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Bojsen JA, Elhakim MT, Graumann O, Gaist D, Nielsen M, Harbo FSG, Krag CH, Sagar MV, Kruuse C, Boesen MP, Rasmussen BSB. Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis. Insights Imaging 2024; 15:160. [PMID: 38913106 PMCID: PMC11196541 DOI: 10.1186/s13244-024-01723-7] [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: 04/08/2024] [Accepted: 05/23/2024] [Indexed: 06/25/2024] Open
Abstract
OBJECTIVES This systematic review and meta-analysis aimed to assess the stroke detection performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and additionally to identify reporting insufficiencies. METHODS PRISMA guidelines were followed. MEDLINE, Embase, Cochrane Central, and IEEE Xplore were searched for studies utilising MRI and AI for stroke detection. The protocol was prospectively registered with PROSPERO (CRD42021289748). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were the primary outcomes. Only studies using MRI in adults were included. The intervention was AI for stroke detection with ischaemic and haemorrhagic stroke in separate categories. Any manual labelling was used as a comparator. A modified QUADAS-2 tool was used for bias assessment. The minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist was used to assess reporting insufficiencies. Meta-analyses were performed for sensitivity, specificity, and hierarchical summary ROC (HSROC) on low risk of bias studies. RESULTS Thirty-three studies were eligible for inclusion. Fifteen studies had a low risk of bias. Low-risk studies were better for reporting MI-CLAIM items. Only one study examined a CE-approved AI algorithm. Forest plots revealed detection sensitivity and specificity of 93% and 93% with identical performance in the HSROC analysis and positive and negative likelihood ratios of 12.6 and 0.079. CONCLUSION Current AI technology can detect ischaemic stroke in MRI. There is a need for further validation of haemorrhagic detection. The clinical usability of AI stroke detection in MRI is yet to be investigated. CRITICAL RELEVANCE STATEMENT This first meta-analysis concludes that AI, utilising diffusion-weighted MRI sequences, can accurately aid the detection of ischaemic brain lesions and its clinical utility is ready to be uncovered in clinical trials. KEY POINTS There is a growing interest in AI solutions for detection aid. The performance is unknown for MRI stroke assessment. AI detection sensitivity and specificity were 93% and 93% for ischaemic lesions. There is limited evidence for the detection of patients with haemorrhagic lesions. AI can accurately detect patients with ischaemic stroke in MRI.
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Affiliation(s)
- Jonas Asgaard Bojsen
- Research and Innovation Unit of Radiology, Odense University Hospital, University of Southern Denmark, Odense, Denmark.
| | - Mohammad Talal Elhakim
- Research and Innovation Unit of Radiology, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Ole Graumann
- Research Unit of Radiology, Aarhus University Hospital, Aarhus University, Aarhus, Denmark
| | - David Gaist
- Research Unit for Neurology, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Frederik Severin Gråe Harbo
- Research and Innovation Unit of Radiology, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Christian Hedeager Krag
- Radiological AI Test Center, Copenhagen University Hospital-Bispebjerg, Frederiksberg, Herlev and Gentofte Hospital, Copenhagen, Denmark
- Department of Radiology, Copenhagen University Hospital-Herlev and Gentofte, Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Malini Vendela Sagar
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital-Herlev and Gentofte, Copenhagen, Denmark
| | - Christina Kruuse
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Mikael Ploug Boesen
- Radiological AI Test Center, Copenhagen University Hospital-Bispebjerg, Frederiksberg, Herlev and Gentofte Hospital, Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Radiology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Benjamin Schnack Brandt Rasmussen
- Research and Innovation Unit of Radiology, Odense University Hospital, University of Southern Denmark, Odense, Denmark
- Centre for Clinical Artificial Intelligence, Odense University Hospital, University of Southern Denmark, Odense, Denmark
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Chen Q, Zhang S, Liu W, Sun X, Luo Y, Sun X. Application of emerging technologies in ischemic stroke: from clinical study to basic research. Front Neurol 2024; 15:1400469. [PMID: 38915803 PMCID: PMC11194379 DOI: 10.3389/fneur.2024.1400469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Stroke is a primary cause of noncommunicable disease-related death and disability worldwide. The most common form, ischemic stroke, is increasing in incidence resulting in a significant burden on patients and society. Urgent action is thus needed to address preventable risk factors and improve treatment methods. This review examines emerging technologies used in the management of ischemic stroke, including neuroimaging, regenerative medicine, biology, and nanomedicine, highlighting their benefits, clinical applications, and limitations. Additionally, we suggest strategies for technological development for the prevention, diagnosis, and treatment of ischemic stroke.
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Affiliation(s)
- Qiuyan Chen
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Shuxia Zhang
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Wenxiu Liu
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Xiao Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Yun Luo
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Xiaobo Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
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Iqbal MS, Belal Bin Heyat M, Parveen S, Ammar Bin Hayat M, Roshanzamir M, Alizadehsani R, Akhtar F, Sayeed E, Hussain S, Hussein HS, Sawan M. Progress and trends in neurological disorders research based on deep learning. Comput Med Imaging Graph 2024; 116:102400. [PMID: 38851079 DOI: 10.1016/j.compmedimag.2024.102400] [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: 01/02/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 06/10/2024]
Abstract
In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.
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Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University of Azad Jammu & Kashmir, Bagh, Pakistan.
| | - Md Belal Bin Heyat
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| | - Saba Parveen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
| | | | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, VIC 3216, Australia.
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Eram Sayeed
- Kisan Inter College, Dhaurahara, Kushinagar, India.
| | - Sadiq Hussain
- Department of Examination, Dibrugarh University, Assam 786004, India.
| | - Hany S Hussein
- Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt.
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
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Nambo R, Karashima S, Mizoguchi R, Konishi S, Hashimoto A, Aono D, Kometani M, Furukawa K, Yoneda T, Imamura K, Nambo H. Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires. Sci Rep 2024; 14:10492. [PMID: 38714730 PMCID: PMC11076536 DOI: 10.1038/s41598-024-61047-w] [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: 10/14/2023] [Accepted: 04/30/2024] [Indexed: 05/10/2024] Open
Abstract
Cardiovascular and cerebrovascular diseases (CCVD) are prominent mortality causes in Japan, necessitating effective preventative measures, early diagnosis, and treatment to mitigate their impact. A diagnostic model was developed to identify patients with ischemic heart disease (IHD), stroke, or both, using specific health examination data. Lifestyle habits affecting CCVD development were analyzed using five causal inference methods. This study included 473,734 patients aged ≥ 40 years who underwent specific health examinations in Kanazawa, Japan between 2009 and 2018 to collect data on basic physical information, lifestyle habits, and laboratory parameters such as diabetes, lipid metabolism, renal function, and liver function. Four machine learning algorithms were used: Random Forest, Logistic regression, Light Gradient Boosting Machine, and eXtreme-Gradient-Boosting (XGBoost). The XGBoost model exhibited superior area under the curve (AUC), with mean values of 0.770 (± 0.003), 0.758 (± 0.003), and 0.845 (± 0.005) for stroke, IHD, and CCVD, respectively. The results of the five causal inference analyses were summarized, and lifestyle behavior changes were observed after the onset of CCVD. A causal relationship from 'reduced mastication' to 'weight gain' was found for all causal species theory methods. This prediction algorithm can screen for asymptomatic myocardial ischemia and stroke. By selecting high-risk patients suspected of having CCVD, resources can be used more efficiently for secondary testing.
