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Mak A, Matouk CC, Avery EW, Behland J, Haider SP, Frey D, Madai VI, Vajkoczy P, Griessenauer CJ, Zand R, Hendrix P, Abedi V, Sanelli PC, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S, Malhotra A. Automated detection of early signs of irreversible ischemic change on CTA source images in patients with large vessel occlusion. PLoS One 2024; 19:e0304962. [PMID: 38870240 PMCID: PMC11175522 DOI: 10.1371/journal.pone.0304962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/21/2024] [Indexed: 06/15/2024] Open
Abstract
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.
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Affiliation(s)
- Adrian Mak
- Department of Radiology and Biomedical Imaging, Section of Neuroradiology, Yale School of Medicine, New Haven, CT, United States of America
- CLAIM—Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Charles C. Matouk
- Department of Neurosurgery, Division of Neurovascular Surgery, Yale University School of Medicine, New Haven, CT, United States of America
| | - Emily W. Avery
- Department of Radiology and Biomedical Imaging, Section of Neuroradiology, Yale School of Medicine, New Haven, CT, United States of America
| | - Jonas Behland
- Department of Radiology and Biomedical Imaging, Section of Neuroradiology, Yale School of Medicine, New Haven, CT, United States of America
- CLAIM—Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Stefan P. Haider
- Department of Radiology and Biomedical Imaging, Section of Neuroradiology, Yale School of Medicine, New Haven, CT, United States of America
- Department of Otorhinolaryngology, LMU Clinic of Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Dietmar Frey
- CLAIM—Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Vince I. Madai
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
- School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph J. Griessenauer
- Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria
- Department of Neurosurgery, Paracelsus Medical University, Salzburg, Austria
| | - Ramin Zand
- Department of Neurology, Geisinger Medical Center, Danville, PA, United States of America
- Department of Neurology, Pennsylvania State University, State College, PA, United States of America
| | - Philipp Hendrix
- Department of Neurosurgery, Geisinger Medical Center, Danville, PA, United States of America
- Department of Neurosurgery, Saarland University Medical Center, Homburg, Germany
| | - Vida Abedi
- Department of Public Health Sciences, The Pennsylvania State University, Hershey, PA, United States of America
- Department of Molecular and Functional Genomics, Geisinger Medical Center, Danville, PA, United States of America
| | - Pina C. Sanelli
- Department of Radiology, Northwell Health Feinstein Institutes for Medical Research, Manhasset, New York, United States of America
| | - Guido J. Falcone
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale University School of Medicine, New Haven, CT, United States of America
| | - Nils Petersen
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale University School of Medicine, New Haven, CT, United States of America
| | - Lauren H. Sansing
- Division of Stroke and Vascular Neurology, Department of Neurology, Yale University School of Medicine, New Haven, CT, United States of America
| | - Kevin N. Sheth
- Division of Stroke and Vascular Neurology, Department of Neurology, Yale University School of Medicine, New Haven, CT, United States of America
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Section of Neuroradiology, Yale School of Medicine, New Haven, CT, United States of America
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Section of Neuroradiology, Yale School of Medicine, New Haven, CT, United States of America
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Westwood M, Ramaekers B, Grimm S, Armstrong N, Wijnen B, Ahmadu C, de Kock S, Noake C, Joore M. 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. Health Technol Assess 2024; 28:1-204. [PMID: 38512017 PMCID: PMC11017149 DOI: 10.3310/rdpa1487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024] Open
Abstract
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.
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Affiliation(s)
| | - Bram Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
| | | | | | - Ben Wijnen
- Kleijnen Systematic Reviews (KSR) Ltd, York, UK
| | | | | | - Caro Noake
- Kleijnen Systematic Reviews (KSR) Ltd, York, UK
| | - Manuela Joore
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
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Wan S, Lu W, Fu Y, Wang M, Liu K, Chen S, Chen W, Wang Y, Wu J, Leng X, Fiehler J, Siddiqui AH, Guan S, Xiang J. Automated ASPECTS calculation may equal the performance of experienced clinicians: a machine learning study based on a large cohort. Eur Radiol 2024; 34:1624-1634. [PMID: 37658137 DOI: 10.1007/s00330-023-10053-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 05/15/2023] [Accepted: 06/22/2023] [Indexed: 09/03/2023]
Abstract
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.
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Affiliation(s)
- Shu Wan
- Brain Center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Lu
- ArteryFlow Technology Co., Ltd., Hangzhou, China
| | - Yu Fu
- Department of Neurointervention Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming Wang
- Brain Center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kaizheng Liu
- ArteryFlow Technology Co., Ltd., Hangzhou, China
| | - Sijing Chen
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Wubiao Chen
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Yang Wang
- Department of Neurosurgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Jun Wu
- Department of Neurology, Qingtian County People's Hospital, Lishui, China
| | | | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Adnan H Siddiqui
- Departments of Neurosurgery and Radiology, University at Buffalo, Buffalo, NY, USA
| | - Sheng Guan
- Department of Neurointervention Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Pacchiano F, Tortora M, Criscuolo S, Jaber K, Acierno P, De Simone M, Tortora F, Briganti F, Caranci F. Artificial intelligence applied in acute ischemic stroke: from child to elderly. LA RADIOLOGIA MEDICA 2024; 129:83-92. [PMID: 37878222 PMCID: PMC10808481 DOI: 10.1007/s11547-023-01735-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 09/28/2023] [Indexed: 10/26/2023]
Abstract
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.
