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Xu F, Dai Z, Ye Y, Hu P, Cheng H. Bibliometric and visualized analysis of the application of artificial intelligence in stroke. Front Neurosci 2024; 18:1411538. [PMID: 39323917 PMCID: PMC11422388 DOI: 10.3389/fnins.2024.1411538] [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: 04/03/2024] [Accepted: 08/29/2024] [Indexed: 09/27/2024] Open
Abstract
Background Stroke stands as a prominent cause of mortality and disability worldwide, posing a major public health concern. Recent years have witnessed rapid advancements in artificial intelligence (AI). Studies have explored the utilization of AI in imaging analysis, assistive rehabilitation, treatment, clinical decision-making, and outcome and risk prediction concerning stroke. However, there is still a lack of systematic bibliometric analysis to discern the current research status, hotspots, and possible future development trends of AI applications in stroke. Methods The publications on the application of AI in stroke were retrieved from the Web of Science Core Collection, spanning 2004-2024. Only articles or reviews published in English were included in this study. Subsequently, a manual screening process was employed to eliminate literature not pertinent to the topic. Visualization diagrams for comprehensive and in-depth analysis of the included literature were generated using CiteSpace, VOSviewer, and Charticulator. Results This bibliometric analysis included a total of 2,447 papers, and the annual publication volume shows a notable upward trajectory. The most prolific authors, countries, and institutions are Dukelow, Sean P., China, and the University of Calgary, respectively, making significant contributions to the advancement of this field. Notably, stable collaborative networks among authors and institutions have formed. Through clustering and citation burst analysis of keywords and references, the current research hotspots have been identified, including machine learning, deep learning, and AI applications in stroke rehabilitation and imaging for early diagnosis. Moreover, emerging research trends focus on machine learning as well as stroke outcomes and risk prediction. Conclusion This study provides a comprehensive and in-depth analysis of the literature regarding AI in stroke, facilitating a rapid comprehension of the development status, cooperative networks, and research priorities within the field. Furthermore, our analysis may provide a certain reference and guidance for future research endeavors.
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Affiliation(s)
- Fangyuan Xu
- The First Clinical Medical School, Anhui University of Chinese Medicine, Hefei, China
| | - Ziliang Dai
- Department of Rehabilitation Medicine, The Second Hospital of Wuhan Iron and Steel (Group) Corp., Wuhan, China
| | - Yu Ye
- The Second Clinical Medical School, Anhui University of Chinese Medicine, Hefei, China
| | - Peijia Hu
- The Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
| | - Hongliang Cheng
- The Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
- Anhui Province Key Laboratory of Meridian Viscera Correlationship, Hefei, China
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2
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He H, Liu J, Li C, Guo Y, Liang K, Du J, Xue J, Liang Y, Chen P, Liu L, Cui M, Wang J, Liu Y, Tian S, Deng Y. Predicting Hematoma Expansion and Prognosis in Cerebral Contusions: A Radiomics-Clinical Approach. J Neurotrauma 2024; 41:1337-1352. [PMID: 38326935 DOI: 10.1089/neu.2023.0410] [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: 02/09/2024] Open
Abstract
Hemorrhagic progression of contusion (HPC) often occurs early in cerebral contusions (CC) patients, significantly impacting their prognosis. It is vital to promptly assess HPC and predict outcomes for effective tailored interventions, thereby enhancing prognosis in CC patients. We utilized the Attention-3DUNet neural network to semi-automatically segment hematomas from computed tomography (CT) images of 452 CC patients, incorporating 695 hematomas. Subsequently, 1502 radiomic features were extracted from 358 hematomas in 261 patients. After a selection process, these features were used to calculate the radiomic signature (Radscore). The Radscore, along with clinical features such as medical history, physical examinations, laboratory results, and radiological findings, was employed to develop predictive models. For prognosis (discharge Glasgow Outcome Scale score), radiomic features of each hematoma were augmented and fused for correlation. We employed various machine learning methodologies to create both a combined model, integrating radiomics and clinical features, and a clinical-only model. Nomograms based on logistic regression were constructed to visually represent the predictive procedure, and external validation was performed on 170 patients from three additional centers. The results showed that for HPC, the combined model, incorporating hemoglobin levels, Rotterdam CT score of 3, multi-hematoma fuzzy sign, concurrent subdural hemorrhage, international normalized ratio, and Radscore, achieved area under the receiver operating characteristic curve (AUC) values of 0.848 and 0.836 in the test and external validation cohorts, respectively. The clinical model predicting prognosis, utilizing age, Abbreviated Injury Scale for the head, Glasgow Coma Scale Motor component, Glasgow Coma Scale Verbal component, albumin, and Radscore, attained AUC values of 0.846 and 0.803 in the test and external validation cohorts, respectively. Selected radiomic features indicated that irregularly shaped and highly heterogeneous hematomas increased the likelihood of HPC, while larger weighted axial lengths and lower densities of hematomas were associated with a higher risk of poor prognosis. Predictive models that combine radiomic and clinical features exhibit robust performance in forecasting HPC and the risk of poor prognosis in CC patients. Radiomic features complement clinical features in predicting HPC, although their ability to enhance the predictive accuracy of the clinical model for adverse prognosis is limited.