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Affiliation(s)
- Riku Nambo
- School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan
| | - Shigehiro Karashima
- Institute of Liberal Arts and Science, Kanazawa University, Kanazawa, Japan.
| | - Ren Mizoguchi
- Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan
| | - Seigo Konishi
- Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan
| | - Atsushi Hashimoto
- Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan
| | - Daisuke Aono
- Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan
| | - Mitsuhiro Kometani
- Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan
| | - Kenji Furukawa
- Health Care Center, Japan Advanced Institute of Science and Technology, Nomi, Japan
| | - Takashi Yoneda
- Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan
| | - Kousuke Imamura
- Faculty of Electrical, Information and Communication Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
| | - Hidetaka Nambo
- Institute of Transdisciplinary Sciences, Kanazawa University, Kanazawa, Japan.
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Huang LX, Wu XB, Liu YA, Guo X, Liu CC, Cai WQ, Wang SW, Luo B. High-resolution magnetic resonance vessel wall imaging in ischemic stroke and carotid artery atherosclerotic stenosis: A review. Heliyon 2024; 10:e27948. [PMID: 38571643 PMCID: PMC10987942 DOI: 10.1016/j.heliyon.2024.e27948] [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: 09/11/2023] [Revised: 03/02/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024] Open
Abstract
Ischemic stroke is a significant burden on human health worldwide. Carotid Atherosclerosis stenosis plays an important role in the comprehensive assessment and prevention of ischemic stroke patients. High-resolution vessel wall magnetic resonance imaging has emerged as a successful technique for assessing carotid atherosclerosis stenosis. This advanced imaging modality has shown promise in effectively displaying a wide range of characteristics associated with the condition, leading to a comprehensive evaluation. High-resolution vessel wall magnetic resonance imaging not only enables a comprehensive evaluation of the instability of carotid atherosclerosis stenosis plaques but also provides valuable information for understanding the pathogenesis and predicting the prognosis of ischemic stroke patients. The purpose of this article is to review the application of high-resolution magnetic resonance imaging in ischemic stroke and carotid atherosclerotic stenosis.
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Affiliation(s)
- Li-Xin Huang
- Department of Neurosurgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Xiao-Bing Wu
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yi-Ao Liu
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Xin Guo
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Chi-Chen Liu
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Wang-Qing Cai
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sheng-Wen Wang
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bin Luo
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
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Mathur R, Meyfroidt G, Robba C, Stevens RD. Neuromonitoring in the ICU - what, how and why? Curr Opin Crit Care 2024; 30:99-105. [PMID: 38441121 DOI: 10.1097/mcc.0000000000001138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
PURPOSE OF REVIEW We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury. RECENT FINDINGS Noninvasive intracranial pressure evaluation with optic nerve sheath diameter measurements, transcranial Doppler waveform analysis, or skull mechanical extensometer waveform recordings have potential safety and resource-intensity advantages when compared to standard invasive monitors, however each of these techniques has limitations. Quantitative electroencephalography can be applied for detection of cerebral ischemia and states of covert consciousness. Near-infrared spectroscopy may be leveraged for cerebral oxygenation and autoregulation computation. Automated quantitative pupillometry and heart rate variability analysis have been shown to have diagnostic and/or prognostic significance in selected subtypes of acute brain injury. Finally, artificial intelligence is likely to transform interpretation and deployment of neuromonitoring paradigms individually and when integrated in multimodal paradigms. SUMMARY The ability to detect brain dysfunction and injury in critically ill patients is being enriched thanks to remarkable advances in neuromonitoring data acquisition and analysis. Studies are needed to validate the accuracy and reliability of these new approaches, and their feasibility and implementation within existing intensive care workflows.
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Affiliation(s)
- Rohan Mathur
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Geert Meyfroidt
- Department of Intensive Care Medicine, University Hospitals Leuven, Belgium and Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Belgium
| | - Chiara Robba
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Scienze Chirurgiche e Diagnostiche Integrate, Università degli Studi di Genova, Genova, Italy
| | - Robert D Stevens
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA
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Santos AD, Visser M, Lin L, Bivard A, Churilov L, Parsons MW. Novel artificial intelligence-based hypodensity detection tool improves clinician identification of hypodensity on non-contrast computed tomography in stroke patients. Front Neurol 2024; 15:1359775. [PMID: 38426177 PMCID: PMC10902446 DOI: 10.3389/fneur.2024.1359775] [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: 12/21/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction In acute stroke, identifying early changes (parenchymal hypodensity) on non-contrast CT (NCCT) can be challenging. We aimed to identify whether the accuracy of clinicians in detecting acute hypodensity in ischaemic stroke patients on a non-contrast CT is improved with the use of an Artificial Intelligence (AI) based, automated hypodensity detection algorithm (HDT) using MRI-DWI as the gold standard. Methods The study employed a case-crossover within-clinician design, where 32 clinicians were tasked with identifying hypodensity lesions on NCCT scans for five a priori selected patient cases, before and after viewing the AI-based HDT. The DICE similarity coefficient (DICE score) was the primary measure of accuracy. Statistical analysis compared DICE scores with and without AI-based HDT using mixed-effects linear regression, with individual NCCT scans and clinicians as nested random effects. Results The AI-based HDT had a mean DICE score of 0.62 for detecting hypodensity across all NCCT scans. Clinicians' overall mean DICE score was 0.33 (SD 0.31) before AI-based HDT implementation and 0.40 (SD 0.27) after implementation. AI-based HDT use was associated with an increase of 0.07 (95% CI: 0.02-0.11, p = 0.003) in DICE score accounting for individual scan and clinician effects. For scans with small lesions, clinicians achieved a mean increase in DICE score of 0.08 (95% CI: 0.02, 0.13, p = 0.004) following AI-based HDT use. In a subgroup of 15 trainees, DICE score improved with AI-based HDT implementation [mean difference in DICE 0.09 (95% CI: 0.03, 0.14, p = 0.004)]. Discussion AI-based automated hypodensity detection has potential to enhance clinician accuracy of detecting hypodensity in acute stroke diagnosis, especially for smaller lesions, and notably for less experienced clinicians.
