Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning

Prognostic value of combining 24-hour ASPECTS and hemoglobin to red cell distribution width ratio to the THRIVE score in predicting in-hospital mortality among ischemic stroke patients treated with intravenous thrombolysis

BACKGROUND

Acute ischemic stroke (AIS) is a significant global health issue, directly impacting mortality and disability. The Totaled Health Risks in Vascular Events (THRIVE) score is appreciated for its simplicity and ease of use to predict stroke clinical outcomes; however, it lacks laboratory and neuroimaging data, which limits its ability to predict outcomes precisely. Our study evaluates the impact of integrating the 24-hour Alberta Stroke Program Early CT Score (ASPECTS) and hemoglobin-to-red cell distribution width (HB/RDW) ratio into the THRIVE score using the multivariable fractional polynomial (MFP) method (combined THRIVE-MFP model) compared to the THRIVE-c model. We aim to assess their added value in predicting in-hospital mortality (IHM) prognosis.

MATERIALS AND METHODS

A retrospective study from January 2015 to July 2022 examined consecutive AIS patients receiving intravenous thrombolysis. Data on THRIVE scores, 24-hour ASPECTS, and HB/RDW levels were collected upon admission. The model was constructed using logistic regression and the MFP method. The prognostic value was determined using the area under the receiver operating characteristic curve (AuROC). Ischemic cerebral lesions within the middle cerebral artery territory were evaluated with non-contrast computed tomography (NCCT) after completing 24 hours of intravenous thrombolysis (24-hour ASPECTS).

RESULTS

Among a cohort of 345 patients diagnosed with AIS who received intravenous thrombolysis, 65 individuals (18.8%) experienced IHM. The combined THRIVE-MFP model was significantly superior to the THRIVE-c model in predicting IHM (AuROC 0.980 vs. 0.876, p<0.001), 3-month mortality (AuROC 0.947 vs. 0.892, p<0.001), and 3-month poor functional outcome (AuROC 0.910 vs. 0.853, p<0.001).

CONCLUSION

The combined THRIVE-MFP model showed excellent predictive performance, enhancing physicians' ability to stratify patient selection for intensive neurological monitoring and guiding treatment decisions. Incorporating 24-hour ASPECTS on NCCT and HB/RDW proved valuable in mortality prediction, particularly for hospitals with limited access to advanced neuroimaging resources.

Automatic assessment of DWI-ASPECTS for acute ischemic stroke based on deep learning

BACKGROUND

Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a standardized semi-quantitative method for early ischemic changes in acute ischemic stroke.

PURPOSE

However, ASPECTS is still affected by expert experience and inconsistent results between readers in clinical. This study aims to propose an automatic ASPECTS scoring model based on diffusion-weighted imaging (DWI) mode to help clinicians make accurate treatment plans.

METHODS

Eighty-two patients with stroke were included in the study. First, we designed a new deep learning network for segmenting ASPECTS scoring brain regions. The network is improved based on U-net, which integrates multiple modules. Second, we proposed using hybrid classifiers to classify brain regions. For brain regions with larger areas, we used brain grayscale comparison algorithm to train machine learning classifiers, while using hybrid feature training for brain regions with smaller areas.

RESULTS

The average DICE coefficient of the segmented hindbrain area can reach 0.864. With the proposed hybrid classifier, our method performs significantly on both region-level ASPECTS and dichotomous ASPECTS. The sensitivity and accuracy on the test set are 95.51% and 93.43%, respectively. For dichotomous ASPECTS, the intraclass correlation coefficient (ICC) between our automated ASPECTS score and the expert reading was 0.87.

CONCLUSIONS

This study proposed an automated model for ASPECTS scoring of patients with acute ischemic stroke based on DWI images. Experimental results show that the method of segmentation first and then classification is feasible. Our method has the potential to assist physicians in the Alberta Stroke Program with early CT scoring and clinical stroke diagnosis.

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the state-of-the-art in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.

Contributions of Machine Learning in the Management of Stroke: A Bibliometric Analysis of the 50 Most Cited Articles

BACKGROUND

Though currently considered a 'black box,' machine learning (ML) has a promising future to ameliorate the health-care burden of stroke which is the second leading cause of mortality worldwide. Through this study, we sought to review the most influential articles on the applications of ML in stroke.

METHODS

Web of Sciences database was searched, and a list of the top 50 most cited articles, assessing the application of ML in stroke, was prepared by 2 authors, independently. Subsequently, a detailed analysis was performed to characterize the most impactful studies.

RESULTS

The total number of citations to the top 50 articles were 2959 (range 35-243 citations) with a median of 47 citations. Highest number of articles were published in the journal Stroke and the United States was the major contributing country. The majority of the studies focused on the utilization of ML to improve stroke risk prediction, diagnosis, and outcome prediction. Statistical analysis revealed an insignificant association between the total and mean number of citations and the impact factor of the journal (P = 0.516 and 0.987, respectively).