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Affiliation(s)
- Francesco Pacchiano
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Caserta, Italy
| | - Mario Tortora
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 5, 80131, Naples, Italy.
| | - Sabrina Criscuolo
- Pediatric University Department, Bambino Gesù Children Hospital, Rome, Italy
| | - Katya Jaber
- Department of Elektrotechnik und Informatik, Hochschule Bremen, Bremen, Germany
| | | | - Marta De Simone
- UOC Neuroradiology, AORN San Giuseppe Moscati, Avellino, Italy
| | - Fabio Tortora
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 5, 80131, Naples, Italy
| | - Francesco Briganti
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 5, 80131, Naples, Italy
| | - Ferdinando Caranci
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Caserta, Italy
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Río Bártulos C, Pirl L, Lier D, Planert M, Hohmann J, El Mountassir A, El Anwar M, Wiggermann P. Performance evaluation of two different software programs for automated ASPECTS scoring in patients with suspected stroke. Clin Hemorheol Microcirc 2024; 86:109-119. [PMID: 37638425 DOI: 10.3233/ch-238105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
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.
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Affiliation(s)
- Carolina Río Bártulos
- Insitut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
| | - Lukas Pirl
- Insitut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
| | - Dennis Lier
- Insitut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
| | - Mathis Planert
- Insitut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
| | - Juliane Hohmann
- Insitut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
| | - Abdelouahed El Mountassir
- Insitut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
| | - Mohamed El Anwar
- Insitut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
| | - Philipp Wiggermann
- Insitut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
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Mallon D, Fallon M, Blana E, McNamara C, Menon A, Ip CL, Garnham J, Yousry T, Cowley P, Simister R, Doig D. Real-world evaluation of Brainomix e-Stroke software. Stroke Vasc Neurol 2023:svn-2023-002859. [PMID: 38164621 DOI: 10.1136/svn-2023-002859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
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.
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Affiliation(s)
- Dermot Mallon
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- UCL Queen Square Institute of Neurology, London, UK
| | - Matthew Fallon
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Eirini Blana
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Cillian McNamara
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Arathi Menon
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Chak Lam Ip
- Comprehensive Stroke Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - Jack Garnham
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Tarek Yousry
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- UCL Queen Square Institute of Neurology, London, UK
| | - Peter Cowley
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Robert Simister
- UCL Queen Square Institute of Neurology, London, UK
- Comprehensive Stroke Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - David Doig
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- UCL Queen Square Institute of Neurology, London, UK
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7
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Lee SJ, Park G, Kim D, Jung S, Song S, Hong JM, Shin DH, Lee JS. Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT. J Neurointerv Surg 2023; 16:61-66. [PMID: 37015781 PMCID: PMC10804033 DOI: 10.1136/jnis-2022-019970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 03/01/2023] [Indexed: 04/06/2023]
Abstract
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.
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Affiliation(s)
- Seong-Joon Lee
- Department of Neurology, Ajou University School of Medicine, Suwon, Gyeonggi-do, South Korea
| | - Gyuha Park
- Research Division, Heuron Co., Ltd, Incheon, South Korea
| | - Dohyun Kim
- Research Division, Heuron Co., Ltd, Incheon, South Korea
| | - Sumin Jung
- Research Division, Heuron Co., Ltd, Incheon, South Korea
| | - Soohwa Song
- Research Division, Heuron Co., Ltd, Incheon, South Korea
| | - Ji Man Hong
- Department of Neurology, Ajou University School of Medicine, Suwon, Gyeonggi-do, South Korea
| | - Dong Hoon Shin
- Research Division, Heuron Co., Ltd, Incheon, South Korea
- Department of Neurology, Gachon University College of Medicine, Incheon, South Korea
| | - Jin Soo Lee
- Department of Neurology, Ajou University School of Medicine, Suwon, Gyeonggi-do, South Korea
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Lim GZ, Lai JY, Seet CYH, Tham CH, Venketasubramanian N, Tan BYQ, Jing M, Yeo JYP, Myint MZ, Sia CH, Teoh HL, Sharma VK, Chan BPL, Yang C, Makmur A, Ong SJ, Yeo LLL. Revolutionizing the Management of Large-Core Ischaemic Strokes: Decoding the Success of Endovascular Therapy in the Recent Stroke Trials. J Cardiovasc Dev Dis 2023; 10:499. [PMID: 38132666 PMCID: PMC10743836 DOI: 10.3390/jcdd10120499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/02/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023] Open
Abstract
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.