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Affiliation(s)
- Haoyue He
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Jinxin Liu
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
- School of Medicine, Chongqing University, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Yi Guo
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Kaixin Liang
- Department of Neurosurgery, Yubei District Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Jun Du
- Department of Neurosurgery, Chongqing Qianjiang Central Hospital, Chongqing University Qianjiang Hospital, Chongqing, China
| | - Jun Xue
- Department of Neurosurgery, Bishan Hospital of Chongqing, Bishan Hospital of Chongqing Medical University, Chongqing, China
| | - Yidan Liang
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Peng Chen
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Liu Liu
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Min Cui
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Jia Wang
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Ye Liu
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
- School of Medicine, Chongqing University, Chongqing, China
| | - Shanshan Tian
- Department of Prehospital Emergency, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Yongbing Deng
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
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3
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Liu F, Yao Y, Zhu B, Yu Y, Ren R, Hu Y. The novel imaging methods in diagnosis and assessment of cerebrovascular diseases: an overview. Front Med (Lausanne) 2024; 11:1269742. [PMID: 38660416 PMCID: PMC11039813 DOI: 10.3389/fmed.2024.1269742] [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: 07/30/2023] [Accepted: 03/27/2024] [Indexed: 04/26/2024] Open
Abstract
Cerebrovascular diseases, including ischemic strokes, hemorrhagic strokes, and vascular malformations, are major causes of morbidity and mortality worldwide. The advancements in neuroimaging techniques have revolutionized the field of cerebrovascular disease diagnosis and assessment. This comprehensive review aims to provide a detailed analysis of the novel imaging methods used in the diagnosis and assessment of cerebrovascular diseases. We discuss the applications of various imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and angiography, highlighting their strengths and limitations. Furthermore, we delve into the emerging imaging techniques, including perfusion imaging, diffusion tensor imaging (DTI), and molecular imaging, exploring their potential contributions to the field. Understanding these novel imaging methods is necessary for accurate diagnosis, effective treatment planning, and monitoring the progression of cerebrovascular diseases.
