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Liu Y, Wen Z, Wang Y, Zhong Y, Wang J, Hu Y, Zhou P, Guo S. Artificial intelligence in ischemic stroke images: current applications and future directions. Front Neurol 2024; 15:1418060. [PMID: 39050128 PMCID: PMC11266078 DOI: 10.3389/fneur.2024.1418060] [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: 04/16/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.
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Affiliation(s)
- Ying Liu
- School of Nursing, Southwest Medical University, Luzhou, China
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Zhongjian Wen
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Applications Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Yiren Wang
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Applications Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Yuxin Zhong
- School of Nursing, Guizhou Medical University, Guiyang, China
| | - Jianxiong Wang
- Department of Rehabilitation, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yiheng Hu
- Department of Medical Imaging, Southwest Medical University, Luzhou, China
| | - Ping Zhou
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Shengmin Guo
- Nursing Department, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Lee HJ, Schwamm LH, Sansing LH, Kamel H, de Havenon A, Turner AC, Sheth KN, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. NPJ Digit Med 2024; 7:130. [PMID: 38760474 PMCID: PMC11101464 DOI: 10.1038/s41746-024-01120-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 04/23/2024] [Indexed: 05/19/2024] Open
Abstract
Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.
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Affiliation(s)
- Ho-Joon Lee
- Department of Genetics and Yale Center for Genome Analysis, Yale School of Medicine, New Haven, CT, USA.
| | - Lee H Schwamm
- Department of Neurology and Comprehensive Stroke Center, Massachusetts General Hospital and Harvard Medical School Boston, Boston, MA, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Lauren H Sansing
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Hooman Kamel
- Department of Neurology, Weill Cornell Medicine, New York City, NY, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Ashby C Turner
- Department of Neurology and Comprehensive Stroke Center, Massachusetts General Hospital and Harvard Medical School Boston, Boston, MA, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Smita Krishnaswamy
- Departments of Genetics and Computer Science, Yale School of Medicine, New Haven, CT, USA
| | - Cynthia Brandt
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
| | - Hongyu Zhao
- Departments of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Harlan Krumholz
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
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Habibi D, Teymoori F, Ebrahimi N, Fateh ST, Najd-Hassan-Bonab L, Saeidian AH, Soleymani Taloubaghi A, Asgarian S, Hosseinpanah F, Hakonarson H, Azizi F, Hedayati M, Daneshpour MS, Akbarzadeh M, Mansourian M. Causal effect of serum 25 hydroxyvitamin D concentration on cardioembolic stroke: Evidence from two-sample Mendelian randomization. Nutr Metab Cardiovasc Dis 2024; 34:1305-1313. [PMID: 38508993 DOI: 10.1016/j.numecd.2024.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND AND AIMS The putative association between serum 25-hydroxyvitamin D concentration [25(OH)D] and the risk of cardioembolic stroke (CES) has been examined in observational studies, which indicate controversial findings. We performed Mendelian randomization (MR) analysis to determine the causal relationship of serum 25(OH)D with the risk of CES. METHODS AND RESULTS The summary statistics dataset on the genetic variants related to 25(OH)D was used from the published GWAS of European descent participants in the UK Biobank, including 417,580 subjects, yielding 143 independent loci in 112 1-Mb regions. GWAS summary data of CES was obtained from GIGASTROKE Consortium, which included European individuals (10,804 cases, 1,234,808 controls). Our results unveiled a causal relationship between 25(OH)D and CES using IVW [OR = 0.82, 95% CI: 0.67-0.98, p = 0.037]. Horizontal pleiotropy was not seen [MR-Egger intercept = 0.001; p = 0.792], suggesting an absence of horizontal pleiotropy. Cochrane's Q [Q = 78.71, p-value = 0.924], Rucker's Q [Q = 78.64, p-value = 0.913], and I2 = 0.0% (95% CI: 0.0%, 24.6%) statistic suggested no heterogeneity. This result remained consistent using different MR methods and sensitivity analyses, including Maximum likelihood [OR = 0.82, 95%CI: 0.67-0.98, p-value = 0.036], Constrained maximum likelihood [OR = 0.76, 95%CI: 0.64-0.90, p-value = 0.002], Debiased inverse-variance weighted [OR = 0.82, 95%CI: 0.68-0.99, p-value = 0.002], MR-PRESSO [OR = 0.82, 95%CI 0.77-0.87, p-value = 0.022], RAPS [OR = 0.82, 95%CI 0.67-0.98, p-value = 0.038], MR-Lasso [OR = 0.82, 95%CI 0.68-0.99, p-value = 0.037]. CONCLUSION Our MR analysis provides suggestive evidence that increased 25(OH)D levels may play a protective role in the development of cardioembolic stroke. Determining the role of 25(OH)D in stroke subtypes has important clinical and public health implications.