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Affiliation(s)
- Angela Dos Santos
- University of New South Wales, South-Western Sydney Clinical Campus, Kensington, NSW, Australia
- Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Milanka Visser
- Melbourne Brain Centre, University of Melbourne, Melbourne, VIC, Australia
| | - Longting Lin
- Sydney Brain Centre, Faculty of Medicine, University of New South Wales, Kensington, NSW, Australia
| | - Andrew Bivard
- Melbourne Brain Centre, University of Melbourne, Melbourne, VIC, Australia
| | - Leonid Churilov
- Melbourne Medical School, University of Melbourne, Melbourne, VIC, Australia
| | - Mark William Parsons
- University of New South Wales, South-Western Sydney Clinical Campus, Kensington, NSW, Australia
- Department of Neurology, Liverpool Hospital, Ingham Institute for Applied Medical Research Liverpool, Liverpool, NSW, Australia
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Shiferaw KB, Wali P, Waltemath D, Zeleke AA. Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling. Front Cardiovasc Med 2024; 10:1308668. [PMID: 38235288 PMCID: PMC10793658 DOI: 10.3389/fcvm.2023.1308668] [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: 10/06/2023] [Accepted: 12/04/2023] [Indexed: 01/19/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.
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Affiliation(s)
- Kirubel Biruk Shiferaw
- Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
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Shi J, Bendig D, Vollmar HC, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. J Med Internet Res 2023; 25:e45815. [PMID: 38064255 PMCID: PMC10746970 DOI: 10.2196/45815] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 08/16/2023] [Accepted: 09/30/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. OBJECTIVE In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. METHODS Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. RESULTS From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. CONCLUSIONS The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.
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Affiliation(s)
- Jin Shi
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | - David Bendig
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | | | - Peter Rasche
- Department of Healthcare, University of Applied Science - Hochschule Niederrhein, Krefeld, Germany
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Demuth S, Müller J, Quenardelle V, Lauer V, Gheoca R, Trzeciak M, Pierre-Paul I, De Sèze J, Gourraud PA, Wolff V. Strokecopilot: a literature-based clinical decision support system for acute ischemic stroke treatment. J Neurol 2023; 270:6113-6123. [PMID: 37668701 DOI: 10.1007/s00415-023-11979-6] [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: 06/05/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND Acute ischemic stroke (AIS) is an immediate emergency whose management is becoming more and more personalized while facing a limited number of neurologists with high expertise. Clinical decision support systems (CDSS) are digital tools leveraging information and artificial intelligence technologies. Here, we present the Strokecopilot project, a CDSS for the management of the acute phase of AIS. It has been designed to support the evidence-based medicine reasoning of neurologists regarding the indications of intravenous thrombolysis (IVT) and endovascular treatments (ET). METHODS Reference populations were manually extracted from the field's main guidelines and randomized clinical trials (RCT). Their characteristics were harmonized in a computerized reference database. We developed a web application whose algorithm identifies the reference populations matching the patient's characteristics. It returns the latter's outcomes in a graphical user interface (GUI), whose design has been driven by real-world practices. RESULTS Strokecopilot has been released at www.digitalneurology.net . The reference database includes 25 reference populations from 2 guidelines and 15 RCTs. After a request, the reference populations matching the patient characteristics are displayed with a summary and a meta-analysis of their results. The status regarding IVT and ET indications are presented as "in guidelines", "in literature", or "outside literature references". The GUI is updated to provide several levels of explanation. Strokecopilot may be updated as the literature evolves by loading a new version of the reference populations' database. CONCLUSION Strokecopilot is a literature-based CDSS, developed to support neurologists in the management of the acute phase of AIS.
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Affiliation(s)
- Stanislas Demuth
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France.
- INSERM U1119 Myelin Biopathology, Neuroprotection, and Therapeutic Strategies, Strasbourg, France.
- INSERM U1064 Center for Research in Transplantation and Translational Immunology, Nantes University, Nantes, France.
| | - Joris Müller
- Public Health Service, University Hospital of Strasbourg, Strasbourg, France
| | | | - Valérie Lauer
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Roxana Gheoca
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Malwina Trzeciak
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France
| | | | - Jérôme De Sèze
- INSERM U1119 Myelin Biopathology, Neuroprotection, and Therapeutic Strategies, Strasbourg, France
- Department of Neurology, University Hospital of Strasbourg, Strasbourg, France
- Center of Clinical Investigations, University Hospital of Strasbourg, Strasbourg, France
| | - Pierre-Antoine Gourraud
- INSERM U1064 Center for Research in Transplantation and Translational Immunology, Nantes University, Nantes, France
- Data Clinic, Nantes University Hospital, Nantes, France
| | - Valérie Wolff
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France
- «Mitochondrie, Stress Oxydant et Protection Musculaire», UR3072, University of Strasbourg, Strasbourg, France
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11
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Caruso PF, Greco M, Ebm C, Angelotti G, Cecconi M. Implementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care. Crit Care Clin 2023; 39:783-793. [PMID: 37704340 DOI: 10.1016/j.ccc.2023.03.007] [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: 09/15/2023]
Abstract
This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers' minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding.
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Affiliation(s)
- Pier Francesco Caruso
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy; Department of Anesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| | - Massimiliano Greco
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy; Department of Anesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy.
| | - Claudia Ebm
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy
| | - Giovanni Angelotti
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| | - Maurizio Cecconi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy; Department of Anesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
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12
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Zhang K, Jiang Y, Zeng H, Zhu H. Application and risk prediction of thrombolytic therapy in cardio-cerebrovascular diseases: a review. Thromb J 2023; 21:90. [PMID: 37667349 PMCID: PMC10476453 DOI: 10.1186/s12959-023-00532-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023] Open
Abstract
Cardiocerebrovascular diseases (CVDs) are the leading cause of death worldwide, consuming huge healthcare budget. For CVD patients, the prompt assessment and appropriate administration is the crux to save life and improve prognosis. Thrombolytic therapy, as a non-invasive approach to achieve recanalization, is the basic component of CVD treatment. Still, there are risks that limits its application. The objective of this review is to give an introduction on the utilization of thrombolytic therapy in cardiocerebrovascular blockage diseases, including coronary heart disease and ischemic stroke, and to review the development in risk assessment of thrombolytic therapy, comparing the performance of traditional scales and novel artificial intelligence-based risk assessment models.