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Affiliation(s)
- Fei Liu
- Neuroscience Intensive Care Unit, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Yao
- Neuroscience Intensive Care Unit, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Bingcheng Zhu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yue Yu
- Neuroscience Intensive Care Unit, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Reng Ren
- Neuroscience Intensive Care Unit, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yinghong Hu
- Neuroscience Intensive Care Unit, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 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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5
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Wu M, Yu K, Zhao Z, Zhu B. Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis. Heliyon 2024; 10:e24230. [PMID: 38288018 PMCID: PMC10823080 DOI: 10.1016/j.heliyon.2024.e24230] [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: 04/18/2023] [Revised: 11/23/2023] [Accepted: 01/04/2024] [Indexed: 01/31/2024] Open
Abstract
Objective Machine learning (ML) models have been widely applied in stroke prediction, diagnosis, treatment, and prognosis assessment. We aimed to conduct a comprehensive scientometrics analysis of studies related to ML in stroke and reveal its current status, knowledge structure, and global trends. Methods All documents related to ML in stroke were retrieved from the Web of Science database on March 15, 2023. We refined the documents by including only original articles and reviews in the English language. The literature published over the past decade was imported into scientometrics software for influence detection and collaborative network analysis. Results 2389 related publications were included. The annual publication outputs demonstrated explosive growth, with an average growth rate of 63.99 %. Among the 90 countries/regions involved, the United States (729 articles) and China (636 articles) were the most productive countries. Frontiers in Neurology was the most prolific journal with 94 articles. 234 highly cited articles, each with more than 31 citations, were detected. Keyword analysis revealed a total of 5333 keywords, with a predominant focus on the application of ML models in the early diagnosis, classification, and prediction of "acute ischemic stroke" and "atrial fibrillation-related stroke". The keyword "classification" had the first and longest burst, spanning from 2013 to 2018. 'Upport vector machine' got the strongest burst strength with 6.2. Keywords such as 'mechanical thrombectomy', 'expression', and 'prognosis' experienced bursts in 2022 and have continued to be prominent. Conclusion The applications of ML in stroke are increasingly diverse and extensive, with researchers showing growing interest over the past decade. However, the clinical application of ML in stroke is still in its early stages, and several limitations and challenges need to be addressed for its widespread adoption in clinical practice.
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Affiliation(s)
- Mingfen Wu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Kefu Yu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zhigang Zhao
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Bin Zhu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
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6
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Jiang YL, Zhao QS, Li A, Wu ZB, Liu LL, Lin F, Li YF. Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis. Clin Appl Thromb Hemost 2024; 30:10760296241279800. [PMID: 39262220 PMCID: PMC11409297 DOI: 10.1177/10760296241279800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 08/02/2024] [Accepted: 08/16/2024] [Indexed: 09/13/2024] Open
Abstract
Background: Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accurate prediction to optimize patient care. Methods: A comprehensive search was conducted across PubMed, Web of Science, Scopus, Embase, and Google Scholar, covering studies from inception until July 10, 2024. Studies were included if they used machine learning (ML) or deep learning algorithms to predict HT in AIS patients treated with thrombolysis. Exclusion criteria included studies involving endovascular treatments and those not evaluating model effectiveness. Data extraction and quality assessment were performed following PRISMA guidelines and using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST) tools. Results: Out of 1943 identified records, 12 studies were included in the final analysis, encompassing 18 007 AIS patients who received thrombolytic therapy. The ML models demonstrated high predictive performance, with pooled area under the curve (AUC) values ranging from 0.79 to 0.95. Specifically, XGBoost models achieved AUCs of up to 0.953 and Artificial Neural Network (ANN) models reached up to 0.942. Sensitivity and specificity varied significantly, with the highest sensitivity at 0.90 and specificity at 0.99. Significant predictors of HT included age, glucose levels, NIH Stroke Scale (NIHSS) score, systolic and diastolic blood pressure, and radiomic features. Despite these promising results, methodological disparities and limited external validation highlighted the need for standardized reporting and further rigorous testing. Conclusion: ML techniques, especially XGBoost and ANN, show great promise in predicting HT following thrombolysis in AIS patients, enhancing risk stratification and clinical decision-making. Future research should focus on prospective study designs, standardized reporting, and integrating ML assessments into clinical workflows to improve AIS management and patient outcomes.