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Affiliation(s)
- Danial Habibi
- Department of Biostatistics and Epidemiology, School of Health, and Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Farshad Teymoori
- Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti, Tehran, Iran; Department of Nutrition, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
| | - Navid Ebrahimi
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | | | - Leila Najd-Hassan-Bonab
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Amir Hossein Saeidian
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Abramson Research Building, Suite 1016I, 3615 Civic Center Boulevard, Philadelphia, PA, 19104-4318, USA.
| | - Alireza Soleymani Taloubaghi
- Data Science and AI Applications, Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliu, Taiwan.
| | - Sara Asgarian
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Farhad Hosseinpanah
- Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Abramson Research Building, Suite 1016I, 3615 Civic Center Boulevard, Philadelphia, PA, 19104-4318, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mehdi Hedayati
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Maryam Sadat Daneshpour
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mahdi Akbarzadeh
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Marjan Mansourian
- Epidemiology and Biostatistics Department, School of Health, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
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Saba L, Cau R, Spinato G, Suri JS, Melis M, De Rubeis G, Antignani P, Gupta A. Carotid stenosis and cryptogenic stroke. J Vasc Surg 2024; 79:1119-1131. [PMID: 38190926 DOI: 10.1016/j.jvs.2024.01.004] [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: 12/13/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 01/10/2024]
Abstract
OBJECTIVES Cryptogenic stroke represents a type of ischemic stroke with an unknown origin, presenting a significant challenge in both stroke management and prevention. According to the Trial of Org 10,172 in Acute Stroke Treatment criteria, a stroke is categorized as being caused by large artery atherosclerosis only when there is >50% luminal narrowing of the ipsilateral internal carotid artery. However, nonstenosing carotid artery plaques can be an underlying cause of ischemic stroke. Indeed, emerging evidence documents that some features of plaque vulnerability may act as an independent risk factor, regardless of the degree of stenosis, in precipitating cerebrovascular events. This review, drawing from an array of imaging-based studies, explores the predictive values of carotid imaging modalities in the detection of nonstenosing carotid plaque (<50%), that could be the cause of a cerebrovascular event when some features of vulnerability are present. METHODS Google Scholar, Scopus, and PubMed were searched for articles on cryptogenic stroke and those reporting the association between cryptogenic stroke and imaging features of carotid plaque vulnerability. RESULTS Despite extensive diagnostic evaluations, the etiology of a considerable proportion of strokes remains undetermined, contributing to the recurrence rate and persistent morbidity in affected individuals. Advances in imaging modalities, such as magnetic resonance imaging, computed tomography scans, and ultrasound examination, facilitate more accurate detection of nonstenosing carotid artery plaque and allow better stratification of stroke risk, leading to a more tailored treatment strategy. CONCLUSIONS Early detection of nonstenosing carotid plaque with features of vulnerability through carotid imaging techniques impacts the clinical management of cryptogenic stroke, resulting in refined stroke subtype classification and improved patient management. Additional research is required to validate these findings and recommend the integration of these state-of-the-art imaging methodologies into standard diagnostic protocols to improve stroke management and prevention.
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Affiliation(s)
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy.