CONCLUSIONS

Recent years have witnessed a surge in the application of ML in stroke, with an enhancement in interest and funding over the years. ML has revolutionized the management of stroke and continues to aid in the neurosurgical decision-making and care in stroke patients.

Application of radiomics in ischemic stroke

In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.

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.

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.

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.

Predictive Value of Acute Neurological Progression Using Bayesian CT Perfusion for Acute Ischemic Stroke with Large or Median Vessel Occlusion

Objective

Since the efficacy of mechanical thrombectomy (MT) for acute cerebral infarction due to large vessel occlusion has been proven, the time available for treatment has gradually increased. Currently, under certain conditions, treatment is indicated up to 24 h from onset. Based on neurological signs and imaging diagnosis, Stroke Treatment Guideline 2021 recommends initiation of MT within 6-24 h from onset. Herein, we retrospectively investigated the relationship between cerebral perfusion imaging evaluation and prognosis in patients with acute cerebral infarction due to large or median vessel occlusion.

Methods

Fifty-one patients diagnosed with acute cerebral infarction due to large or median vessel occlusions in anterior circulation between November 2019 and December 2021 were divided into medical care and reconstructive therapy (including tissue plasminogen activator [t-PA] therapy and MT) groups. The primary outcome was changes in the National Institutes of Health Stroke Scale (NIHSS) at admission and 1 week after onset. Patients in the medical care group were divided into those whose NIHSS did not worsen and those whose NIHSS worsened. Those in the reconstructive therapy group were divided into those whose NIHSS improved and those whose NIHSS did not improve. We evaluated the relationship between improvement factors in acute neurological symptoms and penumbral and core volumes from computed tomography perfusion performed at admission.

Results

Of 45 eligible patients, 10 received medical care without t-PA or MT and 35 underwent reconstructive therapy, including t-PA and MT. Among the 10 patients in the medical care group, 3 had worsening symptoms and 7 did not. The mean and median (interquartile range [IQR]) penumbra volumes were significantly higher in patients with worsening symptoms than in those without. The receiver operating characteristic (ROC) curve showed a threshold value of 28.6 mL with an area under the curve (AUC) of 0.952. Among the 35 patients in the reconstructive therapy group, symptoms improved for 29 but did not improve for 6. The mean and median (IQR) core volumes were significantly higher in patients whose symptoms did not improve than in those whose symptoms improved. The ROC curve showed a threshold value of 25 mL and an AUC of 0.632.

Conclusion

Evaluation of penumbra volumes could detect cases with worsening symptoms in cases where medical care was performed, and evaluation of core volumes may detect cases with non-improved symptoms in cases that received reconstructive therapy.

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.

Recent developments in pre-hospital and in-hospital triage for endovascular stroke treatment

Triage describes the assignment of resources based on where they can be best used, are most needed, or are most likely to achieve success. Triage is of particular importance in time-critical conditions such as acute ischemic stroke. In this setting, one of the goals of triage is to minimize the delay to endovascular thrombectomy (EVT), without delaying intravenous thrombolysis or other time-critical treatments including patients who cannot benefit from EVT. EVT triage is highly context-specific, and depends on availability of financial resources, staff resources, local infrastructure, and geography. Furthermore, the EVT triage landscape is constantly changing, as EVT indications evolve and new neuroimaging methods, EVT technologies, and adjunctive medical treatments are developed and refined. This review provides an overview of recent developments in EVT triage at both the pre-hospital and in-hospital stages. We discuss pre-hospital large vessel occlusion detection tools, transport paradigms, in-hospital workflows, acute stroke neuroimaging protocols, and angiography suite workflows. The most important factor in EVT triage, however, is teamwork. Irrespective of any new technology, EVT triage will only reach optimal performance if all team members, including paramedics, nurses, technologists, emergency physicians, neurologists, radiologists, neurosurgeons, and anesthesiologists, are involved and engaged. Thus, building sustainable relationships through continuous efforts and hands-on training forms an integral part in ensuring rapid and efficient EVT triage.

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.

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.

Combining convolutional attention mechanism and residual deformable Transformer for infarct segmentation from CT scans of acute ischemic stroke patients

Background

Segmentation and evaluation of infarcts on medical images are essential for diagnosis and prognosis of acute ischemic stroke (AIS). Computed tomography (CT) is the first-choice examination for patients with AIS.

Methods

To accurately segment infarcts from the CT images of patients with AIS, we proposed an automated segmentation method combining the convolutional attention mechanism and residual Deformable Transformer in this article. The method used the encoder-decoder structure, where the encoders were employed for downsampling to obtain the feature of the images and the decoder was used for upsampling and segmentation. In addition, we further applied the convolutional attention mechanism and residual network structure to improve the effectiveness of feature extraction. Our code is available at: https://github.com/XZhiXiang/AIS-segmentation/tree/master.