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Affiliation(s)
- Kexin Zhang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Yao Jiang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Hesong Zeng
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Hongling Zhu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
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13
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Camardella C, Germanotta M, Aprile I, Cappiello G, Curto Z, Scoglio A, Mazzoleni S, Frisoli A. A Decision Support System to Provide an Ongoing Prediction of Robot-Assisted Rehabilitation Outcome in Stroke Survivors. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941244 DOI: 10.1109/icorr58425.2023.10304700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Clinicians often deal with complex robotic platform and serious games in stroke patients rehabilitation contexts, and they face two main problems: 1) the interpretation of either the performance in game or measures of a robotic system from the motor recovery point of view, and 2) the duration and complexity of clinical scales administration that makes repetitive assessments during the therapy unpractical. In this paper, a Random Tree Forest based system was trained and tested to provide a prediction of different clinical outcomes (i.e. FMA, ARAT, and MI) along the whole therapy duration, having non-clinical measures only as inputs, acting as a simulated decision support system. The dataset includes 30 post-stroke patients, that underwent a 30-session robot-assisted rehabilitation treatment. Results have shown that the system is able to produce very accurate and reliable predictions about the motor recovery of the patient at the end of the therapy, already in the first phases of the rehabilitation (i40% of therapy execution), just using robotic platform measures. Such a tool would provide a great benefit in terms of rehabilitation objectives planning, as a decision support tool for highly personalized rehabilitation treatments.
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14
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Jo S, Song Y, Lee Y, Heo SH, Jang SJ, Kim Y, Shin JH, Jeong J, Park HS. Functional MRI Assessment of Brain Activity During Hand Rehabilitation with an MR-Compatible Soft Glove in Chronic Stroke Patients: A Preliminary Study. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941170 DOI: 10.1109/icorr58425.2023.10304776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Brain plasticity plays a significant role in functional recovery after stroke, but the specific benefits of hand rehabilitation robot therapy remain unclear. Evaluating the specific effects of hand rehabilitation robot therapy is crucial in understanding how it impacts brain activity and its relationship to rehabilitation outcomes. This study aimed to investigate the brain activity pattern during hand rehabilitation exercise using functional magnetic resonance imaging (fMRI), and to compare it before and after 3-week hand rehabilitation robot training. To evaluate it, an fMRI experimental environment was constructed to facilitate the same hand posture used in rehabilitation robot therapy. Two stroke survivors participated and the conjunction analysis results from fMRI scans showed that patient 1 exhibited a significant improvement in activation profile after hand rehabilitation robot training, indicative of improved motor function in the bilateral motor cortex. However, activation profile of patient 2 exhibited a slight decrease, potentially due to habituation to the rehabilitation task. Clinical results supported these findings, with patient 1 experiencing a greater increase in FMA score than patient 2. These results suggest that hand rehabilitation robot therapy can induce different brain activity patterns in stroke survivors, which may be linked to patient-specific training outcomes. Further studies with larger sample sizes are necessary to confirm these findings.
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15
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Najjar R. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics (Basel) 2023; 13:2760. [PMID: 37685300 PMCID: PMC10487271 DOI: 10.3390/diagnostics13172760] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/01/2023] [Accepted: 08/10/2023] [Indexed: 09/10/2023] Open
Abstract
This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.
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Affiliation(s)
- Reabal Najjar
- Canberra Health Services, Australian Capital Territory 2605, Australia
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16
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Kallweit U, Marson AG. Neurology beyond big data - the ninth Congress of the EAN. Nat Rev Neurol 2023:10.1038/s41582-023-00837-8. [PMID: 37393314 DOI: 10.1038/s41582-023-00837-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2023]
Affiliation(s)
- Ulf Kallweit
- Department of Health, University of Witten/Herdecke, Witten, Germany
| | - Anthony G Marson
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK.
- The Walton Centre, NHS Foundation Trust, Liverpool, UK.
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17
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Akay EMZ, Hilbert A, Carlisle BG, Madai VI, Mutke MA, Frey D. Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review. Stroke 2023; 54:1505-1516. [PMID: 37216446 DOI: 10.1161/strokeaha.122.041442] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/21/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND Established randomized trial-based parameters for acute ischemic stroke group patients into generic treatment groups, leading to attempts using various artificial intelligence (AI) methods to directly correlate patient characteristics to outcomes and thereby provide decision support to stroke clinicians. We review AI-based clinical decision support systems in the development stage, specifically regarding methodological robustness and constraints for clinical implementation. METHODS Our systematic review included full-text English language publications proposing a clinical decision support system using AI techniques for direct decision support in acute ischemic stroke cases in adult patients. We (1) describe data and outcomes used in those systems, (2) estimate the systems' benefits compared with traditional stroke diagnosis and treatment, and (3) reported concordance with reporting standards for AI in healthcare. RESULTS One hundred twenty-one studies met our inclusion criteria. Sixty-five were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous. CONCLUSIONS Our results suggest significant validity threats, dissonance in reporting practices, and challenges to clinical translation. We outline practical recommendations for the successful implementation of AI research in acute ischemic stroke treatment and diagnosis.
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Affiliation(s)
- Ela Marie Z Akay
- Charité Lab for Artificial Intelligence in Medicine (CLAIM) (E.M.Z.A., A.H., D.F.), Charité Universitätsmedizin Berlin, Germany
| | - Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine (CLAIM) (E.M.Z.A., A.H., D.F.), Charité Universitätsmedizin Berlin, Germany
| | - Benjamin G Carlisle
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH) (B.G.C., V.I.M.), Charité Universitätsmedizin Berlin, Germany
| | - Vince I Madai
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH) (B.G.C., V.I.M.), Charité Universitätsmedizin Berlin, Germany
- Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, United Kingdom (V.I.M.)
| | - Matthias A Mutke
- Department of Neuroradiology, Heidelberg University Hospital, Germany (M.A.M.)
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine (CLAIM) (E.M.Z.A., A.H., D.F.), Charité Universitätsmedizin Berlin, Germany
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18
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Gkantzios A, Kokkotis C, Tsiptsios D, Moustakidis S, Gkartzonika E, Avramidis T, Aggelousis N, Vadikolias K. Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning. Diagnostics (Basel) 2023; 13:diagnostics13030532. [PMID: 36766637 PMCID: PMC9914778 DOI: 10.3390/diagnostics13030532] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/25/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: "Independent" vs. "Non-Independent" and "Non-Disability" vs. "Disability". Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients.