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Affiliation(s)
- Gareth Zigui Lim
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Jonathan Yexian Lai
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Christopher Ying Hao Seet
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Carol Huilian Tham
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | | | - Benjamin Yong Qiang Tan
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Mingxue Jing
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Joshua Yee Peng Yeo
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - May Zin Myint
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Ching-Hui Sia
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
- Department of Cardiology, National University Heart Center, Singapore 119228, Singapore
| | - Hock Luen Teoh
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Vijay Kumar Sharma
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Bernard Poon Lap Chan
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Cunli Yang
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Department of Diagnostic Imaging, National University Health System, Singapore 119228, Singapore
| | - Andrew Makmur
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Department of Diagnostic Imaging, National University Health System, Singapore 119228, Singapore
| | - Shao Jin Ong
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Department of Diagnostic Imaging, National University Health System, Singapore 119228, Singapore
| | - Leonard Leong Litt Yeo
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Department of Diagnostic Imaging, National University Health System, Singapore 119228, Singapore
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9
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Oliveira G, Fonseca AC, Ferro J, Oliveira AL. Deep Learning-Based Extraction of Biomarkers for the Prediction of the Functional Outcome of Ischemic Stroke Patients. Diagnostics (Basel) 2023; 13:3604. [PMID: 38132189 PMCID: PMC10743068 DOI: 10.3390/diagnostics13243604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/26/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023] Open
Abstract
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.
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Affiliation(s)
- Gonçalo Oliveira
- NeuralShift, 1000-138 Lisbon, Portugal
- INESC-ID, Instituto Superior Técnico, 1000-029 Lisbon, Portugal
| | - Ana Catarina Fonseca
- Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal; (A.C.F.); (J.F.)
| | - José Ferro
- Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal; (A.C.F.); (J.F.)
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10
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Adamou A, Beltsios ET, Bania A, Gkana A, Kastrup A, Chatziioannou A, Politi M, Papanagiotou P. Artificial intelligence-driven ASPECTS for the detection of early stroke changes in non-contrast CT: a systematic review and meta-analysis. J Neurointerv Surg 2023; 15:e298-e304. [PMID: 36522179 DOI: 10.1136/jnis-2022-019447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/20/2022] [Indexed: 12/23/2022]
Abstract
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.
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Affiliation(s)
- Antonis Adamou
- Department of Radiology, University of Thessaly, School of Health Sciences, Larissa, Greece
| | - Eleftherios T Beltsios
- Department of Thoracic and Cardiovascular Surgery, West German Heart and Vascular Center, University of Duisburg-Essen, Essen, Germany
| | - Angelina Bania
- Faculty of Medicine, University of Patras, School of Health Sciences, Patras, Greece
| | - Androniki Gkana
- Deparment of Radiology, Ippokratio Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Kastrup
- Department of Neurology, Hospital Bremen-Mitte GmbH, Bremen, Germany
| | - Achilles Chatziioannou
- Department of Radiology, Areteion University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Maria Politi
- Interventional Radiology Unit, Evangelismos General Hospital, Athens, Greece
- Department of Diagnostic and Interventional Neuroradiology, Hospital Bremen-Mitte GmbH, Bremen, Germany
| | - Panagiotis Papanagiotou
- Department of Radiology, Areteion University Hospital, National and Kapodistrian University of Athens, Athens, Greece
- Department of Diagnostic and Interventional Neuroradiology, Hospital Bremen-Mitte GmbH, Bremen, Germany
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11
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Kobeissi H, Kallmes DF, Benson J, Nagelschneider A, Madhavan A, Messina SA, Schwartz K, Campeau N, Carr CM, Nasr DM, Braksick S, Scharf EL, Klaas J, Woodhead ZVJ, Harston G, Briggs J, Joly O, Gerry S, Kuhn AL, Kostas AA, Nael K, AbdalKader M, Kadirvel R, Brinjikji W. Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study. Front Neurol 2023; 14:1221255. [PMID: 37745671 PMCID: PMC10513025 DOI: 10.3389/fneur.2023.1221255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/14/2023] [Indexed: 09/26/2023] Open
Abstract
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.
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Affiliation(s)
- Hassan Kobeissi
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - David F. Kallmes
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - John Benson
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | | | - Ajay Madhavan
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | | | - Kara Schwartz
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Norbert Campeau
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Carrie M. Carr
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Deena M. Nasr
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Sherri Braksick
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Eugene L. Scharf
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - James Klaas
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | | | - George Harston
- Brainomix Limited, Oxford, United Kingdom
- Acute Stroke Service, Oxford University Hospitals NHSFT, Oxford, United Kingdom
| | - James Briggs
- Brainomix Limited, Oxford, United Kingdom
- Royal Berkshire NHS Foundation Trust, Reading, United Kingdom
| | | | - Stephen Gerry
- Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Anna L. Kuhn
- Division of Neurointerventional Radiology, Department of Radiology, UMass Medical Center, Worcester, MA, United States
| | - Angelos A. Kostas
- Huntington Hospital and Hill Medical Imaging, Pasadena, CA, United States
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA, United States
| | - Mohamad AbdalKader
- Department of Radiology, Boston Medical Center, Boston, MA, United States
| | - Ramanathan Kadirvel
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
| | - Waleed Brinjikji
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
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12
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Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, Smith TR. FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval. Artif Intell Med 2023; 143:102607. [PMID: 37673576 DOI: 10.1016/j.artmed.2023.102607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
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.