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Affiliation(s)
- You-li Jiang
- Department of Neurology, People's Hospital of Longhua, Shenzhen, China
| | - Qing-shi Zhao
- Department of Neurology, People's Hospital of Longhua, Shenzhen, China
| | - Ao Li
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Zong-bi Wu
- Nursing Department, Shenzhen Traditional Chinese Medicine Hospital (The Fourth Clinical Medical School of Guangzhou University of Chinese Medicine), Shenzhen, China
| | - Lin-lin Liu
- Hengyang Medical School, School of Nursing, University of South China, Hengyang, China
| | - Fu Lin
- Department of Neurology, People's Hospital of Longhua, Shenzhen, China
| | - Yan-feng Li
- Department of Neurology, People's Hospital of Longhua, Shenzhen, China
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7
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Zhang Y, Jiang M, Gao Y, Zhao W, Wu C, Li C, Li M, Wu D, Wang W, Ji X. "No-reflow" phenomenon in acute ischemic stroke. J Cereb Blood Flow Metab 2024; 44:19-37. [PMID: 37855115 PMCID: PMC10905637 DOI: 10.1177/0271678x231208476] [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: 03/30/2023] [Revised: 08/04/2023] [Accepted: 09/13/2023] [Indexed: 10/20/2023]
Abstract
Acute ischemic stroke (AIS) afflicts millions of individuals worldwide. Despite the advancements in thrombolysis and thrombectomy facilitating proximal large artery recanalization, the resultant distal hypoperfusion, referred to "no-reflow" phenomenon, often impedes the neurological function restoration in patients. Over half a century of scientific inquiry has validated the existence of cerebral "no-reflow" in both animal models and human subjects. Furthermore, the correlation between "no-reflow" and adverse clinical outcomes underscores the necessity to address this phenomenon as a pivotal strategy for enhancing AIS prognoses. The underlying mechanisms of "no-reflow" are multifaceted, encompassing the formation of microemboli, microvascular compression and contraction. Moreover, a myriad of complex mechanisms warrant further investigation. Insights gleaned from mechanistic exploration have prompted advancements in "no-reflow" treatment, including microthrombosis therapy, which has demonstrated clinical efficacy in improving patient prognoses. The stagnation in current "no-reflow" diagnostic methods imposes limitations on the timely application of combined therapy on "no-reflow" post-recanalization. This narrative review will traverse the historical journey of the "no-reflow" phenomenon, delve into its underpinnings in AIS, and elucidate potential therapeutic and diagnostic strategies. Our aim is to equip readers with a swift comprehension of the "no-reflow" phenomenon and highlight critical points for future research endeavors.
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Affiliation(s)
- Yang Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Miaowen Jiang
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Yuan Gao
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Wenbo Zhao
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Chuanjie Wu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Chuanhui Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Ming Li
- China-America Institute of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Di Wu
- China-America Institute of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Wu Wang
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xunming Ji
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- China-America Institute of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
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8
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Simpkins AN, Indupuru HKR, Savitz SI. Editorial: Big Data analytics to advance stroke and cerebrovascular disease: a tool to bridge translational and clinical research. Front Neurol 2023; 14:1347654. [PMID: 38164203 PMCID: PMC10758229 DOI: 10.3389/fneur.2023.1347654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024] Open
Affiliation(s)
- Alexis Nétis Simpkins
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
| | | | - Sean Isaac Savitz
- Institute for Stroke and Cerebrovascular Disease, University of Texas Health Science Center, Houston, TX, United States
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Saceleanu VM, Toader C, Ples H, Covache-Busuioc RA, Costin HP, Bratu BG, Dumitrascu DI, Bordeianu A, Corlatescu AD, Ciurea AV. Integrative Approaches in Acute Ischemic Stroke: From Symptom Recognition to Future Innovations. Biomedicines 2023; 11:2617. [PMID: 37892991 PMCID: PMC10604797 DOI: 10.3390/biomedicines11102617] [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: 08/23/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/29/2023] Open
Abstract
Among the high prevalence of cerebrovascular diseases nowadays, acute ischemic stroke stands out, representing a significant worldwide health issue with important socio-economic implications. Prompt diagnosis and intervention are important milestones for the management of this multifaceted pathology, making understanding the various stroke-onset symptoms crucial. A key role in acute ischemic stroke management is emphasizing the essential role of a multi-disciplinary team, therefore, increasing the efficiency of recognition and treatment. Neuroimaging and neuroradiology have evolved dramatically over the years, with multiple approaches that provide a higher understanding of the morphological aspects as well as timely recognition of cerebral artery occlusions for effective therapy planning. Regarding the treatment matter, the pharmacological approach, particularly fibrinolytic therapy, has its merits and challenges. Endovascular thrombectomy, a game-changer in stroke management, has witnessed significant advances, with technologies like stent retrievers and aspiration catheters playing pivotal roles. For select patients, combining pharmacological and endovascular strategies offers evidence-backed benefits. The aim of our comprehensive study on acute ischemic stroke is to efficiently compare the current therapies, recognize novel possibilities from the literature, and describe the state of the art in the interdisciplinary approach to acute ischemic stroke. As we aspire for holistic patient management, the emphasis is not just on medical intervention but also on physical therapy, mental health, and community engagement. The future holds promising innovations, with artificial intelligence poised to reshape stroke diagnostics and treatments. Bridging the gap between groundbreaking research and clinical practice remains a challenge, urging continuous collaboration and research.