Results

The proposed method was assessed on a public dataset containing 397 non-contrast CT (NCCT) images of AIS patients (AISD dataset). The symptom onset to CT time was less than 24 h. The experimental results illustrate that this work had a Dice coefficient (DC) of 58.66% for AIS infarct segmentation, which outperforms several existing methods. Furthermore, volumetric analysis of infarcts indicated a strong correlation (Pearson correlation coefficient = 0.948) between the AIS infarct volume obtained by the proposed method and manual segmentation.

Conclusion

The strong correlation between the infarct segmentation obtained via our method and the ground truth allows us to conclude that our method could accurately segment infarcts from NCCT images.

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.

DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography

Detecting the early signs of stroke using non-contrast computerized tomography (NCCT) is essential for the diagnosis of acute ischemic stroke (AIS). However, the hypoattenuation in NCCT is difficult to precisely identify, and accurate assessments of the Alberta Stroke Program Early CT Score (ASPECTS) are usually time-consuming and require experienced neuroradiologists. To this end, this study proposes DGA3-Net, a convolutional neural network (CNN)-based model for ASPECTS assessment via detecting early ischemic changes in ASPECTS regions. DGA3-Net is based on a novel parameter-efficient dihedral group CNN encoder to exploit the rotation and reflection symmetry of convolution kernels. The bounding volume of each ASPECTS region is extracted from the encoded feature, and an attention-guided slice aggregation module is used to aggregate features from all slices. An asymmetry-aware classifier is then used to predict stroke presence via comparison between ASPECTS regions from the left and right hemispheres. Pre-treatment NCCTs of suspected AIS patients were collected retrospectively, which consists of a primary dataset (n = 170) and an external validation dataset (n = 90), with expert consensus ASPECTS readings as ground truth. DGA3-Net outperformed two expert neuroradiologists in regional stroke identification (F1 = 0.69) and ASPECTS evaluation (Cohen's weighted Kappa = 0.70). Our ablation study also validated the efficacy of the proposed model design. In addition, class-relevant areas highlighted by visualization techniques corresponded highly with various well-established qualitative imaging signs, further validating the learned representation. This study demonstrates the potential of deep learning techniques for timely and accurate AIS diagnosis from NCCT, which could substantially improve the quality of treatment for AIS patients.

Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic stroke

PURPOSE

Radiomics analysis is a promising image analysis technique. This study aims to extract a radiomics signature from baseline computed tomography (CT) to predict malignant cerebral edema (MCE) in patients with acute anterior circulation infarction after endovascular treatment (EVT).

METHODS

In this retrospective study, 111 patients underwent EVT for acute ischemic stroke caused by middle cerebral artery (MCA) and/or internal carotid artery occlusion. The participants were randomly divided into two datasets: the training set (n = 77) and the test set (n = 34). The clinico-radiological profiles of all patients were collected, including cranial non-contrast-enhanced CT, CT angiography, and CT perfusion. The MCA territory on non-contrast-enhanced CT images was segmented, and the radiomics features associated with MCE were analyzed. The clinico-radiological parameters related to MCE were also identified. In addition, a routine visual radiological model based on radiological factors and a combined model comprising radiomics features and clinico-radiological factors were constructed to predict MCE.

RESULTS

The areas under the curve (AUCs) of the radiomics signature for predicting MCE were 0.870 (P < 0.001) and 0.837 (P = 0.002) in the training and test sets, respectively. The AUCs of the routine visual radiological model were 0.808 (P < 0.001) and 0.813 (P = 0.005) in the training and test sets, respectively. The AUCs of the model combining the radiomics signature and clinico-radiological factors were 0.924 (P < 0.001) and 0.879 (P = 0.001) in the training and test sets, respectively.

CONCLUSION

A CT image-based radiomics signature is a promising tool for predicting MCE in patients with acute anterior circulation infarction after EVT. For clinicians, it may assist in diagnostic decision-making.

Automatic comprehensive aspects reports in clinical acute stroke MRIs

The Alberta Stroke Program Early CT Score (ASPECTS) is a simple visual system to assess the extent and location of ischemic stroke core. The capability of ASPECTS for selecting patients' treatment, however, is affected by the variability in human evaluation. In this study, we developed a fully automatic system to calculate ASPECTS comparable with consensus expert readings. Our system was trained in 400 clinical diffusion weighted images of patients with acute infarcts and evaluated with an external testing set of 100 cases. The models are interpretable, and the results are comprehensive, evidencing the features that lead to the classification. This system adds to our automated pipeline for acute stroke detection, segmentation, and quantification in MRIs (ADS), which outputs digital infarct masks and the proportion of diverse brain regions injured, in addition to the predicted ASPECTS, the prediction probability and the explanatory features. ADS is public, free, accessible to non-experts, has very few computational requirements, and run in real time in local CPUs with a single command line, fulfilling the conditions to perform large-scale, reproducible clinical and translational research.

Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network

Collateral scoring plays an important role in diagnosis and treatment decisions of acute ischemic stroke (AIS). Most existing automated methods rely on vessel prominence and amount after vessel segmentation. The purpose of this study was to design a vessel-segmentation free method for automating collateral scoring on CT angiography (CTA). We first processed the original CTA via maximum intensity projection (MIP) and middle cerebral artery (MCA) region segmentation. The obtained MIP images were fed into our proposed hybrid CNN and Transformer model (MPViT) to automatically determine the collateral scores. We collected 154 CTA scans of patients with AIS for evaluation using five-folder cross validation. Results show that the proposed MPViT achieved an intraclass correlation coefficient of 0.767 (95% CI: 0.68-0.83) and a Kappa of 0.6184 (95% CI: 0.4954-0.7414) for three-point collateral score classification. For dichotomized classification (good vs. non-good and poor vs. non-poor), it also achieved great performance.

Artificial intelligence in emergency radiology: A review of applications and possibilities

Artificial intelligence (AI) applications in radiology have been rising exponentially in the last decade. Although AI has found usage in various areas of healthcare, its utilization in the emergency department (ED) as a tool for emergency radiologists shows great promise towards easing some of the challenges faced daily. There have been numerous reported studies examining the application of AI-based algorithms in identifying common ED conditions to ensure more rapid reporting and in turn quicker patient care. In addition to interpretive applications, AI assists with many of the non-interpretive tasks that are encountered every day by emergency radiologists. These include, but are not limited to, protocolling, image quality control and workflow prioritization. AI continues to face challenges such as physician uptake or costs, but is a long-term investment that shows great potential to relieve many difficulties faced by emergency radiologists and ultimately improve patient outcomes. This review sums up the current advances of AI in emergency radiology, including current diagnostic applications (interpretive) and applications that stretch beyond imaging (non-interpretive), analyzes current drawbacks of AI in emergency radiology and discusses future challenges.

Benefit and risk of intravenous alteplase in patients with acute large vessel occlusion stroke and low ASPECTS

BACKGROUND

The benefit of best medical treatment including intravenous alteplase (IVT) before mechanical thrombectomy (MT) in patients with acute ischemic stroke and extensive early ischemic changes on baseline CT remains uncertain. The purpose of this study was to evaluate the benefit of IVT for patients with low ASPECTS (Alberta Stroke Programme Early CT Score) compared with patients with or without MT.

METHODS

This multicenter study pooled consecutive patients with anterior circulation acute stroke and ASPECTS≤5 to analyze the impact of IVT on functional outcome, and to compare bridging IVT with direct MT. Functional endpoints were the rates of good (modified Rankin Scale (mRS) score ≤2) and very poor (mRS ≥5) outcome at day 90. Safety endpoint was the occurrence of symptomatic intracranial hemorrhage (sICH).

RESULTS

429 patients were included. 290 (68%) received IVT and 168 (39%) underwent MT. The rate of good functional outcome was 14.4% (95% CI 7.1% to 21.8%) for patients who received bridging IVT and 24.4% (95% CI 16.5% to 32.2%) for those who underwent direct MT. The rate of sICH was significantly higher in patients with bridging IVT compared with direct MT (17.8% vs 6.4%, p=0.004). In multivariable logistic regression analysis, IVT was significantly associated with very poor outcome (OR 2.22, 95% CI 1.05 to 4.73, p=0.04) and sICH (OR 3.44, 95% CI 1.18 to 10.07, p=0.02). Successful recanalization, age, and ASPECTS were associated with good functional outcome.

CONCLUSIONS

Bridging IVT in patients with low ASPECTS was associated with very poor functional outcome and an increased risk of sICH. The benefit of this treatment should therefore be carefully weighed in such scenarios. Further randomized controlled trials are required to validate our findings.

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.

Deep Learning-Based Automatic Detection of ASPECTS in Acute Ischemic Stroke: Improving Stroke Assessment on CT Scans

(1) Background: The Alberta Stroke Program Early CT Score (ASPECTS) is a standardized scoring tool used to evaluate the severity of acute ischemic stroke (AIS) on non-contrast CT (NCCT). Our aim in this study was to automate ASPECTS. (2) Methods: We utilized a total of 258 patient images with suspected AIS symptoms. Expert ASPECTS readings on NCCT were used as ground truths. A deep learning-based automatic detection (DLAD) algorithm was developed for automated ASPECTS scoring based on 168 training patient images using a convolutional neural network (CNN) architecture. An additional 90 testing patient images were used to evaluate the performance of the DLAD algorithm, which was then compared with ASPECTS readings on NCCT as performed by physicians. (3) Results: The sensitivity, specificity, and accuracy of DLAD for the prediction of ASPECTS were 65%, 82%, and 80%, respectively. These results demonstrate that the DLAD algorithm was not inferior to radiologist-read ASPECTS on NCCT. With the assistance of DLAD, the individual sensitivity of the ER physician, neurologist, and radiologist improved. (4) Conclusion: The proposed DLAD algorithm exhibits a reasonable ability for ASPECTS scoring on NCCT images in patients presenting with AIS symptoms. The DLAD algorithm could be a valuable tool to improve and accelerate the decision-making process of front-line physicians.

Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities

The value of artificial intelligence (AI) in healthcare has become evident, especially in the field of medical imaging. The accelerated pace and acuity of care in the Emergency Department (ED) has made it a popular target for artificial intelligence-driven solutions. Software that helps better detect, report, and appropriately guide management can ensure high quality patient care while enabling emergency radiologists to better meet the demands of quick turnaround times. Beyond diagnostic applications, AI-based algorithms also have the potential to optimize other important steps within the ED imaging workflow. This review will highlight the different types of AI-based applications currently available for use in the ED, as well as the challenges and opportunities associated with their implementation.

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.

Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography

PURPOSE

Non-contrast computed tomography (NCCT) is a first-line imaging technique for determining treatment options for acute ischemic stroke (AIS). However, its poor contrast and signal-to-noise ratio limit the diagnosis accuracy for radiologists, and automated AIS lesion segmentation using NCCT also remains a challenge. In this paper, we propose R2U-RNet, a novel model for AIS lesion segmentation using NCCT.

METHODS

We used an in-house retrospective NCCT dataset with 261 AIS patients with manual lesion segmentation using follow-up diffusion-weighted images. R2U-RNet is based on an R2U-Net backbone with a novel residual refinement unit. Each input image contains two image channels from separate preprocessing procedures. The proposed model incorporates multiscale focal loss to mitigate the class imbalance problem and to leverage the importance of different levels of details. A proposed noisy-label training scheme is utilized to account for uncertainties in the manual annotations.

RESULTS

The proposed model outperformed several iconic segmentation models in AIS lesion segmentation using NCCT, and our ablation study demonstrated the efficacy of the proposed model. Statistical analysis of segmentation performance revealed significant effects of regional stroke occurrence and side of the stroke, suggesting the importance of region-specific information for automated segmentation, and the potential influence of the hemispheric difference in clinical data.

CONCLUSION

This study demonstrated the potentials of R2U-RNet model for automated NCCT AIS lesion segmentation. The proposed model can serve as a tool for accelerating AIS diagnoses and improving the treatment quality of AIS patients.

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.

Variability assessment of manual segmentations of ischemic lesion volume on 24-h non-contrast CT

PURPOSE

Infarct lesion volume (ILV) may serve as an imaging biomarker for clinical outcomes in the early post-treatment stage in patients with acute ischemic stroke. The aim of this study was to evaluate the inter- and intra-rater reliability of manual segmentation of ILV on follow-up non-contrast CT (NCCT) scans.

METHODS

Fifty patients from the Prove-IT study were randomly selected for this analysis. Three raters manually segmented ILV on 24-h NCCT scans, slice by slice, three times. The reference standard for ILV was generated by the Simultaneous Truth And Performance Level estimation (STAPLE) algorithm. Intra- and inter-rater reliability was evaluated, using metrics of intraclass correlation coefficient (ICC) regarding lesion volume and the Dice similarity coefficient (DSC).

RESULTS

Median age of the 50 subjects included was 74.5 years (interquartile range [IQR] 67-80), 54% were women, median baseline National Institutes of Health Stroke Scale was 18 (IQR 11-22), median baseline ASPECTS was 9 (IQR 6-10). The mean reference standard ILV was 92.5 ml (standard deviation (SD) ± 100.9 ml). The manually segmented ILV ranged from 88.2 ± 91.5 to 135.5 ± 119.9 ml (means referring to the variation between readers, SD within readers). Inter-rater ICC was 0.83 (95%CI: 0.76-0.88); intra-rater ICC ranged from 0.85 (95%CI: 0.72-0.92) to 0.95 (95%CI: 0.91-0.97). The mean DSC among the three readers ranged from 65.5 ± 22.9 to 76.4 ± 17.1% and the mean overall DSC was 72.8 ± 23.0%.

CONCLUSION

Manual ILV measurements on follow-up CT scans are reliable to measure the radiological outcome despite some variability.