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Affiliation(s)
- Alexander G Yearley
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Caroline M W Goedmakers
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands
| | - Armon Panahi
- The George Washington University School of Medicine and Health Sciences, 2300 I St NW, Washington, DC 20052, USA
| | - Joanne Doucette
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; School of Pharmacy, MCPHS University, 179 Longwood Ave, Boston, MA 02115, USA
| | - Aakanksha Rana
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Kavitha Ranganathan
- Division of Plastic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA
| | - Timothy R Smith
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
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13
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Kawano H, Hirano T. Minimal Imaging Requirements. JOURNAL OF NEUROENDOVASCULAR THERAPY 2023; 17:243-256. [PMID: 38025254 PMCID: PMC10657732 DOI: 10.5797/jnet.ra.2023-0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/09/2023] [Indexed: 12/01/2023]
Abstract
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.
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Affiliation(s)
- Hiroyuki Kawano
- Department of Stroke and Cerebrovascular Medicine, Kyorin University, Mitaka, Tokyo, Japan
| | - Teruyuki Hirano
- Department of Stroke and Cerebrovascular Medicine, Kyorin University, Mitaka, Tokyo, Japan
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14
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Wagner DT, Tilmans L, Peng K, Niedermeier M, Rohl M, Ryan S, Yadav D, Takacs N, Garcia-Fraley K, Koso M, Dikici E, Prevedello LM, Nguyen XV. Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges. Diagnostics (Basel) 2023; 13:2670. [PMID: 37627929 PMCID: PMC10453240 DOI: 10.3390/diagnostics13162670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
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.
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Affiliation(s)
- Daniel T. Wagner
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luke Tilmans
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Kevin Peng
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Matt Rohl
- College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Sean Ryan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Divya Yadav
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Noah Takacs
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Krystle Garcia-Fraley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Mensur Koso
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Engin Dikici
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Xuan V. Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
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15
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Lambert J, Demeestere J, Dewachter B, Cockmartin L, Wouters A, Symons R, Boomgaert L, Vandewalle L, Scheldeman L, Demaerel P, Lemmens R. Performance of Automated ASPECTS Software and Value as a Computer-Aided Detection Tool. AJNR Am J Neuroradiol 2023; 44:894-900. [PMID: 37500286 PMCID: PMC10411841 DOI: 10.3174/ajnr.a7956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/14/2023] [Indexed: 07/29/2023]
Abstract
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.
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Affiliation(s)
- J Lambert
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Departments of Imaging and Pathology (J.L., B.D., P.D.)
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
| | - J Demeestere
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - B Dewachter
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Departments of Imaging and Pathology (J.L., B.D., P.D.)
| | - L Cockmartin
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
| | - A Wouters
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - R Symons
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Imelda Hospital (R.S.), Bonheiden, Belgium
| | - L Boomgaert
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
| | - L Vandewalle
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - L Scheldeman
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - P Demaerel
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Departments of Imaging and Pathology (J.L., B.D., P.D.)
| | - R Lemmens
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
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16
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Wu Y, Sun R, Xie Y, Nie S. Automatic Alberta Stroke Program Early Computed Tomographic Scoring in patients with acute ischemic stroke using diffusion-weighted imaging. Med Biol Eng Comput 2023:10.1007/s11517-023-02867-2. [PMID: 37347402 DOI: 10.1007/s11517-023-02867-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 06/23/2023]
Abstract
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.
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Affiliation(s)
- Yan Wu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai, 200093, China
| | - Rong Sun
- School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai, 200093, China
| | - Yuanzhong Xie
- Medical Imaging Center, Tai'an Central Hospital, Shandong, China
| | - Shengdong Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai, 200093, China.
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Chandrabhatla AS, Kuo EA, Sokolowski JD, Kellogg RT, Park M, Mastorakos P. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies. J Clin Med 2023; 12:jcm12113755. [PMID: 37297949 DOI: 10.3390/jcm12113755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
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.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Elyse A Kuo
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Jennifer D Sokolowski
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Ryan T Kellogg
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Min Park
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Panagiotis Mastorakos
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, Thomas Jefferson University Hospital, 111 S 11th Street, Philadelphia, PA 19107, USA
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18
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Chan N, Sibtain N, Booth T, de Souza P, Bibby S, Mah YH, Teo J, U-King-Im JM. Machine-learning algorithm in acute stroke: real-world experience. Clin Radiol 2023; 78:e45-e51. [PMID: 36411087 DOI: 10.1016/j.crad.2022.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 10/04/2022] [Accepted: 10/15/2022] [Indexed: 11/19/2022]
Abstract
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.
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Affiliation(s)
- N Chan
- Department of Neuroradiology, King's College Hospital, London, UK; Department of Interventional Neuroradiology, The Royal London Hospital, London, UK.
| | - N Sibtain
- Department of Neuroradiology, King's College Hospital, London, UK
| | - T Booth
- Department of Neuroradiology, King's College Hospital, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - P de Souza
- Department of Neuroradiology, The Royal London Hospital, London, UK
| | - S Bibby
- Department of Neuroradiology, King's College Hospital, London, UK
| | - Y-H Mah
- Department of Neurology, King's College Hospital, London, UK
| | - J Teo
- Department of Neurology, King's College Hospital, London, UK
| | - J M U-King-Im
- Department of Neuroradiology, King's College Hospital, London, UK
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19
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Sheth SA, Giancardo L, Colasurdo M, Srinivasan VM, Niktabe A, Kan P. Machine learning and acute stroke imaging. J Neurointerv Surg 2023; 15:195-199. [PMID: 35613840 PMCID: PMC10523646 DOI: 10.1136/neurintsurg-2021-018142] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 05/08/2022] [Indexed: 01/14/2023]
Abstract
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.