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Affiliation(s)
- Vicentiu Mircea Saceleanu
- Neurosurgery Department, Sibiu County Emergency Hospital, 550245 Sibiu, Romania;
- Neurosurgery Department, “Lucian Blaga” University of Medicine, 550024 Sibiu, Romania
| | - Corneliu Toader
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.-A.C.-B.); (H.P.C.); (B.-G.B.); (D.-I.D.); (A.B.); (A.D.C.); (A.V.C.)
- Department of Vascular Neurosurgery, National Institute of Neurology and Neurovascular Diseases, 020022 Bucharest, Romania
| | - Horia Ples
- Centre for Cognitive Research in Neuropsychiatric Pathology (NeuroPsy-Cog), “Victor Babes” University of Medicine and Pharmacy, 300736 Timisoara, Romania
- Department of Neurosurgery, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Razvan-Adrian Covache-Busuioc
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.-A.C.-B.); (H.P.C.); (B.-G.B.); (D.-I.D.); (A.B.); (A.D.C.); (A.V.C.)
| | - Horia Petre Costin
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.-A.C.-B.); (H.P.C.); (B.-G.B.); (D.-I.D.); (A.B.); (A.D.C.); (A.V.C.)
| | - Bogdan-Gabriel Bratu
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.-A.C.-B.); (H.P.C.); (B.-G.B.); (D.-I.D.); (A.B.); (A.D.C.); (A.V.C.)
| | - David-Ioan Dumitrascu
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.-A.C.-B.); (H.P.C.); (B.-G.B.); (D.-I.D.); (A.B.); (A.D.C.); (A.V.C.)
| | - Andrei Bordeianu
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.-A.C.-B.); (H.P.C.); (B.-G.B.); (D.-I.D.); (A.B.); (A.D.C.); (A.V.C.)
| | - Antonio Daniel Corlatescu
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.-A.C.-B.); (H.P.C.); (B.-G.B.); (D.-I.D.); (A.B.); (A.D.C.); (A.V.C.)
| | - Alexandru Vlad Ciurea
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.-A.C.-B.); (H.P.C.); (B.-G.B.); (D.-I.D.); (A.B.); (A.D.C.); (A.V.C.)
- Neurosurgery Department, Sanador Clinical Hospital, 010991 Bucharest, Romania
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10
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Wagner DT, Tilmans L, Peng K, Niedermeier M, Rohl M, Ryan S, Yadav D, Takacs N, Garcia-Fraley K, Koso M, Dikici E, Prevedello LM, Nguyen XV. Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges. Diagnostics (Basel) 2023; 13:2670. [PMID: 37627929 PMCID: PMC10453240 DOI: 10.3390/diagnostics13162670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.
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Affiliation(s)
- Daniel T. Wagner
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luke Tilmans
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Kevin Peng
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Matt Rohl
- College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Sean Ryan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Divya Yadav
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Noah Takacs
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Krystle Garcia-Fraley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Mensur Koso
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Engin Dikici
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Xuan V. Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
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11
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Li Q, Chi L, Zhao W, Wu L, Jiao C, Zheng X, Zhang K, Li X. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis. Front Neurol 2023; 14:1039794. [PMID: 37388543 PMCID: PMC10299899 DOI: 10.3389/fneur.2023.1039794] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 05/25/2023] [Indexed: 07/01/2023] Open
Abstract
Background Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke. Methods A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters. Results A total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively. Conclusion ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.