The Alberta Stroke Program Early CT score (ASPECTS): A predictor of mortality in acute ischemic stroke

Stroke is one of the leading causes of mortality globally and a main cause of disability. The objective of this study was to evaluate the importance and utility of the Alberta Stroke Program Early CT Score (ASPECTS) as a mortality predictor factor in diabetic vs. non-diabetic patients with acute ischemic stroke (AIS), correlated with age, monocyte values, and high-sensitivity cardiac troponin I (hs-cTnI). The prospective longitudinal observational study included 340 patients with AIS divided into two groups: diabetics and non-diabetics. ASPECTS was evaluated within the first 24 h after admission to the center. The ASPECTS was lower in the group of diabetic patients on average 4.9 vs. 6.05 (P<0.0001). As the age of the patients increased, the lower the ASPECTS and the higher infarct size, indicating a statistically significant (P<0.0001) result. The optimal correlation was observed between infarct size (ASPECTS) and hs-cTnI serum level [95% confidence interval (CI): -0.3216 to -0.1193; P<0.0001]. Almost 94% of patients who had an ASPECTS higher than 3 points on admission survived, resulting in a favorable outcome and a very good predictability of the score (95% CI: 0.85 to 0.926, P<0.0001). The ASPECTS is a mortality predictor, its value correlating inversely with the severity and evolution of patients, confirming a good predictability with good specificity, sensitivity and area under the curve.

Automated Pathology Detection and Patient Triage in Routinely Acquired Head Computed Tomography Scans

OBJECTIVES

Anomaly detection systems can potentially uncover the entire spectrum of pathologies through deviations from a learned norm, meaningfully supporting the radiologist's workflow. We aim to report on the utility of a weakly supervised machine learning (ML) tool to detect pathologies in head computed tomography (CT) and adequately triage patients in an unselected patient cohort.

MATERIALS AND METHODS

All patients having undergone a head CT at a tertiary care hospital in March 2020 were eligible for retrospective analysis. Only the first scan of each patient was included. Anomaly detection was performed using a weakly supervised ML technique. Anomalous findings were displayed on voxel-level and pooled to an anomaly score ranging from 0 to 1. Thresholds for this score classified patients into the 3 classes: "normal," "pathological," or "inconclusive." Expert-validated radiological reports with multiclass pathology labels were considered as ground truth. Test assessment was performed with receiver operator characteristics analysis; inconclusive results were pooled to "pathological" predictions for accuracy measurements. External validity was tested in a publicly available external data set (CQ500).

RESULTS

During the investigation period, 297 patients were referred for head CT of which 248 could be included. Definite ratings into normal/pathological were feasible in 167 patients (67.3%); 81 scans (32.7%) remained inconclusive. The area under the curve to differentiate normal from pathological scans was 0.95 (95% confidence interval, 0.92-0.98) for the study data set and 0.87 (95% confidence interval, 0.81-0.94) in external validation. The negative predictive value to exclude pathology if a scan was classified as "normal" was 100% (25/25), and the positive predictive value was 97.6% (137/141). Sensitivity and specificity were 100% and 86%, respectively. In patients with inconclusive ratings, pathologies were found in 26 (63%) of 41 cases.

CONCLUSIONS

Our study provides the first clinical evaluation of a weakly supervised anomaly detection system for brain imaging. In an unselected, consecutive patient cohort, definite classification into normal/diseased was feasible in approximately two thirds of scans, going along with an excellent diagnostic accuracy and perfect negative predictive value for excluding pathology. Moreover, anomaly heat maps provide important guidance toward pathology interpretation, also in cases with inconclusive ratings.

Artificial Intelligence shaping the future of neurology practice

Neurology practice has faced many challenges since Jean-Martin Charcot established its sacred tenets. Artificial Intelligence (AI) promises to revolutionize the time-tested neurology practice in unimaginable ways. AI can now diagnose stroke from CT/MRI scans, detect papilledema and diabetic retinopathy from retinal scans, interpret electroencephalogram (EEG) to prognosticate coma, detect seizure well before ictus, predict conversion of mild cognitive impairment to Alzheimer's dementia, classify neurodegenerative diseases based on gait and handwriting. Clinical practice would likely change in near future to accommodate AI as a complementary tool. The clinician should be prepared to change the perception of AI from nemesis to opportunity.