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Affiliation(s)
- Sunil A Sheth
- Department of Neurology, UTHealth McGovern Medical School, Houston, Texas, USA
| | - Luca Giancardo
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Marco Colasurdo
- Department of Neurosurgery, The University of Texas Medical Branch at Galveston, Galveston, Texas, USA
- Department of Neuroradiology, The University of Texas Medical Branch at Galveston, Galveston, Texas, USA
| | - Visish M Srinivasan
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Arash Niktabe
- Department of Neurology, UTHealth McGovern Medical School, Houston, Texas, USA
| | - Peter Kan
- Department of Neurosurgery, The University of Texas Medical Branch at Galveston, Galveston, Texas, USA
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20
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Jabal MS, Kallmes DF, Harston G, Campeau N, Schwartz K, Messina S, Carr C, Benson J, Little J, Nagelschneider A, Madhavan A, Nasr D, Braksick S, Klaas J, Scharf E, Bilgin C, Brinjikji W. Automated CT angiography collateral scoring in anterior large vessel occlusion stroke: A multireader study. Interv Neuroradiol 2023:15910199221150470. [PMID: 36650942 DOI: 10.1177/15910199221150470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
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.
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Affiliation(s)
| | - David F Kallmes
- Department of Radiology, 6915Mayo Clinic, Rochester, MN, USA
| | - George Harston
- Brainomix Limited, Oxford, UK
- 6397Oxford University Hospitals, NHS Foundation Trust, Oxford, UK
| | - Norbert Campeau
- Department of Radiology, 6915Mayo Clinic, Rochester, MN, USA
| | - Kara Schwartz
- Department of Radiology, 6915Mayo Clinic, Rochester, MN, USA
| | - Steven Messina
- Department of Radiology, 6915Mayo Clinic, Rochester, MN, USA
| | - Carrie Carr
- Department of Radiology, 6915Mayo Clinic, Rochester, MN, USA
| | - John Benson
- Department of Radiology, 6915Mayo Clinic, Rochester, MN, USA
| | - Jason Little
- Department of Radiology, 6915Mayo Clinic, Rochester, MN, USA
| | | | - Ajay Madhavan
- Department of Radiology, 6915Mayo Clinic, Rochester, MN, USA
| | - Deena Nasr
- Department of Neurology, 6915Mayo Clinic, Rochester, MN, USA
| | - Sherry Braksick
- Department of Neurology, 6915Mayo Clinic, Rochester, MN, USA
| | - James Klaas
- Department of Neurology, 6915Mayo Clinic, Rochester, MN, USA
| | - Eugene Scharf
- Department of Neurology, 6915Mayo Clinic, Rochester, MN, USA
| | - Cem Bilgin
- Department of Radiology, 6915Mayo Clinic, Rochester, MN, USA
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21
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Mair G, White P, Bath PM, Muir KW, Al‐Shahi Salman R, Martin C, Dye D, Chappell FM, Vacek A, von Kummer R, Macleod M, Sprigg N, Wardlaw JM. External Validation of e-ASPECTS Software for Interpreting Brain CT in Stroke. Ann Neurol 2022; 92:943-957. [PMID: 36053916 PMCID: PMC9826303 DOI: 10.1002/ana.26495] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 07/08/2022] [Accepted: 08/29/2022] [Indexed: 01/11/2023]
Abstract
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.
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Affiliation(s)
- Grant Mair
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Philip White
- Translational and Clinical Research InstituteNewcastle University and Newcastle upon Tyne Hospitals NHS TrustNewcastle upon TyneUK
| | - Philip M. Bath
- Stroke Trials Unit, Mental Health & Clinical NeuroscienceUniversity of NottinghamNottinghamUK
| | - Keith W. Muir
- School of Psychology & NeuroscienceUniversity of GlasgowGlasgowUK
| | | | - Chloe Martin
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - David Dye
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | | | - Adam Vacek
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Rüdiger von Kummer
- Department of NeuroradiologyUniversity Hospital, Technische Universität DresdenDresdenGermany
| | - Malcolm Macleod
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Nikola Sprigg
- Translational and Clinical Research InstituteNewcastle University and Newcastle upon Tyne Hospitals NHS TrustNewcastle upon TyneUK
| | - Joanna M. Wardlaw
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- UK Dementia Research Institute Centre at the University of EdinburghEdinburghUK
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22
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The Utility of Automated ASPECTS in Acute Ischemic Stroke for Intravenous Recombinant Tissue Plasminogen Activator (IV-rtPA) Therapy. Neurol Int 2022; 14:981-990. [PMID: 36412699 PMCID: PMC9680239 DOI: 10.3390/neurolint14040077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/13/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022] Open
Abstract
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.