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Affiliation(s)
- Aimilios Gkantzios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
- Correspondence:
| | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Dimitrios Tsiptsios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Serafeim Moustakidis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
- AIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Estonia
| | - Elena Gkartzonika
- School of Philosophy, University of Ioannina, 45110 Ioannina, Greece
| | - Theodoros Avramidis
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Konstantinos Vadikolias
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
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19
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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20
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Bhattacharya S, Bennet L, Davidson JO, Unsworth CP. Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training. PLoS One 2022; 17:e0278874. [PMID: 36512546 PMCID: PMC9746996 DOI: 10.1371/journal.pone.0278874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic ischemic brain injury, such as those conducted in fetal sheep, have and continue to play a key role in furthering our understanding of the cellular and molecular mechanisms of injury and developing new treatment strategies for clinical translation. At present, the quantification of neurons in histological images consists of slow, manually intensive morphological assessment, requiring many repeats by an expert, which can prove to be time-consuming and prone to human error. Hence, there is an urgent need to automate the neuron classification and quantification process. In this article, we present a 'Gradient Direction, Grey level Co-occurrence Matrix' (GD-GLCM) image training method which outperforms and simplifies the standard training methodology using texture analysis to cell-classification. This is achieved by determining the Grey level Co-occurrence Matrix of the gradient direction of a cell image followed by direct passing to a classifier in the form of a Multilayer Perceptron (MLP). Hence, avoiding all texture feature computation steps. The proposed MLP is trained on both healthy and dying neurons that are manually identified by an expert and validated on unseen hypoxic-ischemic brain slice images from the fetal sheep in utero model. We compared the performance of our classifier using the gradient magnitude dataset as well as the gradient direction dataset. We also compare the performance of a perceptron, a 1-layer MLP, and a 2-layer MLP to each other. We demonstrate here a way of accurately identifying both healthy and dying cortical neurons obtained from brain slice images of the fetal sheep model under global hypoxia to high precision by identifying the most minimised MLP architecture, minimised input space (GLCM size) and minimised training data (GLCM representations) to achieve the highest performance over the standard methodology.
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Affiliation(s)
- Saheli Bhattacharya
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
- * E-mail:
| | - Laura Bennet
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Joanne O. Davidson
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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21
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Kallmes DF, Rabinstein AA. Perfusion from Diffusion: Yet Another Take on Mismatch. Radiology 2022; 307:e222743. [PMID: 36472541 DOI: 10.1148/radiol.222743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- David F. Kallmes
- From the Departments of Radiology (D.F.K.) and Neurology (A.A.R.), Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Alejandro A. Rabinstein
- From the Departments of Radiology (D.F.K.) and Neurology (A.A.R.), Mayo Clinic, 200 First St SW, Rochester, MN 55905
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22
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Chen S, Duan J, Wang H, Wang R, Li J, Qi M, Duan Y, Qi S. Automatic detection of stroke lesion from diffusion-weighted imaging via the improved YOLOv5. Comput Biol Med 2022; 150:106120. [PMID: 36179511 DOI: 10.1016/j.compbiomed.2022.106120] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 08/31/2022] [Accepted: 09/17/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND OBJECTIVE Stroke is the second most deadly disease globally and seriously endangers people's lives and health. The automatic detection of stroke lesions from diffusion-weighted imaging (DWI) can improve the diagnosis. Recently, automatic detection methods based on YOLOv5 have been utilized in medical images. However, most of them barely capture the stroke lesions because of their small size and fuzzy boundaries. METHODS To address this problem, a novel method for tracing the edge of the stroke lesion based on YOLOv5 (TE-YOLOv5) is proposed. Specifically, we constantly update the high-level features of the lesion using an aggregate pool (AP) module. Conversely, we feed the extracted feature into the reverse attention (RA) module to trace the edge relationship promptly. Overall, 1681 DWI images of 319 stroke patients have been collected, and experienced radiologists have marked the lesions. DWI images were randomly split into the training and test set at a ratio of 8:2. TE-YOLOv5 has been compared with the related models, and a detailed ablation analysis has been conducted to clarify the role of the RA and AP modules. RESULTS TE-YOLOv5 outperforms its counterparts and achieves competitive performance with a precision of 81.5%, a recall of 75.8%, and a mAP@0.5 of 80.7% (mean average precision while the intersection over union is 0.5) under the same backbone. At the patient level, the positive finding rate can reach 98.51%, while the confidence is set at 80.0%. After ablating RA, the mAP@0.5 decreases to 79.6%; after ablating RA and AP, the mAP@0.5 decreases to 78.1%. CONCLUSIONS The proposed TE-YOLOv5 can automatically and effectively detect stroke lesions from DWI images, especially for those with an extremely small size and blurred boundaries. AP and RA modules can aggregate multi-layer high-level features and concurrently track the edge relationship of stroke lesions. These detection methods might help radiologists improve stroke diagnosis and have great application potential in clinical practice.
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Affiliation(s)
- Shannan Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Lab of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian, China.
| | - Jinfeng Duan
- Department of General Surgery, General Hospital of Northern Theater Command, Shenyang, China.
| | - Hong Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Rongqiang Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Jinze Li
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Miao Qi
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Yang Duan
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
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23
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Kokkotis C, Giarmatzis G, Giannakou E, Moustakidis S, Tsatalas T, Tsiptsios D, Vadikolias K, Aggelousis N. An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data. Diagnostics (Basel) 2022; 12:diagnostics12102392. [PMID: 36292081 PMCID: PMC9600473 DOI: 10.3390/diagnostics12102392] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge economic consequences. Hence, there is an urgency to model the effect of several risk factors on stroke occurrence, and artificial intelligence (AI) seems to be the appropriate tool. In the present study, we aimed to (i) develop reliable machine learning (ML) prediction models for stroke disease; (ii) cope with a typical severe class imbalance problem, which is posed due to the stroke patients’ class being significantly smaller than the healthy class; and (iii) interpret the model output for understanding the decision-making mechanism. The effectiveness of the proposed ML approach was investigated in a comparative analysis with six well-known classifiers with respect to metrics that are related to both generalization capability and prediction accuracy. The best overall false-negative rate was achieved by the Multi-Layer Perceptron (MLP) classifier (18.60%). Shapley Additive Explanations (SHAP) were employed to investigate the impact of the risk factors on the prediction output. The proposed AI method could lead to the creation of advanced and effective risk stratification strategies for each stroke patient, which would allow for timely diagnosis and the right treatments.