Predictive values of early head computed tomography for survival outcome after cardiac arrest in childhood: a pilot study

Predicting outcomes of children after cardiac arrest (CA) remains challenging. To identify useful prognostic markers for pediatric CA, we retrospectively analyzed the early findings of head computed tomography (CT) of patients. Subjects were non-traumatic, out-of-hospital CA patients < 16 years of age who underwent the first head CT within 24 h in our institute from 2006 to 2018 (n = 70, median age: 4 months, range 0–163). Of the 24 patients with return of spontaneous circulation, 14 survived up to 30 days after CA. The degree of brain damage was quantitatively measured with modified methods of the Alberta Stroke Program Early CT Score (mASPECTS) and simplified gray-matter-attenuation-to-white-matter-attenuation ratio (sGWR). The 14 survivors showed higher mASPECTS values than the 56 non-survivors (p = 0.035). All 3 patients with mASPECTS scores ≥ 20 survived, while an sGWR ≥ 1.14 indicated a higher chance of survival than an sGWR < 1.14 (54.5% vs. 13.6%). Follow-up magnetic resonance imaging for survivors validated the correlation of the mASPECTS < 15 with severe brain damage. Thus, low mASPECTS scores were associated with unfavorable neurological outcomes on the Pediatric Cerebral Performance Category scale. A quantitative analysis of early head CT findings might provide clues for predicting survival of pediatric CA.

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.

CT perfusion based ASPECTS improves the diagnostic performance of early ischemic changes in large vessel occlusion

Background

ASPECTS scoring method varies, but which one is most suitable for predicting the prognosis still unclear. We aimed to evaluate the diagnostic performance of Automated (Auto)-, noncontrast CT (NCCT)- and CT perfusion (CTP) -ASPECTS for early ischemic changes (EICs) in acute ischemic stroke patients with large vessel occlusion (LVO) and to explore which scoring method is most suitable for predicting the clinical outcome.

Methods

Eighty-one patients with anterior circulation LVO were retrospectively enrolled and grouped as having a good (0–2) or poor (3–6) clinical outcome using a 90-day modified Rankin Scale score. Clinical characteristics and perfusion parameters were compared between the patients with good and poor outcomes. Differences in scores obtained with the three scoring methods were assessed. Diagnosis performance and receiver operating characteristic (ROC) curves were used to evaluate the value of the three ordinal or dichotomized ASPECTS methods for predicting the clinical outcome.

Results

Sixty-three patients were finally included, with 36 (57.1%) patients having good clinical outcome. Significant differences were observed in the ordinal or dichotomized Auto-, NCCT- and CTP-ASPECTS between the patients with good and poor clinical outcomes (all p < 0.01). The areas under the curves (AUCs) of the ordinal and dichotomized CTP-ASPECTS were higher than that of the other two methods (all p < 0.01), but the AUCs of the Auto-ASPECTS was similar to that of the NCCT-ASPECTS (p > 0.05).

Conclusions

The CTP-ASPECTS is superior to the Auto- and NCCT-ASPECTS in detecting EICs in LVO. CTP-ASPECTS with a cutoff value of 6 is a good predictor of the clinical outcome at 90-day follow-up.

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.

Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods

Noncontrast Computed Tomography (NCCT) of the brain has been the first-line diagnosis for emergency evaluation of acute stroke, so a rapid and automated detection, localization, and/or segmentation of ischemic lesions is of great importance. We provide the state-of-the-art review of methods for automated detection, localization, and/or segmentation of ischemic lesions on NCCT in human brain scans along with their comparison, evaluation, and classification. Twenty-two methods are (1) reviewed and evaluated; (2) grouped into image processing and analysis-based methods (11 methods), brain atlas-based methods (two methods), intensity template-based methods (1 method), Stroke Imaging Marker-based methods (two methods), and Artificial Intelligence-based methods (six methods); and (3) properties of these groups of methods are characterized. A new method classification scheme is proposed as a 2 × 2 matrix with local versus global processing and analysis, and density versus spatial sampling. Future studies are necessary to develop more efficient methods directed toward deep learning methods as well as combining the global methods with a high sampling both in space and density for the merged radiologic and neurologic data.

Artificial Intelligence and Acute Stroke Imaging

Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence-driven applications for acute stroke triage, surveillance, and prediction.

Role of Artificial Intelligence in TeleStroke: An Overview

Teleneurology has provided access to neurological expertise and state-of-the-art stroke care where previously they have been inaccessible. The use of Artificial Intelligence with machine learning to assist telestroke care can be revolutionary. This includes more rapid and more reliable diagnosis through imaging analysis as well as prediction of hospital course and 3-month prognosis. Intelligent Electronic Medical Records can search free text and provide decision assistance by analyzing patient charts. Speech recognition has advanced enough to be reliable and highly convenient. Smart contextually aware communication and alert programs can enhance efficiency of patient flow and improve outcomes. Automated data collection and analysis can make quality improvement and research projects quicker and much less burdensome. Despite current challenges, these synergistic technologies hold immense promise in enhancing the clinician experience, helping to reduce physician burnout while improving patient health outcomes at a lower cost. This brief overview discusses the multifaceted potential of AI use in telestroke.

