaluate algorithms, and then provide an assessment of existing approaches.

METHODS

In this review, we give an overview of ML techniques commonly used in medical imaging analysis and methods to evaluate performance. We then review the literature for relevant publications. Searches were run in November 2021 in Ovid Medline and PubMed. Inclusion criteria included studies in English reporting use of artificial intelligence (AI), machine learning, or similar techniques in the setting of, and in applications for, acute ischemic stroke or mechanical thrombectomy. Articles that included image-level data with meaningful results and sound ML approaches were included in this discussion.

RESULTS

Many publications on acute stroke imaging, including detection of large vessel occlusion, detection and quantification of intracranial hemorrhage and detection of infarct core, have been published using ML methods. Imaging inputs have included non-contrast head CT, CT angiograph and MRI, with a range of performances. We discuss and review several of the most relevant publications.

CONCLUSIONS

ML in acute ischemic stroke imaging has already made tremendous headway. Additional applications and further integration with clinical care is inevitable. Thus, facility with these approaches is critical for the neurointerventional clinician.

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.

Comparison of automated ASPECTS, large vessel occlusion detection and CTP analysis provided by Brainomix and RapidAI in patients with suspected ischaemic stroke

OBJECTIVES

The ischaemic core and penumbra volumes derived from CTP aid the selection of patients with an arterial occlusion for mechanical thrombectomy. Different post-processing software packages may give different CTP outputs, potentially causing variable patient selection for mechanical thrombectomy. The study aims were, firstly, to assess the correlation in CTP outputs from software packages provided by Brainomix and RapidAI. Secondly, the correlation between automated ASPECTS and neuroradiologist-derived ASPECTS and accuracy in detecting large vessel occlusion was assessed.

MATERIALS AND METHODS

This retrospective study included patients undergoing CTP for suspected anterior circulation large vessel occlusion. Pearson's correlation coefficient was used for testing the correlation in CTP outputs, ASPECTS/automated ASPECTS, and-in those with complete or near complete occlusion-final infarct volume. Diagnostic statistics were calculated for large vessel occlusion detection.

RESULTS

Correlation was high for ischaemic core and penumbra volumes (0.862 and 0.832, respectively) but lower for the mismatch ratio (0.477). Agreement in mechanical thrombectomy eligibility was achieved in 85% of cases (46/54). Correlation between ischaemic core and final infarct volume was higher for Brainomix (0.757) than for RapidAI (0.595). The correlation between ASPECTS and automated ASPECTS (0.738 and 0.659) and the accuracy of detecting large vessel occlusion (77% and 71%) was higher for Brainomix than for RapidAI.

CONCLUSION

There was high correlation between the CTP output from Brainomix and RapidAI. However, there was a difference in MT eligibility in 15% of cases, which highlights that the decision regarding MT should not be based on imaging parameters alone.

Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence

There is increasing interest in computer applications, using artificial intelligence methodologies, to perform health care tasks previously performed by humans, particularly in medical imaging for diagnosis. In stroke, there are now commercial artificial intelligence software for use with computed tomography or MR imaging to identify acute ischemic brain tissue pathology, arterial obstruction on computed tomography angiography or as hyperattenuated arteries on computed tomography, brain hemorrhage, or size of perfusion defects. A rapid, accurate diagnosis may aid treatment decisions for individual patients and could improve outcome if it leads to effective and safe treatment; or conversely, to disaster if a delayed or incorrect diagnosis results in inappropriate treatment. Despite this potential clinical impact, diagnostic tools including artificial intelligence methods are not subjected to the same clinical evaluation standards as are mandatory for drugs. Here, we provide an evidence-based review of the pros and cons of commercially available automated methods for medical imaging diagnosis, including those based on artificial intelligence, to diagnose acute brain pathology on computed tomography or magnetic resonance imaging in patients with stroke.