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Affiliation(s)
- Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Georgios Giarmatzis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Erasmia Giannakou
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | | | - Themistoklis Tsatalas
- Department of Physical Education and Sport Science, University of Thessaly, 38221 Trikala, Greece
| | - Dimitrios Tsiptsios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Konstantinos Vadikolias
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
- Correspondence:
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24
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Damigos G, Zacharaki EI, Zerva N, Pavlopoulos A, Chatzikyrkou K, Koumenti A, Moustakas K, Pantos C, Mourouzis I, Lourbopoulos A. Machine learning based analysis of stroke lesions on mouse tissue sections. J Cereb Blood Flow Metab 2022; 42:1463-1477. [PMID: 35209753 PMCID: PMC9274860 DOI: 10.1177/0271678x221083387] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
An unbiased, automated and reliable method for analysis of brain lesions in tissue after ischemic stroke is missing. Manual infarct volumetry or by threshold-based semi-automated approaches is laborious, and biased to human error or biased by many false -positive and -negative data, respectively. Thereby, we developed a novel machine learning, atlas-based method for fully automated stroke analysis in mouse brain slices stained with 2% Triphenyltetrazolium-chloride (2% TTC), named "StrokeAnalyst", which runs on a user-friendly graphical interface. StrokeAnalyst registers subject images on a common spatial domain (a novel mouse TTC- brain atlas of 80 average mathematical images), calculates pixel-based, tissue-intensity statistics (z-scores), applies outlier-detection and machine learning (Random-Forest) models to increase accuracy of lesion detection, and produces volumetry data and detailed neuroanatomical information per lesion. We validated StrokeAnalyst in two separate experimental sets using the filament stroke model. StrokeAnalyst detects stroke lesions in a rater-independent and reproducible way, correctly detects hemispheric volumes even in presence of post-stroke edema and significantly minimizes false-positive errors compared to threshold-based approaches (false-positive rate 1.2-2.3%, p < 0.05). It can process scanner-acquired, and even smartphone-captured or pdf-retrieved images. Overall, StrokeAnalyst surpasses all previous TTC-volumetry approaches and increases quality, reproducibility and reliability of stroke detection in relevant preclinical models.
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Affiliation(s)
- Gerasimos Damigos
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece.,Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Nefeli Zerva
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Angelos Pavlopoulos
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantina Chatzikyrkou
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Argyro Koumenti
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Constantinos Pantos
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Iordanis Mourouzis
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Lourbopoulos
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece.,Institute for Stroke and Dementia Research (ISD), University of Munich Medical Center, Munich, Germany.,Neurointensive Care Unit, Schoen Klinik Bad Aibling, Germany
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25
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Ironside N, Patrie J, Ng S, Ding D, Rizvi T, Kumar JS, Mastorakos P, Hussein MZ, Naamani KE, Abbas R, Harrison Snyder M, Zhuang Y, Kearns KN, Doan KT, Shabo LM, Marfatiah S, Roh D, Lignelli-Dipple A, Claassen J, Worrall BB, Johnston KC, Jabbour P, Park MS, Sander Connolly E, Mukherjee S, Southerland AM, Chen CJ. Quantification of hematoma and perihematomal edema volumes in intracerebral hemorrhage study: Design considerations in an artificial intelligence validation (QUANTUM) study. Clin Trials 2022; 19:534-544. [PMID: 35786006 DOI: 10.1177/17407745221105886] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Hematoma and perihematomal edema volumes are important radiographic markers in spontaneous intracerebral hemorrhage. Accurate, reliable, and efficient quantification of these volumes will be paramount to their utility as measures of treatment effect in future clinical studies. Both manual and semi-automated quantification methods of hematoma and perihematomal edema volumetry are time-consuming and susceptible to inter-rater variability. Efforts are now underway to develop a fully automated algorithm that can replace them. A (QUANTUM) study to establish inter-quantification method measurement equivalency, which deviates from the traditional use of measures of agreement and a comparison hypothesis testing paradigm to indirectly infer quantification method measurement equivalence, is described in this article. The Quantification of Hematoma and Perihematomal Edema Volumes in Intracerebral Hemorrhage study aims to determine whether a fully automated quantification method and a semi-automated quantification method for quantification of hematoma and perihematomal edema volumes are equivalent to the hematoma and perihematomal edema volumes of the manual quantification method. METHODS/DESIGN Hematoma and perihematomal edema volumes of supratentorial intracerebral hemorrhage on 252 computed tomography scans will be prospectively quantified in random order by six raters using the fully automated, semi-automated, and manual quantification methods. Primary outcome measures for hematoma and perihematomal edema volumes will be quantified via computed tomography scan on admission (<24 h from symptom onset) and on day 3 (72 ± 12 h from symptom onset), respectively. Equivalence hypothesis testing will be conducted to determine if the hematoma and perihematomal edema volume measurements of the fully automated and semi-automated quantification methods are within 7.5% of the hematoma and perihematomal edema volume measurements of the manual quantification reference method. DISCUSSION By allowing direct equivalence hypothesis testing, the Quantification of Hematoma and Perihematomal Edema Volumes in Intracerebral Hemorrhage study offers advantages over radiology validation studies which utilize measures of agreement to indirectly infer measurement equivalence and studies which mistakenly try to infer measurement equivalence based on the failure of a comparison two-sided null hypothesis test to reach the significance level for rejection. The equivalence hypothesis testing paradigm applied to artificial intelligence application validation is relatively uncharted and warrants further investigation. The challenges encountered in the design of this study may influence future studies seeking to translate artificial intelligence medical technology into clinical practice.
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Affiliation(s)
- Natasha Ironside
- Department of Neurological Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - James Patrie
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Sherman Ng
- Department of Software Engineering, Microsoft Corporation, Redmond, WA, USA
| | - Dale Ding
- Department of Neurosurgery, University of Louisville School of Medicine, Louisville, KY, USA
| | - Tanvir Rizvi
- Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Jeyan S Kumar
- Department of Neurological Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Panagiotis Mastorakos
- Department of Neurological Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Mohamed Z Hussein
- Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kareem El Naamani
- Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Rawad Abbas
- Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | | | - Yan Zhuang
- Department of Biomedical Engineering and Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA
| | - Kathryn N Kearns
- Department of Neurological Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kevin T Doan
- Department of Neurological Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Leah M Shabo
- Department of Neurological Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Saurabh Marfatiah
- Department of Radiology, Columbia University School of Medicine, New York, NY, USA
| | - David Roh
- Department of Neurology, Columbia University School of Medicine, New York, NY, USA
| | | | - Jan Claassen
- Department of Neurology, Columbia University School of Medicine, New York, NY, USA
| | - Bradford B Worrall
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.,Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Karen C Johnston
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.,Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Pascal Jabbour
- Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Min S Park
- Department of Neurological Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - E Sander Connolly
- Department of Neurosurgery, Columbia University School of Medicine, New York, NY, USA
| | - Sugoto Mukherjee
- Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Andrew M Southerland
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.,Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Ching-Jen Chen
- Department of Neurosurgery, The University of Texas Health Science Center, Houston, TX, USA
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26
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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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27
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28
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Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, Kim JA, Sharma R, Schiff SJ, Rosen MS, Kalpathy-Cramer J, Iglesias Gonzalez JE, Kimberly WT, Sheth KN. Deep Learning Applications for Acute Stroke Management. Ann Neurol 2022; 92:574-587. [PMID: 35689531 DOI: 10.1002/ana.26435] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/04/2022] [Indexed: 11/08/2022]
Abstract
Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter-clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice-focused primer on DL. Next, we examine real-world examples of DL applications in pixel-wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter-rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022.