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Affiliation(s)
- Qinglin Li
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Chi
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Weiying Zhao
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Wu
- Department of Acupuncture, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Chuanxu Jiao
- Department of Neurorehabilitation, Taizhou Enze Medical Center Luqiao Hospital, Taizhou, Zhejiang, China
| | - Xue Zheng
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Kaiyue Zhang
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Xiaoning Li
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
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12
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Kim ES, Shin DJ, Cho ST, Chung KJ. Artificial Intelligence-Based Speech Analysis System for Medical Support. Int Neurourol J 2023; 27:99-105. [PMID: 37401020 DOI: 10.5213/inj.2346136.068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 06/26/2023] [Indexed: 07/05/2023] Open
Abstract
PURPOSE Prior research has indicated that stroke can influence the symptoms and presentation of neurogenic bladder, with various patterns emerging, including abnormal facial and linguistic characteristics. Language patterns, in particular, can be easily recognized. In this paper, we propose a platform that accurately analyzes the voices of stroke patients with neurogenic bladder, enabling early detection and prevention of the condition. METHODS In this study, we developed an artificial intelligence-based speech analysis diagnostic system to assess the risk of stroke associated with neurogenic bladder disease in elderly individuals. The proposed method involves recording the voice of a stroke patient while they speak a specific sentence, analyzing it to extract unique feature data, and then offering a voice alarm service through a mobile application. The system processes and classifies abnormalities, and issues alarm events based on analyzed voice data. RESULTS In order to assess the performance of the software, we first obtained the validation accuracy and training accuracy from the training data. Subsequently, we applied the analysis model by inputting both abnormal and normal data and tested the outcomes. The analysis model was evaluated by processing 30 abnormal data points and 30 normal data points in real time. The results demonstrated a high test accuracy of 98.7% for normal data and 99.6% for abnormal data. CONCLUSION Patients with neurogenic bladder due to stroke experience long-term consequences, such as physical and cognitive impairments, even when they receive prompt medical attention and treatment. As chronic diseases become increasingly prevalent in our aging society, it is essential to investigate digital treatments for conditions like stroke that lead to significant sequelae. This artificial intelligence-based healthcare convergence medical device aims to provide patients with timely and safe medical care through mobile services, ultimately reducing national social costs.
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Affiliation(s)
- Eui-Sun Kim
- Department of Media, Soongsil University, Seoul, Korea
| | - Dong Jin Shin
- Department of Neurology, Gachon University Gil Medical Center, Incheon, Korea
| | - Sung Tae Cho
- Department of Urology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Kyung Jin Chung
- Department of Urology, Gachon University Gil Medical Center, Incheon, Korea
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13
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Chandrabhatla AS, Kuo EA, Sokolowski JD, Kellogg RT, Park M, Mastorakos P. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies. J Clin Med 2023; 12:jcm12113755. [PMID: 37297949 DOI: 10.3390/jcm12113755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain imaging for more immediate diagnosis or promote post-stroke neurological/functional recovery. Technologies that assist with diagnosis predominantly use convolutional neural networks to identify abnormal brain images (e.g., CT perfusion). These technologies perform comparably to neuroradiologists, improve clinical workflows (e.g., time from scan acquisition to reading), and improve patient outcomes (e.g., days spent in the neurological ICU). Two devices are indicated for post-stroke rehabilitation by leveraging neuromodulation techniques. Multiple FDA-approved technologies exist that can help clinicians better diagnose and manage stroke. This review summarizes the most up-to-date literature regarding the functionality, performance, and utility of these technologies so clinicians can make informed decisions when using them in practice.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Elyse A Kuo
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Jennifer D Sokolowski
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Ryan T Kellogg
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Min Park
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Panagiotis Mastorakos
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, Thomas Jefferson University Hospital, 111 S 11th Street, Philadelphia, PA 19107, USA
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14
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Gottesman RF, Latour L. What's the Future of Vascular Neurology? Neurotherapeutics 2023; 20:605-612. [PMID: 37129762 PMCID: PMC10275820 DOI: 10.1007/s13311-023-01374-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2023] [Indexed: 05/03/2023] Open
Abstract
The field of vascular neurology has made tremendous advances over the last several decades, with major shifts in diagnosis, treatment, prevention, and rehabilitation of patients with stroke. Furthermore, the individuals who are providing the care represent a different cohort than those who were caring for stroke patients 30 years ago, with the increasing need for rapid decision-making for acute interventions and a larger workforce being needed to provide the many complicated aspects of care of stroke patients. Understanding the history of the field is critical before one can speculate about its future directions. In summarizing some of the past massive shifts in the past few decades, this review will discuss future opportunities and future challenges and will introduce the rest of this special issue focusing on vascular neurology in a post-thrombectomy era. Although thrombolysis and thrombectomy remain a major part of ischemic stroke management and care, in the coming years, there will likely be further modifications in how we provide the care, who provides it, how we train those individuals who provide it, where it is provided, and what data inform early management decisions.