Multi-Mode Imaging Scale for Endovascular Therapy in Patients with Acute Ischemic Stroke (META)

Background: With the guidance of multi-mode imaging, the time window for endovascular thrombectomy (EVT) has been expanded to 24 h. However, poor clinical outcomes are still not uncommon. We aimed to develop a multi-mode imaging scale for endovascular therapy in patients with acute ischemic stroke (META) to predict the neurological outcome in patients receiving endovascular thrombectomy (EVT). Methods: We included consecutive acute ischemic stroke patients with occlusion of middle cerebral artery and/or internal carotid artery who underwent EVT. Poor outcome was defined as modified Rankin Scale (mRS) score of 3−6 at 3 months. A five-point META score was constructed based on clot burden score, multi-segment clot, the Alberta Stroke Program early computed tomography score of cerebral blood volume (CBV-ASPECTS), and collateral status. We evaluated the META score performance using area under the curve (AUC) calculations. Results: A total of 259 patients were included. A higher META score was independently correlated with poor outcomes at 3 months (odds ratio, 1.690, 95% CI, 1.340 to 2.132, p < 0.001) after adjusting for age, hypertension, baseline National Institutes of Health Stroke Scale (NIHSS) score, and baseline blood glucose. Patients with a META score ≥ 2 were less likely to benefit from EVT (mRS 3−6: 60.8% vs. 29.2%, p < 0.001). The META score predicted poor outcomes with an AUC of 0.714, higher than the Pittsburgh Response to Endovascular therapy (PRE) score, the totaled health risks in vascular events (THRIVE) score (AUC: 0.566, 0.706), and the single imaging marker in the scale. Conclusions: The novel META score could refine the predictive accuracy of prognosis after EVT, which might provide a promising avenue for future automatic imaging analysis to help decision making.

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

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 CTP core volume of an infraction. The proposed method shows its potential for automated ASPECTS scoring on NCCT images.

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.

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.

Diagnostic accuracy of automated occlusion detection in CT angiography using e-CTA

Background and aim

The aim of this study was to assess the diagnostic accuracy of e-CTA (product name) (Brainomix) in the automatic detection of large vessel occlusions in anterior circulation stroke.

Methods

Of 487 CT angiographies from patients with large vessel occlusions stroke, 327 were used to train the algorithm while the remaining cases together with 140 negative CT angiographies were used to validate its performance against ground truth. Of these 301 cases, 144 were randomly selected and used for an additional comparative analysis against 4 raters. Sensitivity, specificity, positive and negative predictive value (PPV and NPV), accuracy and level of agreement with ground truth (Cohen’s Kappa) were determined and compared to the performance of a neuroradiologist, a radiology resident, and two neurology residents.

Results

e-CTA had a sensitivity and specificity of 0.84 (0.77–0.89) and 0.96 (0.91–0.98) respectively for the detection of any large vessel occlusions on the correct side in the whole validation cohort. This performance was identical in the comparative analysis subgroup and was within the range of physicians at different levels of expertise: 0.86–0.97 and 0.91–1.00, respectively. For the detection of proximal occlusions, it was 0.92 (0.84–0.96) and 0.98 (0.94–1.00) for the whole cohort and 0.93 (0.80–0.98) and 1.00 (0.95–1.00) for the comparative analysis, respectively for e-CTA. The range was 0.8–0.97 for sensitivity and 0.97–1.00 for specificity for the four physicians.

Conclusions

The performance of e-CTA in detecting any large vessel occlusions is comparable to less experienced physicians but is similar to experienced physicians for detecting proximal large vessel occlusions.

The effects of the skull on CT imaging of the brain: a skull and brain phantom study

OBJECTIVE

To investigate the effects of beam hardening by the skull on the measured radiodensity of the brain. To test a hypothesis that these effects of beam hardening are decreased using a monochromatic energy source.

METHODS

Selected clinical cases were reviewed in illustration. An anthropomorphic skull and brain phantom was created and scanned in a clinical CT scanner with skull, without skull, and with hemicraniectomy. The effects of beam hardening were illustrated by scanning the phantom with mono- and poly-chromatic X-ray sources.

RESULTS

In clinical cases, the HU values of the brain were consistently lower when the X-ray beam traversed the skull than when it did not. An anthropomorphic skull-and-brain phantom further demonstrated these effects, which were evident with a polychromatic energy source and absent with a virtual monochromatic energy source.

CONCLUSIONS

Beam hardening by the skull lowers the measured HU values of the brain. The effects, which can impact quantitative imaging, may be mitigated by a virtual monochromatic energy source.

ADVANCES IN KNOWLEDGE

Beam hardening by the skull lowers the measured radiodensity of the brain. The effects may be mitigated by a virtual monochromatic energy source.