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Affiliation(s)
- Isha R Chavva
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Anna L Crawford
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Mercy H Mazurek
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Matthew M Yuen
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | | | - Sam Payabvash
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Gordon Sze
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Charles C Matouk
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Jennifer A Kim
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Steven J Schiff
- Departments of Neurosurgery, Engineering Science and Mechanics and Physics, Penn State University, University Park, PA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Juan E Iglesias Gonzalez
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - W Taylor Kimberly
- Department of Neurology, Division of Neurocritical Care, Massachusetts General Hospital, Boston, MA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT
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29
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Bonkhoff AK, Grefkes C. Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain 2022; 145:457-475. [PMID: 34918041 PMCID: PMC9014757 DOI: 10.1093/brain/awab439] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 11/02/2021] [Accepted: 11/21/2021] [Indexed: 11/16/2022] Open
Abstract
Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.
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Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian Grefkes
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, University Hospital Cologne, Cologne, Germany
- Medical Faculty, University of Cologne, Cologne, Germany
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30
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Big Data and Artificial Intelligence for Precision Medicine in the Neuro-ICU: Bla, Bla, Bla. Neurocrit Care 2022; 37:163-165. [PMID: 35023043 PMCID: PMC9343268 DOI: 10.1007/s12028-021-01427-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 12/16/2021] [Indexed: 11/06/2022]
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31
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Intelligent, mobile stroke imaging. Nat Rev Neurol 2021; 18:66. [PMID: 34934173 DOI: 10.1038/s41582-021-00614-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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32
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Inamdar MA, Raghavendra U, Gudigar A, Chakole Y, Hegde A, Menon GR, Barua P, Palmer EE, Cheong KH, Chan WY, Ciaccio EJ, Acharya UR. A Review on Computer Aided Diagnosis of Acute Brain Stroke. SENSORS (BASEL, SWITZERLAND) 2021; 21:8507. [PMID: 34960599 PMCID: PMC8707263 DOI: 10.3390/s21248507] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/05/2021] [Accepted: 12/09/2021] [Indexed: 01/01/2023]
Abstract
Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.
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Affiliation(s)
- Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Udupi Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Yashas Chakole
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Ajay Hegde
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India; (A.H.); (G.R.M.)
| | - Girish R. Menon
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India; (A.H.); (G.R.M.)
| | - Prabal Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Elizabeth Emma Palmer
- School of Women’s and Children’s Health, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Research Imaging Centre, University of Malaya, Kuala Lumpur 59100, Malaysia;
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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33
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Shlobin NA, Baig AA, Waqas M, Patel TR, Dossani RH, Wilson No Degree M, Cappuzzo JM, Siddiqui AH, Tutino VM, Levy EI. Artificial Intelligence for Large Vessel Occlusion Stroke: A Systematic Review. World Neurosurg 2021; 159:207-220.e1. [PMID: 34896351 DOI: 10.1016/j.wneu.2021.12.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 12/17/2022]
Affiliation(s)
- Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ammad A Baig
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute at Kaleida Health, Buffalo, New York, USA
| | - Muhammad Waqas
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute at Kaleida Health, Buffalo, New York, USA
| | - Tatsat R Patel
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo NY USA
| | - Rimal H Dossani
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute at Kaleida Health, Buffalo, New York, USA
| | | | - Justin M Cappuzzo
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute at Kaleida Health, Buffalo, New York, USA
| | - Adnan H Siddiqui
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute at Kaleida Health, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Jacobs Institute, Buffalo, New York, USA; Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Vincent M Tutino
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo NY USA; Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo NY USA
| | - Elad I Levy
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute at Kaleida Health, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Jacobs Institute, Buffalo, New York, USA; Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA.
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Adhya J, Li C, Eisenmenger L, Cerejo R, Tayal A, Goldberg M, Chang W. Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience. Neuroradiol J 2021; 34:476-481. [PMID: 33906499 PMCID: PMC8559016 DOI: 10.1177/19714009211012353] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
PURPOSE Several new techniques have emerged for detecting anterior circulation large vessel occlusion by quantifying relative vessel density including RAPID-CTA, potentially allowing for faster triage and decreased time to mechanical thrombectomy. We present our one-year experience on positive predictive value of RAPID-CTA for the detection of large vessel occlusion in patients presenting with stroke symptoms and its effect on treatment time and clinical outcomes. MATERIALS AND METHODS Three hundred and ten patients presenting with stroke symptoms with relative vessel density <60% on RAPID-CTA were included (average age 70 years, 145 male, 165 female). Examinations were considered positive if there was evidence of large vessel occlusion or high grade stenosis. Computed tomography angiography to groin puncture time was calculated during one-year time intervals before and after RAPID-CTA installation. Ninety-day Modified Rankin Scale scores were obtained for patients in each cohort. RESULTS Of the 310 patients, 270 had large vessel occlusion or high grade stenosis (87% positive predictive value), with 161 having large vessel occlusion. Using 45% relative vessel density threshold, 129/161 large vessel occlusion were detected (80% sensitivity) and 163/172 examinations were positive (95% positive predictive value). Computed tomography angiography to groin puncture time was significantly lower after deployment of RAPID-CTA (93 min vs 68 min, p<0.05). Average 90 day modified Rankin Scale score was lower in the RAPID-CTA group with a higher percentage of patients with functional independence, although the data was not statistically significant. CONCLUSION RAPID-CTA had high positive predictive value for large vessel occlusion with a 45% relative vessel density threshold, which could facilitate active worklist reprioritization. Time to treatment was significantly lower and clinical outcomes were improved after deployment of RAPID-CTA.