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23
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Mallon DH, Taylor EJR, Vittay OI, Sheeka A, Doig D, Lobotesis K. Comparison of automated ASPECTS, large vessel occlusion detection and CTP analysis provided by Brainomix and RapidAI in patients with suspected ischaemic stroke. J Stroke Cerebrovasc Dis 2022; 31:106702. [PMID: 35994882 DOI: 10.1016/j.jstrokecerebrovasdis.2022.106702] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 07/27/2022] [Accepted: 08/04/2022] [Indexed: 12/30/2022] Open
Abstract
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.
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Affiliation(s)
- Dermot H Mallon
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom; MRC London Institute of Medical Sciences, Imperial College London, London, UK.
| | - Eleanor J R Taylor
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Orsolya I Vittay
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Alexander Sheeka
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - David Doig
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Kyriakos Lobotesis
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
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24
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Wardlaw JM, Mair G, von Kummer R, Williams MC, Li W, Storkey AJ, Trucco E, Liebeskind DS, Farrall A, Bath PM, White P. Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence. Stroke 2022; 53:2393-2403. [PMID: 35440170 DOI: 10.1161/strokeaha.121.036204] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Joanna M Wardlaw
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | - Grant Mair
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | - Rüdiger von Kummer
- Institute of Diagnostic and Interventional Neuroradiology, Universitätsklinikum Carl Gustav Carus, Dresden, Germany (R.v.K.)
| | - Michelle C Williams
- Centre for Cardiovascular Science, University of Edinburgh, Little France, United Kingdom (M.C.W.)
| | - Wenwen Li
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | | | - Emanuel Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee (E.T.)
| | | | - Andrew Farrall
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | - Philip M Bath
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, Queen's Medical Centre campus, United Kingdom (P.M.B.)
| | - Philip White
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne and Newcastle upon Tyne Hospitals NHS Trust, United Kingdom (P.W.)
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25
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Zhong W, Chen Z, Yan S, Zhou Y, Zhang R, Luo Z, Yu J, Lou M. Multi-Mode Imaging Scale for Endovascular Therapy in Patients with Acute Ischemic Stroke (META). Brain Sci 2022; 12:brainsci12070821. [PMID: 35884628 PMCID: PMC9313044 DOI: 10.3390/brainsci12070821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/11/2022] [Accepted: 06/16/2022] [Indexed: 11/17/2022] Open
Abstract
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.
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Affiliation(s)
- Wansi Zhong
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China; (W.Z.); (Z.C.); (S.Y.); (Y.Z.); (R.Z.); (Z.L.)
| | - Zhicai Chen
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China; (W.Z.); (Z.C.); (S.Y.); (Y.Z.); (R.Z.); (Z.L.)
| | - Shenqiang Yan
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China; (W.Z.); (Z.C.); (S.Y.); (Y.Z.); (R.Z.); (Z.L.)
| | - Ying Zhou
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China; (W.Z.); (Z.C.); (S.Y.); (Y.Z.); (R.Z.); (Z.L.)
| | - Ruoxia Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China; (W.Z.); (Z.C.); (S.Y.); (Y.Z.); (R.Z.); (Z.L.)
| | - Zhongyu Luo
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China; (W.Z.); (Z.C.); (S.Y.); (Y.Z.); (R.Z.); (Z.L.)
| | - Jun Yu
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China;
| | - Min Lou
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China; (W.Z.); (Z.C.); (S.Y.); (Y.Z.); (R.Z.); (Z.L.)
- Correspondence: ; Tel.: +86-571-87784810; Fax: +86-571-87784850
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26
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Cao Z, Xu J, Song B, Chen L, Sun T, He Y, Wei Y, Niu G, Zhang Y, Feng Q, Ding Z, Shi F, Shen D. Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients. Hum Brain Mapp 2022; 43:3023-3036. [PMID: 35357053 PMCID: PMC9189036 DOI: 10.1002/hbm.25845] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 02/15/2022] [Accepted: 03/09/2022] [Indexed: 02/05/2023] Open
Abstract
Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers. In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients. First, we proposed to use the CT perfusion (CTP) from one-stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings. Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of CTP core volume of an infraction. The proposed method shows its potential for automated ASPECTS scoring on NCCT images.