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Giacomo Spinato
- Department of Neurosciences, Section of Otolaryngology and Regional Centre for Head and Neck Cancer, University of Padova, Treviso, Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA
| | - Marta Melis
- Department of Neurology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
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Miceli G, Basso MG, Pintus C, Pennacchio AR, Cocciola E, Cuffaro M, Profita M, Rizzo G, Tuttolomondo A. Molecular Pathways of Vulnerable Carotid Plaques at Risk of Ischemic Stroke: A Narrative Review. Int J Mol Sci 2024; 25:4351. [PMID: 38673936 PMCID: PMC11050267 DOI: 10.3390/ijms25084351] [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: 02/26/2024] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
The concept of vulnerable carotid plaques is pivotal in understanding the pathophysiology of ischemic stroke secondary to large-artery atherosclerosis. In macroscopic evaluation, vulnerable plaques are characterized by one or more of the following features: microcalcification; neovascularization; lipid-rich necrotic cores (LRNCs); intraplaque hemorrhage (IPH); thin fibrous caps; plaque surface ulceration; huge dimensions, suggesting stenosis; and plaque rupture. Recognizing these macroscopic characteristics is crucial for estimating the risk of cerebrovascular events, also in the case of non-significant (less than 50%) stenosis. Inflammatory biomarkers, such as cytokines and adhesion molecules, lipid-related markers like oxidized low-density lipoprotein (LDL), and proteolytic enzymes capable of degrading extracellular matrix components are among the key molecules that are scrutinized for their associative roles in plaque instability. Through their quantification and evaluation, these biomarkers reveal intricate molecular cross-talk governing plaque inflammation, rupture potential, and thrombogenicity. The current evidence demonstrates that plaque vulnerability phenotypes are multiple and heterogeneous and are associated with many highly complex molecular pathways that determine the activation of an immune-mediated cascade that culminates in thromboinflammation. This narrative review provides a comprehensive analysis of the current knowledge on molecular biomarkers expressed by symptomatic carotid plaques. It explores the association of these biomarkers with the structural and compositional attributes that characterize vulnerable plaques.
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Affiliation(s)
- Giuseppe Miceli
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy; (G.M.); (M.G.B.); (C.P.); (A.R.P.); (E.C.); (M.C.); (M.P.); (G.R.)
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico “P. Giaccone”, 90127 Palermo, Italy
| | - Maria Grazia Basso
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy; (G.M.); (M.G.B.); (C.P.); (A.R.P.); (E.C.); (M.C.); (M.P.); (G.R.)
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico “P. Giaccone”, 90127 Palermo, Italy
| | - Chiara Pintus
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy; (G.M.); (M.G.B.); (C.P.); (A.R.P.); (E.C.); (M.C.); (M.P.); (G.R.)
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico “P. Giaccone”, 90127 Palermo, Italy
| | - Andrea Roberta Pennacchio
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy; (G.M.); (M.G.B.); (C.P.); (A.R.P.); (E.C.); (M.C.); (M.P.); (G.R.)
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico “P. Giaccone”, 90127 Palermo, Italy
| | - Elena Cocciola
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy; (G.M.); (M.G.B.); (C.P.); (A.R.P.); (E.C.); (M.C.); (M.P.); (G.R.)
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico “P. Giaccone”, 90127 Palermo, Italy
| | - Mariagiovanna Cuffaro
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy; (G.M.); (M.G.B.); (C.P.); (A.R.P.); (E.C.); (M.C.); (M.P.); (G.R.)
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico “P. Giaccone”, 90127 Palermo, Italy
| | - Martina Profita
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy; (G.M.); (M.G.B.); (C.P.); (A.R.P.); (E.C.); (M.C.); (M.P.); (G.R.)
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico “P. Giaccone”, 90127 Palermo, Italy
| | - Giuliana Rizzo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy; (G.M.); (M.G.B.); (C.P.); (A.R.P.); (E.C.); (M.C.); (M.P.); (G.R.)
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico “P. Giaccone”, 90127 Palermo, Italy
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy; (G.M.); (M.G.B.); (C.P.); (A.R.P.); (E.C.); (M.C.); (M.P.); (G.R.)
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico “P. Giaccone”, 90127 Palermo, Italy
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Zhao Z, Zhang Y, Su J, Yang L, Pang L, Gao Y, Wang H. A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke. Front Neurol 2024; 15:1367854. [PMID: 38606275 PMCID: PMC11007047 DOI: 10.3389/fneur.2024.1367854] [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: 01/09/2024] [Accepted: 03/08/2024] [Indexed: 04/13/2024] Open
Abstract
Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.