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Affiliation(s)
- Julie Adhya
- Department of Radiology, Allegheny Health Network, USA
| | - Charles Li
- Department of Radiology, Allegheny Health Network, USA
| | - Laura Eisenmenger
- Department of Radiology, University of Wisconsin School of
Medicine and Public Health, USA
| | | | - Ashis Tayal
- Department of Neurology, Allegheny Health Network, USA
| | | | - Warren Chang
- Department of Radiology, Allegheny Health Network, USA
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Lin PJ, Jia T, Li C, Li T, Qian C, Li Z, Pan Y, Ji L. CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1936-1943. [PMID: 34516378 DOI: 10.1109/tnsre.2021.3112167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient's neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of prescribing BCI training accordingly. A Convolution Neural Network (CNN) based prognosis model was developed to predict the outcome of 11 stroke patients' recovery following a 2-week rehabilitation training with BCI. In our study, functional connectivity and power spectrum have been evaluated and applied as the inputs of CNN to regress patients' recovery rate. A saliency map was used to identify the correlation between EEG channels with the recovery outcome. The performance of our model was assessed using the leave-one-out cross-validation. Overall, the proposed model predicted patients' recovery with R2 0.98 and MSE 0.89. According to the saliency map, the highest functional connectivity occurred in Fp2/Fpz-AF8, Fp2/F4/F8-P3, P1/PO7-PO5 and AF3-AF4. Our results demonstrated that deep learning method has the potential to predict the recovery rate of BCI training, which contributes to guiding individualized prescription in the early stage of clinical rehabilitation.
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Ganesh A, Ospel JM, Marko M, van Zwam WH, Roos YBWEM, Majoie CBLM, Goyal M. From Three-Months to Five-Years: Sustaining Long-Term Benefits of Endovascular Therapy for Ischemic Stroke. Front Neurol 2021; 12:713738. [PMID: 34381418 PMCID: PMC8350336 DOI: 10.3389/fneur.2021.713738] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 06/28/2021] [Indexed: 11/28/2022] Open
Abstract
Background and Purpose: During the months and years post-stroke, treatment benefits from endovascular therapy (EVT) may be magnified by disability-related differences in morbidity/mortality or may be eroded by recurrent strokes and non-stroke-related disability/mortality. Understanding the extent to which EVT benefits may be sustained at 5 years, and the factors influencing this outcome, may help us better promote the sustenance of EVT benefits until 5 years post-stroke and beyond. Methods: In this review, undertaken 5 years after EVT became the standard of care, we searched PubMed and EMBASE to examine the current state of the literature on 5-year post-stroke outcomes, with particular attention to modifiable factors that influence outcomes between 3 months and 5 years post-EVT. Results: Prospective cohorts and follow-up data from EVT trials indicate that 3-month EVT benefits will likely translate into lower 5-year disability, mortality, institutionalization, and care costs and higher quality of life. However, these group-level data by no means guarantee maintenance of 3-month benefits for individual patients. We identify factors and associated “action items” for stroke teams/systems at three specific levels (medical care, individual psychosocioeconomic, and larger societal/environmental levels) that influence the long-term EVT outcome of a patient. Medical action items include optimizing stroke rehabilitation, clinical follow-up, secondary stroke prevention, infection prevention/control, and post-stroke depression care. Psychosocioeconomic aspects include addressing access to primary care, specialist clinics, and rehabilitation; affordability of healthy lifestyle choices and preventative therapies; and optimization of family/social support and return-to-work options. High-level societal efforts include improving accessibility of public/private spaces and transportation, empowering/engaging persons with disability in society, and investing in treatments/technologies to mitigate consequences of post-stroke disability. Conclusions: In the longtime horizon from 3 months to 5 years, several factors in the medical and societal spheres could negate EVT benefits. However, many factors can be leveraged to preserve or magnify treatment benefits, with opportunities to share responsibility with widening circles of care around the patient.
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Affiliation(s)
- Aravind Ganesh
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | | | - Martha Marko
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Wim H van Zwam
- Department of Radiology, Maastricht University Medical Centre, Maastricht, Netherlands
| | | | | | - Mayank Goyal
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Department of Radiology, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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Corrias G, Mazzotta A, Melis M, Cademartiri F, Yang Q, Suri JS, Saba L. Emerging role of artificial intelligence in stroke imaging. Expert Rev Neurother 2021; 21:745-754. [PMID: 34282975 DOI: 10.1080/14737175.2021.1951234] [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: 10/20/2022]
Abstract
Introduction: The recognition and therapy of patients with stroke is becoming progressively intricate as additional treatment choices become accessible and new associations between disease characteristics and treatment response are incessantly uncovered. Therefore, clinicians must regularly learn new skill, stay up to date with the literature and integrate advances into daily practice. The application of artificial intelligence (AI) to assist clinical decision making could diminish inter-rater variation in routine clinical practice and accelerate the mining of vital data that could expand recognition of patients with stroke, forecast of treatment responses and patient outcomes.Areas covered: In this review, the authors provide an up-to-date review of AI in stroke, analyzing the latest papers on this subject. These have been divided in two main groups: stroke diagnosis and outcome prediction.Expert opinion: The highest value of AI is its capability to merge, select and condense a large amount of clinical and imaging features of a single patient and to associate these with fitted models that have gone through robust assessment and optimization with large cohorts of data to support clinical decision making.
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Affiliation(s)
- Giuseppe Corrias
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Di Cagliari - Polo Di Monserrato, S.s. 554 Monserrato (Cagliari), Italy
| | - Andrea Mazzotta
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Di Cagliari - Polo Di Monserrato, S.s. 554 Monserrato (Cagliari), Italy
| | - Marta Melis
- Department of Neurology, Azienda Ospedaliero Universitaria (A.O.U.), Di Cagliari - Cagliari, Italy
| | | | - Qi Yang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Di Cagliari - Polo Di Monserrato, S.s. 554 Monserrato (Cagliari), Italy
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Yeo M, Tahayori B, Kok HK, Maingard J, Kutaiba N, Russell J, Thijs V, Jhamb A, Chandra RV, Brooks M, Barras CD, Asadi H. Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging. J Neurointerv Surg 2021; 13:369-378. [PMID: 33479036 DOI: 10.1136/neurintsurg-2020-017099] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/05/2020] [Accepted: 12/09/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting.
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Affiliation(s)
- Melissa Yeo
- Melbourne Medical School, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia
| | - Bahman Tahayori
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,IBM Research Australia, Melbourne, Victoria, Australia
| | - Hong Kuan Kok
- Department of Radiology, Northern Health, Epping, Victoria, Australia.,School of Medicine, Deakin University Faculty of Health, Burwood, Victoria, Australia
| | - Julian Maingard
- School of Medicine, Deakin University Faculty of Health, Burwood, Victoria, Australia.,Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
| | - Numan Kutaiba
- Department of Radiology, Austin Health, Heidelberg, Victoria, Australia
| | - Jeremy Russell
- Department of Neurosurgery, Austin Health, Heidelberg, Victoria, Australia
| | - Vincent Thijs
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
| | - Ashu Jhamb
- Department of Radiology, St Vincent's Hospital Melbourne Pty Ltd, Fitzroy, 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
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Interventional Neuroradiology Service, Austin Health, Heidelberg, Victoria, Australia
| | - Christen D Barras
- School of Medicine, The University of Adelaide, Adelaide, South Australia, Australia.,South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Hamed Asadi
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
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