Automatic Assessment of ASPECTS Using Diffusion-Weighted Imaging in Acute Ischemic Stroke Using Recurrent Residual Convolutional Neural Network

The early detection and rapid quantification of acute ischemic lesions play pivotal roles in stroke management. We developed a deep learning algorithm for the automatic binary classification of the Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) using diffusion-weighted imaging (DWI) in acute stroke patients. Three hundred and ninety DWI datasets with acute anterior circulation stroke were included. A classifier algorithm utilizing a recurrent residual convolutional neural network (RRCNN) was developed for classification between low (1–6) and high (7–10) DWI-ASPECTS groups. The model performance was compared with a pre-trained VGG16, Inception V3, and a 3D convolutional neural network (3DCNN). The proposed RRCNN model demonstrated higher performance than the pre-trained models and 3DCNN with an accuracy of 87.3%, AUC of 0.941, and F1-score of 0.888 for classification between the low and high DWI-ASPECTS groups. These results suggest that the deep learning algorithm developed in this study can provide a rapid assessment of DWI-ASPECTS and may serve as an ancillary tool that can assist physicians in making urgent clinical decisions.

Artificial intelligence in stroke imaging: Current and future perspectives

Artificial intelligence (AI) is a fast-growing research area in computer science that aims to mimic cognitive processes through a number of techniques. Supervised machine learning, a subfield of AI, includes methods that can identify patterns in high-dimensional data using labeled 'ground truth' data and apply these learnt patterns to analyze, interpret, or make predictions on new datasets. Supervised machine learning has become a significant area of interest within the medical community. Radiology and neuroradiology in particular are especially well suited for application of machine learning due to the vast amount of data that is generated. One devastating disease for which neuroimaging plays a significant role in the clinical management is stroke. Within this context, AI techniques can play pivotal roles for image-based diagnosis and management of stroke. This overview focuses on the recent advances of artificial intelligence methods - particularly supervised machine learning and deep learning - with respect to workflow, image acquisition and reconstruction, and image interpretation in patients with acute stroke, while also discussing potential pitfalls and future applications.

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.

CT for Treatment Selection in Acute Ischemic Stroke: A Code Stroke Primer

CT is the primary imaging modality used for selecting appropriate treatment in patients with acute stroke. Awareness of the typical findings, pearls, and pitfalls of CT image interpretation is therefore critical for radiologists, stroke neurologists, and emergency department providers to make accurate and timely decisions regarding both (a) immediate treatment with intravenous tissue plasminogen activator up to 4.5 hours after a stroke at primary stroke centers and (b) transfer of patients with large-vessel occlusion (LVO) at CT angiography to comprehensive stroke centers for endovascular thrombectomy (EVT) up to 24 hours after a stroke. Since the DAWN and DEFUSE 3 trials demonstrated the efficacy of EVT up to 24 hours after last seen well, CT angiography has become the operational standard for rapid accurate identification of intracranial LVO. A systematic approach to CT angiographic image interpretation is necessary and useful for rapid triage, and understanding common stroke syndromes can help speed vessel evaluation. Moreover, when diffusion-weighted MRI is unavailable, multiphase CT angiography of collateral vessels and source-image assessment or perfusion CT can be used to help estimate core infarct volume. Both have the potential to allow distinction of patients likely to benefit from EVT from those unlikely to benefit. This article reviews CT-based workup of ischemic stroke for making tPA and EVT treatment decisions and focuses on practical skills, interpretation challenges, mimics, and pitfalls.^©RSNA, 2019.

Automatic collateral circulation scoring in ischemic stroke using 4D CT angiography with low-rank and sparse matrix decomposition

PURPOSE

Sufficient collateral blood supply is crucial for favorable outcomes with endovascular treatment. The current practice of collateral scoring relies on visual inspection and thus can suffer from inter and intra-rater inconsistency. We present a robust and automatic method to score cerebral collateral blood supply to aid ischemic stroke treatment decision making. The developed method is based on 4D dynamic CT angiography (CTA) and the ASPECTS scoring protocol.

METHODS

The proposed method, ACCESS (Automatic Collateral Circulation Evaluation in iSchemic Stroke), estimates a target patient's unfilled cerebrovasculature in contrast-enhanced CTA using the lack of contrast agent due to clotting. To do so, the fast robust matrix completion algorithm with in-face extended Frank-Wolfe optimization is applied on a cohort of healthy subjects and a target patient, to model the patient's unfilled vessels and the estimated full vasculature as sparse and low-rank components, respectively. The collateral score is computed as the ratio of the unfilled vessels to the full vasculature, mimicking existing clinical protocols.

RESULTS

ACCESS was tested with 46 stroke patients and obtained an overall accuracy of 84.78%. The optimal threshold selection was evaluated using a receiver operating characteristics curve with the leave-one-out approach, and a mean area under the curve of 85.39% was obtained.

CONCLUSION

ACCESS automates collateral scoring to mitigate the shortcomings of the standard clinical practice. It is a robust approach, which resembles how radiologists score clinical scans, and can be used to help radiologists in clinical decisions of stroke treatment.