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Affiliation(s)
- Zijian Zhao
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yuanyuan Zhang
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Jiuhui Su
- Department of Orthopedics, Haicheng Bonesetting Hospital, Haicheng, Liaoning Province, China
| | - Lianbo Yang
- Department of Reparative and Reconstructive Surgery, The Second Hospital of Dalian Medical University, Dalian Liaoning Province, China
| | - Luhang Pang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yingshan Gao
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
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Oura D, Gekka M, Sugimori H. The montage method improves the classification of suspected acute ischemic stroke using the convolution neural network and brain MRI. Radiol Phys Technol 2024; 17:297-305. [PMID: 37934345 DOI: 10.1007/s12194-023-00754-x] [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: 05/29/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023]
Abstract
This study investigated the usefulness of the montage method that combines four different magnetic resonance images into one images for automatic acute ischemic stroke (AIS) diagnosis with deep learning method. The montage image was consisted from diffusion weighted image (DWI), fluid attenuated inversion recovery (FLAIR), arterial spin labeling (ASL), and apparent diffusion coefficient (ASL). The montage method was compared with pseudo color map (pCM) which was consisted from FLAIR, ASL and ADC. 473 AIS patients were classified into four categories: mechanical thrombectomy, conservative therapy, hemorrhage, and other diseases. The results showed that the montage image significantly outperformed pCM in terms of accuracy (montage image = 0.76 ± 0.01, pCM = 0.54 ± 0.05) and the area under the curve (AUC) (montage image = 0.94 ± 0.01, pCM = 0.76 ± 0.01). This study demonstrates the usefulness of the montage method and its potential for overcoming the limitations of pCM.
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Affiliation(s)
- Daisuke Oura
- Department of Radiology, Otaru General Hospital, Otaru, 047-0152, Japan
- Graduate School of Health Sciences, Hokkaido University, Sapporo, 060-0812, Japan
| | - Masayuki Gekka
- Department of Neurosurgery, Otaru General Hospital, Otaru, 047-0152, Japan
| | - Hiroyuki Sugimori
- Faculty of Health Sciences, Hokkaido University, Sapporo, 060-0812, Japan.
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Carrillo Navarrete KA, Chapa González C. Hemiplegia in acute ischemic stroke: A comprehensive review of case studies and the role of intravenous thrombolysis and mechanical thrombectomy. IBRAIN 2024; 10:59-68. [PMID: 38682021 PMCID: PMC11045183 DOI: 10.1002/ibra.12146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 05/01/2024]
Abstract
Acute ischemic stroke is a significant health concern worldwide, often leading to long-term disability and decreased quality of life. Rapid and appropriate treatment is crucial for achieving optimal outcomes in these patients. Intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) are two commonly used interventions for acute ischemic stroke, but their effectiveness in improving neurological symptoms and functional outcomes in patients with hemiplegia remains uncertain. The aim of this work was to evaluate the impact of IVT and MT within a 4.5-h time frame on patients with acute ischemic stroke and hemiplegia. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Relevant studies that assessed the impact of IVT and MT within 4.5-h on hemiplegia in patients with an acute ischemic stroke were included. Data were extracted and analyzed to determine the overall effects of these interventions. Most included case reports indicate positive outcomes in terms of neurological symptom improvement and functional recovery in patients with hemiplegia after receiving IVT and MT within the specified time frame. However, the heterogeneity among the patients and the limited use of IVT due to contraindications posed challenges in determining the most effective treatment option. The findings from the included studies demonstrate that both interventions led to a decrease in National Institutes of Health Stroke Scale scores, indicating an improvement in neurological symptoms. The results highlight the beneficial effects of early thrombolytic interventions and MT on the neurological status and functional outcomes of patients with an acute ischemic stroke.