Correlation between ASPECTS and Core Volume on CT Perfusion: Impact of Time since Stroke Onset and Presence of Large-Vessel Occlusion

BACKGROUND AND PURPOSE

Both ASPECTS and core volume on CTP are used to estimate infarct volume in acute ischemic stroke. To evaluate the potential role of ASPECTS for acute endovascular treatment decisions, we studied the correlation between ASPECTS and CTP core, depending on the timing and the presence of large-vessel occlusion.

MATERIALS AND METHODS

We retrospectively reviewed all MCA acute ischemic strokes with standardized reconstructions of CTP maps entered in the Acute STroke Registry and Analysis of Lausanne (ASTRAL) registry. Correlation between ASPECTS and CTP core was determined for early (<6 hours) versus late (6-24 hours) times from stroke onset and in the presence versus absence of large-vessel occlusion. We used correlation coefficients and adjusted multiple linear regression models.

RESULTS

We included 1046 patients with a median age of 71.4 years (interquartile range, IQR = 59.8-79.4 years), an NIHSS score of 12 (IQR, 6-18), an ASPECTS of 9 (IQR, 7-10), and a CTP core of 13.6 mL (IQR, 0.6-52.8 mL). The overall correlation between ASPECTS and CTP core was moderate (ρ = -0.49, P < .01) but significantly stronger in the late-versus-early window (ρ = -0.56 and ρ = -0.48, respectively; P = .05) and in the presence versus absence of large-vessel occlusion (ρ = -0.40 and ρ = -0.20, respectively; P < .01). In the regression model, the independent association between ASPECTS and CTP core was confirmed and was twice as strong in late-arriving patients with large-vessel occlusion (β = -0.21 per 10 mL; 95% CI, -0.27 to -0.15; P < .01) than in the overall population (β = -0.10; 95% CI, -0.14 to -0.07; P < .01).

CONCLUSIONS

In a large cohort of patients with acute ischemic stroke, we found a moderate correlation between ASPECTS and CTP core. However, this was stronger in patients with large-vessel occlusion and longer delay from stroke onset. Our results could support the use of ASPECTS as a surrogate marker of CTP core in late-arriving patients with acute ischemic stroke with large-vessel occlusion.

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.

Quantitative Serial CT Imaging-Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke

INTRODUCTION

Malignant cerebral edema develops in a small subset of patients with hemispheric strokes, precipitating deterioration and death if decompressive hemicraniectomy (DHC) is not performed in a timely manner. Predicting which stroke patients will develop malignant edema is imprecise based on clinical data alone. Head computed tomography (CT) imaging is often performed at baseline and 24-h. We determined the incremental value of incorporating imaging-derived features from serial CTs to enhance prediction of malignant edema.

METHODS

We identified hemispheric stroke patients at three sites with NIHSS ≥ 7 who had baseline as well as 24-h clinical and CT imaging data. We extracted quantitative imaging features from baseline and follow-up CTs, including CSF volume, intracranial reserve (CSF/cranial volume), as well as midline shift (MLS) and infarct-related hypodensity volume. Potentially lethal malignant edema was defined as requiring DHC or dying with MLS over 5-mm. We built machine-learning models using logistic regression first with baseline data and then adding 24-h data including reduction in CSF volume (ΔCSF). Model performance was evaluated with cross-validation using metrics of recall (sensitivity), precision (predictive value), as well as area under receiver-operating-characteristic and precision-recall curves (AUROC, AUPRC).

RESULTS

Twenty of 361 patients (6%) died or underwent DHC. Baseline clinical variables alone had recall of 60% with low precision (7%), AUROC 0.59, AUPRC 0.15. Adding baseline intracranial reserve improved recall to 80% and AUROC to 0.82 but precision remained only 16% (AUPRC 0.28). Incorporating ΔCSF improved AUPRC to 0.53 (AUROC 0.91) while all imaging features further improved prediction (recall 90%, precision 38%, AUROC 0.96, AUPRC 0.66).

CONCLUSION

Incorporating quantitative CT-based imaging features from baseline and 24-h CT enhances identification of patients with malignant edema needing DHC. Further refinements and external validation of such imaging-based machine-learning models are required.