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Affiliation(s)
- Zehong Cao
- School of Biomedical Engineering Southern Medical UniversityGuangzhouChina,Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Jiaona Xu
- The Fourth School of Clinical MedicineZhejiang Chinese Medicine UniversityHangzhouChina,Department of Neurology, Affiliated Hangzhou First People's HospitalZhejiang University School of MedicineHangzhouChina
| | - Bin Song
- Department of Radiology, West China HospitalSichuan UniversityChengduChina
| | - Lizhou Chen
- Department of Radiology, West China HospitalSichuan UniversityChengduChina
| | - Tianyang Sun
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Yichu He
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Ying Wei
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Guozhong Niu
- Department of Neurology, Affiliated Hangzhou First People's HospitalZhejiang University School of MedicineHangzhouChina
| | - Yu Zhang
- School of Biomedical Engineering Southern Medical UniversityGuangzhouChina
| | - Qianjin Feng
- School of Biomedical Engineering Southern Medical UniversityGuangzhouChina
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's HospitalZhejiang University School of MedicineHangzhouChina
| | - Feng Shi
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Dinggang Shen
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.ShanghaiChina,School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
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Li X, Zhen Y, Liu H, Zeng W, Li Y, Liu L, Yang R. 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. Acta Radiol 2022; 64:328-335. [PMID: 35118879 DOI: 10.1177/02841851221075789] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Xiang Li
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, PR China
| | - Yanling Zhen
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Sichuan, PR China
| | - Huan Liu
- GE Healthcare, Shanghai, PR China
| | - Wenbing Zeng
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, PR China
| | - Yige Li
- GE Healthcare, Shanghai, PR China
| | - Ling Liu
- GE Healthcare, Shanghai, PR China
| | - Ran Yang
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, PR China
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Impact of Encephalomalacia and White Matter Hyperintensities on ASPECTS in Patients With Acute Ischemic Stroke: Comparison of Automated- and Radiologist-Derived Scores. AJR Am J Roentgenol 2021; 218:878-887. [PMID: 34910537 DOI: 10.2214/ajr.21.26819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
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.
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Yeo M, Kok HK, Kutaiba N, Maingard J, Thijs V, Tahayori B, Russell J, Jhamb A, Chandra RV, Brooks M, Barras CD, Asadi H. Artificial intelligence in clinical decision support and outcome prediction - applications in stroke. J Med Imaging Radiat Oncol 2021; 65:518-528. [PMID: 34050596 DOI: 10.1111/1754-9485.13193] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 04/29/2021] [Indexed: 01/19/2023]
Abstract
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.
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Affiliation(s)
- Melissa Yeo
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Hong Kuan Kok
- Interventional Radiology Service, Department of Radiology, Northern Health, Melbourne, Victoria, Australia
- School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia
| | - Numan Kutaiba
- Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia
| | - Julian Maingard
- School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia
- Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Vincent Thijs
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Department of Neurology, Austin Health, Melbourne, Victoria, Australia
| | - Bahman Tahayori
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
- IBM Research Australia, Melbourne, Victoria, Australia
| | - Jeremy Russell
- Department of Neurosurgery, Austin Hospital, Melbourne, Victoria, Australia
| | - Ashu Jhamb
- Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Ronil V Chandra
- Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Mark Brooks
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Interventional Neuroradiology Service, Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia
| | - Christen D Barras
- South Australian Institute of Health and Medical Research, Adelaide, South Australia, Australia
- School of Medicine, The University of Adelaide, Adelaide, South Australia, Australia
| | - Hamed Asadi
- School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia
- Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia
- Interventional Neuroradiology Service, Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia
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Shafaat O, Bernstock JD, Shafaat A, Yedavalli VS, Elsayed G, Gupta S, Sotoudeh E, Sair HI, Yousem DM, Sotoudeh H. Leveraging artificial intelligence in ischemic stroke imaging. J Neuroradiol 2021; 49:343-351. [PMID: 33984377 DOI: 10.1016/j.neurad.2021.05.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/02/2021] [Accepted: 05/03/2021] [Indexed: 11/30/2022]
Abstract
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.
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Affiliation(s)
- Omid Shafaat
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Amir Shafaat
- Department of Mechanical Engineering, Arak University of Technology, Daneshgah St, 38181-41167 Arak, Iran.
| | - Vivek S Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Galal Elsayed
- Department of Neurosurgery, University of Alabama at Birmingham, 1960 6th Ave. S., Birmingham, AL 35233, USA.
| | - Saksham Gupta
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Ehsan Sotoudeh
- Department of Surgery, Iranian Hospital in Dubai, P.O.BOX: 2330, Al-Wasl Road, Dubai 2330, UAE.
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA; Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, 600 North Wolfe Street, Baltimore, MD 21287, USA.
| | - David M Yousem
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Houman Sotoudeh
- Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA.
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Naganuma M, Tachibana A, Fuchigami T, Akahori S, Okumura S, Yi K, Matsuo Y, Ikeno K, Yonehara T. Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm. J Stroke Cerebrovasc Dis 2021; 30:105791. [PMID: 33878549 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105791] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/24/2021] [Accepted: 03/24/2021] [Indexed: 11/19/2022] Open
Abstract
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.
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Affiliation(s)
- Masaki Naganuma
- Department of Neurology, Saiseikai Kumamoto Hospital, Chikami 5-3-1, Minami-ku, Kumamoto, Japan.
| | | | | | | | - Shuichiro Okumura
- Department of Radiology, Saiseikai Kumamoto Hospital, Kumamoto, Japan.
| | - Kenichiro Yi
- Department of Neurology, Saiseikai Kumamoto Hospital, Chikami 5-3-1, Minami-ku, Kumamoto, Japan.
| | - Yoshimasa Matsuo
- Department of Neurology, Saiseikai Kumamoto Hospital, Chikami 5-3-1, Minami-ku, Kumamoto, Japan.
| | - Koichi Ikeno
- Department of Neurology, Saiseikai Kumamoto Hospital, Chikami 5-3-1, Minami-ku, Kumamoto, Japan.
| | - Toshiro Yonehara
- Department of Neurology, Saiseikai Kumamoto Hospital, Chikami 5-3-1, Minami-ku, Kumamoto, Japan.