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Affiliation(s)
- Rebecca F Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA.
| | - Lawrence Latour
- Stroke Branch, National Institute of Neurological Disorders and Stroke, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
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15
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The Establishment of Hypertrophic Cardiomyopathy Diagnosis Model via Artificial Neural Network and Random Decision Forest Method. Mediators Inflamm 2022; 2022:2024974. [PMID: 36157891 PMCID: PMC9500244 DOI: 10.1155/2022/2024974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/01/2022] [Indexed: 12/03/2022] Open
Abstract
Hypertrophic cardiomyopathy is a hereditary disease characterized by asymmetric ventricular hypertrophy as the key anatomical feature. Currently, there exists no effective method for the early diagnosis of hypertrophic cardiomyopathy. In this analysis, we incorporated multiple GEO datasets containing RNA profiles of hypertrophic cardiomyopathic patient tissues, identified 642 differentially expressed genes, and performed GO and KEGG analyses. Furthermore, we narrowed down 46 characteristic genes from these differentially expressed genes using random decision forests and conducted transcription factor regulation analysis on them. Using 40 genes that showed overlap between the training set and the verification set, the artificial neural network was trained, and the final MPS scoring model was constructed, and a receiver-operating characteristic (ROC) curve was drawn. We used the MPS model to predict the verification dataset and drew the ROC curve, which demonstrated the good prediction performance of the model. In conclusion, this study combines a random decision forest and artificial neural network to build a diagnostic model for hypertrophic cardiomyopathy to predict the disease, aiming at early detection and treatment, prolonging the survival time, and improving the quality of life of patients.
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Wang X, Fan Y, Zhang N, Li J, Duan Y, Yang B. Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis. Front Neurol 2022; 13:910259. [PMID: 35873778 PMCID: PMC9305175 DOI: 10.3389/fneur.2022.910259] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/20/2022] [Indexed: 12/03/2022] Open
Abstract
Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke (AIS). This study aimed to provide a systematic review and meta-analysis of the overall performance of current ML algorithms for final infarct prediction from baseline imaging. We made a comprehensive literature search on eligible studies developing ML models for core infarcted tissue estimation on admission CT or MRI in AIS patients. Eleven studies meeting the inclusion criteria were included in the quantitative analysis. Study characteristics, model methodology, and predictive performance of the included studies were extracted. A meta-analysis was conducted on the dice similarity coefficient (DSC) score by using a random-effects model to assess the overall predictive performance. Study heterogeneity was assessed by Cochrane Q and Higgins I2 tests. The pooled DSC score of the included ML models was 0.50 (95% CI 0.39–0.61), with high heterogeneity observed across studies (I2 96.5%, p < 0.001). Sensitivity analyses using the one-study removed method showed the adjusted overall DSC score ranged from 0.47 to 0.52. Subgroup analyses indicated that the DL-based models outperformed the conventional ML classifiers with the best performance observed in DL algorithms combined with CT data. Despite the presence of heterogeneity, current ML-based approaches for final infarct prediction showed moderate but promising performance. Before well integrated into clinical stroke workflow, future investigations are suggested to train ML models on large-scale, multi-vendor data, validate on external cohorts and adopt formalized reporting standards for improving model accuracy and robustness.
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Affiliation(s)
- Xinrui Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Yiming Fan
- Department of Orthopedics, Chinese PLA General Hospital, Beijing, China
| | - Nan Zhang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yang Duan
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Benqiang Yang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- *Correspondence: Benqiang Yang
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