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Affiliation(s)
- Karen Adriana Carrillo Navarrete
- Instituto de Ingeniería y TecnologíaUniversidad Autónoma de Ciudad JuárezCiudad Juárez, ChihuahuaMéxico
- Grupo de Nanomedicina, Laboratorio de Integración de Datos y Evidencia en Revisiones de Salud y Ciencia, LIDERSCUniversidad Autónoma de Ciudad JuárezCiudad Juárez, ChihuahuaMéxico
| | - Christian Chapa González
- Instituto de Ingeniería y TecnologíaUniversidad Autónoma de Ciudad JuárezCiudad Juárez, ChihuahuaMéxico
- Grupo de Nanomedicina, Laboratorio de Integración de Datos y Evidencia en Revisiones de Salud y Ciencia, LIDERSCUniversidad Autónoma de Ciudad JuárezCiudad Juárez, ChihuahuaMéxico
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Miyamoto N, Ueno Y, Yamashiro K, Hira K, Kijima C, Kitora N, Iwao Y, Okuda K, Mishima S, Takahashi D, Ono K, Asari M, Miyazaki K, Hattori N. Stroke classification and treatment support system artificial intelligence for usefulness of stroke diagnosis. Front Neurol 2023; 14:1295642. [PMID: 38156087 PMCID: PMC10753815 DOI: 10.3389/fneur.2023.1295642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/01/2023] [Indexed: 12/30/2023] Open
Abstract
Background and aims It is important to diagnose cerebral infarction at an early stage and select an appropriate treatment method. The number of stroke-trained physicians is unevenly distributed; thus, a shortage of specialists is a major problem in some regions. In this retrospective design study, we tested whether an artificial intelligence (AI) we built using computer-aided detection/diagnosis may help medical physicians to classify stroke for the appropriate treatment. Methods To build the Stroke Classification and Treatment Support System AI, the clinical data of 231 hospitalized patients with ischemic stroke from January 2016 to December 2017 were used for training the AI. To verify the diagnostic accuracy, 151 patients who were admitted for stroke between January 2018 and December 2018 were also enrolled. Results By utilizing multimodal data, such as DWI and ADC map images, as well as patient examination data, we were able to construct an AI that can explain the analysis results with a small amount of training data. Furthermore, the AI was able to classify with high accuracy (Cohort 1, evaluation data 88.7%; Cohort 2, validation data 86.1%). Conclusion In recent years, the treatment options for cerebral infarction have increased in number and complexity, making it even more important to provide appropriate treatment according to the initial diagnosis. This system could be used for initial treatment to automatically diagnose and classify strokes in hospitals where stroke-trained physicians are not available and improve the prognosis of cerebral infarction.
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Affiliation(s)
- Nobukazu Miyamoto
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Yuji Ueno
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Kazuo Yamashiro
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Kenichiro Hira
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Chikage Kijima
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | | | | | | | | | | | - Kazuto Ono
- Ohara Pharmaceutical Co., Ltd., Tokyo, Japan
| | - Mika Asari
- PARKINSON Laboratories Co., Ltd., Tokyo, Japan
| | | | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
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Lee HJ, Schwamm LH, Sansing L, Kamel H, de Havenon A, Turner AC, Sheth KN, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records. RESEARCH SQUARE 2023:rs.3.rs-3367169. [PMID: 37961532 PMCID: PMC10635373 DOI: 10.21203/rs.3.rs-3367169/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists' review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 (±0.01) and weighted F1 of 0.74 (±0.01). In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70 and 0.71, respectively. SHapley Additive exPlanation analysis elucidated that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2% to 7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.
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Affiliation(s)
- Ho-Joon Lee
- Department of Genetics and Yale Center for Genome Analysis, Yale School of Medicine, New Haven, CT
| | - Lee H. Schwamm
- Department of Neurology and Comprehensive Stroke Center, Massachusetts General Hospital and Harvard Medical School Boston, MA
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Lauren Sansing
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Hooman Kamel
- Department of Neurology, Weill Cornell Medicine, New York City, NY
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Ashby C. Turner
- Department of Neurology and Comprehensive Stroke Center, Massachusetts General Hospital and Harvard Medical School Boston, MA
| | - Kevin N. Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Smita Krishnaswamy
- Departments of Genetics and Computer Science, Yale School of Medicine, New Haven, CT
| | - Cynthia Brandt
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
| | - Hongyu Zhao
- Departments of Biostatistics, Yale School of Public Health, New Haven, CT
| | - Harlan Krumholz
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT
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11
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Abujaber AA, Albalkhi I, Imam Y, Nashwan AJ, Yaseen S, Akhtar N, Alkhawaldeh IM. Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning. J Pers Med 2023; 13:1555. [PMID: 38003870 PMCID: PMC10672468 DOI: 10.3390/jpm13111555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/26/2023] [Accepted: 10/11/2023] [Indexed: 11/26/2023] Open
Abstract
(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar's stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.
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Affiliation(s)
- Ahmad A. Abujaber
- Nursing Department, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar
| | - Ibrahem Albalkhi
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
- Department of Neuroradiology, Great Ormond Street Hospital NHS Foundation Trust, Great Ormond St., London WC1N 3JH, UK
| | - Yahia Imam
- Neurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar
| | | | - Said Yaseen
- School of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Naveed Akhtar
- Neurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar
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