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Seker F, Pfaff JAR, Mokli Y, Berberich A, Namias R, Gerry S, Nagel S, Bendszus M, Herweh C. Diagnostic accuracy of automated occlusion detection in CT angiography using e-CTA. Int J Stroke 2021; 17:77-82. [PMID: 33527886 PMCID: PMC8739618 DOI: 10.1177/1747493021992592] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
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.
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Affiliation(s)
- Fatih Seker
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Yahia Mokli
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne Berberich
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Steven Gerry
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Simon Nagel
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Herweh
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
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Cauley KA, Yorks PJ, Flora S, Fielden SW. The effects of the skull on CT imaging of the brain: a skull and brain phantom study. Br J Radiol 2021; 94:20200714. [PMID: 33533635 DOI: 10.1259/bjr.20200714] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
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.
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Affiliation(s)
- Keith A Cauley
- Department of Radiology, Geisinger Medical Center, Danville, PA, USA
| | - Patrick J Yorks
- Department of Medical Health Physics, Geisinger Medical Center, Danville, PA, USA
| | - Sarah Flora
- Department of Radiology, Geisinger Medical Center, Danville, PA, USA
| | - Samuel W Fielden
- Geisinger Autism & Developmental Medicine Institute, Lewisburg, PA, USA
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Nannoni S, Ricciardi F, Strambo D, Sirimarco G, Wintermark M, Dunet V, Michel P. Correlation between ASPECTS and Core Volume on CT Perfusion: Impact of Time since Stroke Onset and Presence of Large-Vessel Occlusion. AJNR Am J Neuroradiol 2021; 42:422-428. [PMID: 33509915 DOI: 10.3174/ajnr.a6959] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 10/15/2020] [Indexed: 11/07/2022]
Abstract
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.
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Affiliation(s)
- S Nannoni
- From the Stroke Center (S.N., D.S., G.S., P.M.), Neurology Service
| | - F Ricciardi
- Department of Statistical Science (F.R.), University College London, London, UK
| | - D Strambo
- From the Stroke Center (S.N., D.S., G.S., P.M.), Neurology Service
| | - G Sirimarco
- From the Stroke Center (S.N., D.S., G.S., P.M.), Neurology Service
| | - M Wintermark
- Department of Radiology (M.W.), Neuroradiology Division, Stanford University and Medical Center, Stanford, California
| | - V Dunet
- Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital, Lausanne, Switzerland
| | - P Michel
- From the Stroke Center (S.N., D.S., G.S., P.M.), Neurology Service
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35
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Wolff L, Berkhemer OA, van Es ACGM, van Zwam WH, Dippel DWJ, Majoie CBLM, van Walsum T, van der Lugt A. Validation of automated Alberta Stroke Program Early CT Score (ASPECTS) software for detection of early ischemic changes on non-contrast brain CT scans. Neuroradiology 2020; 63:491-498. [PMID: 32857212 PMCID: PMC7966210 DOI: 10.1007/s00234-020-02533-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 08/17/2020] [Indexed: 11/25/2022]
Abstract
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.
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Affiliation(s)
- Lennard Wolff
- Department of Radiology & Nuclear Medicine, Erasmus MC, P. van Andel & L. Wolff, room Ne-515, Postbus 2040, 3000, CA, Rotterdam, the Netherlands.
| | - Olvert A Berkhemer
- Department of Radiology & Nuclear Medicine, Erasmus MC, P. van Andel & L. Wolff, room Ne-515, Postbus 2040, 3000, CA, Rotterdam, the Netherlands.,Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands.,Department of Neurology, Erasmus MC, Rotterdam, the Netherlands
| | - Adriaan C G M van Es
- Department of Radiology & Nuclear Medicine, Erasmus MC, P. van Andel & L. Wolff, room Ne-515, Postbus 2040, 3000, CA, Rotterdam, the Netherlands
| | - Wim H van Zwam
- Department of Radiology, Maastricht UMC+, Maastricht, the Netherlands
| | - Diederik W J Dippel
- Department of Radiology & Nuclear Medicine, Erasmus MC, P. van Andel & L. Wolff, room Ne-515, Postbus 2040, 3000, CA, Rotterdam, the Netherlands.,Department of Neurology, Erasmus MC, Rotterdam, the Netherlands
| | - Charles B L M Majoie
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands
| | - Theo van Walsum
- Department of Radiology & Nuclear Medicine, Erasmus MC, P. van Andel & L. Wolff, room Ne-515, Postbus 2040, 3000, CA, Rotterdam, the Netherlands.,Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, the Netherlands
| | - Aad van der Lugt
- Department of Radiology & Nuclear Medicine, Erasmus MC, P. van Andel & L. Wolff, room Ne-515, Postbus 2040, 3000, CA, Rotterdam, the Netherlands
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Quantitative Serial CT Imaging-Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke. Neurocrit Care 2020; 33:785-792. [PMID: 32729090 DOI: 10.1007/s12028-020-01056-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 07/16/2020] [Indexed: 12/16/2022]
Abstract
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.
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