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Deshpande A, Zhang LQ, Balu R, Yahyavi-Firouz-Abadi N, Badjatia N, Laksari K, Tahsili-Fahadan P. Cerebrovascular morphology: Insights into normal variations, aging effects, and disease implications. J Cereb Blood Flow Metab 2025:271678X251328537. [PMID: 40314210 PMCID: PMC12048404 DOI: 10.1177/0271678x251328537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Revised: 02/20/2025] [Accepted: 03/04/2025] [Indexed: 05/03/2025]
Abstract
Cerebrovascular morphology plays a critical role in brain health, influencing cerebral blood flow (CBF) and contributing to the pathogenesis of various neurological diseases. This review examines the anatomical structure of the cerebrovascular network and its variations in healthy and diseased populations and highlights age-related changes and their implications in various neurological conditions. Normal variations, including the completeness and anatomical anomalies of the Circle of Willis and collateral circulation, are discussed in relation to their impact on CBF and susceptibility to ischemic events. Age-related changes in the cerebrovascular system, such as alterations in vessel geometry and density, are explored for their contributions to age-related neurological disorders, including Alzheimer's disease and vascular dementia. Advances in medical imaging and computational methods have enabled automatic quantitative assessment of cerebrovascular structures, facilitating the identification of pathological changes in both acute and chronic cerebrovascular disorders. Emerging technologies, including machine learning and computational fluid dynamics, offer new tools for predicting disease risk and patient outcomes based on vascular morphology. This review underscores the importance of understanding cerebrovascular remodeling for early diagnosis and the development of novel therapeutic approaches in brain diseases.
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Affiliation(s)
- Aditi Deshpande
- Department of Mechanical Engineering, University of California, Riverside, USA
| | - Lucy Q Zhang
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Ramani Balu
- Vascular Neurology and Neurocritical Care, Inova Neuroscience and Spine Institute, Inova Fairfax Medical Campus, Falls Church, VA, USA
- Department of Medical Education, University of Virginia, Inova Campus, Falls Church, VA, USA
| | - Noushin Yahyavi-Firouz-Abadi
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Neeraj Badjatia
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kaveh Laksari
- Department of Mechanical Engineering, University of California, Riverside, USA
| | - Pouya Tahsili-Fahadan
- Vascular Neurology and Neurocritical Care, Inova Neuroscience and Spine Institute, Inova Fairfax Medical Campus, Falls Church, VA, USA
- Department of Medical Education, University of Virginia, Inova Campus, Falls Church, VA, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Fu X, Zhang C, Huang H, Li C, Li M, Li X, Gao Z, Peng M, Xu H, Zhu W. Machine learning models based on location-radiomics enable the accurate prediction of early neurological function deterioration for acute stroke in elderly patients. Front Aging Neurosci 2025; 17:1582687. [PMID: 40336946 PMCID: PMC12055763 DOI: 10.3389/fnagi.2025.1582687] [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: 02/24/2025] [Accepted: 04/02/2025] [Indexed: 05/09/2025] Open
Abstract
Background The timely and accurate identification of elderly stroke patients at risk of early neurological deterioration (END) is crucial for guiding clinical management. The present study aimed to create a comprehensive map of lesion location in elderly stroke, and build a machine learning model integrating location features and radiomics to predict END in elderly stroke patients. Methods A cohort of 709 elderly stroke patients from two centers patients were enrolled. Three machine learning models [logistic regression (LR), random forest (RF), and support vector machine (SVM)] based on location features, radiomics, and Loc-Rad were constructed to predict END in elderly stroke patients, respectively. The performance of models was evaluated using the receiver operating characteristic curves (ROC) and decision curve analysis (DCA). The SHapley Additive exPlanations (SHAP) was used to interpret and visualize the impact of the model predictors on the risk of END. Results The location maps for elderly stroke patients showed the bilateral cerebellum, left basal ganglia, left corona radiata, and right occipital lobe were significantly associated with END (p < 0.05). For three ML algorithms, the Loc-Rad models based on location features and radiomics demonstrated better performance than the separate location and radiomics-based models in the training cohort (p < 0.05), and the Loc-Rad model constructed with the RF algorithm performed best, with an AUC of 0.883 and accuracy of 0.888, and showed strong prediction performance in the external validation set (AUC of 0.818; accuracy of 0.811). The SHAP plots demonstrated that the most significant contributors to model performance were related to postcentral gyrus left, superior frontal gyrus right, w-HLH_glcm_Correlation, large vessel occlusion and lateral ventricle_body left. Conclusion We constructed comprehensive maps of strategic lesion network localizations for predicting END in elderly stroke patients and developed a predictive ML model that incorporates both location and radiomics features. This model could facilitate the rapid and robust prediction of the risk of END, enabling timely interventions and personalized treatment strategies to improve patient outcomes.
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Affiliation(s)
- Xiaoming Fu
- Department of Radiology, The Affiliated Gaochun Hospital of Jiangsu University, Nanjing, China
| | - Chuanyang Zhang
- Department of Radiology, The Affiliated Gaochun Hospital of Jiangsu University, Nanjing, China
| | - Hongjie Huang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Changcheng Li
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Miaomiao Li
- Department of Radiology, The Affiliated Gaochun Hospital of Jiangsu University, Nanjing, China
| | - XiaoRan Li
- Department of Radiology, The Affiliated Gaochun Hospital of Jiangsu University, Nanjing, China
| | - Zhijun Gao
- Department of Radiology, The Affiliated Gaochun Hospital of Jiangsu University, Nanjing, China
| | - Mingyang Peng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Hui Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wenli Zhu
- Department of Radiology, The Affiliated Gaochun Hospital of Jiangsu University, Nanjing, China
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Colasurdo M, Amran D, Chen H, Ziv K, Geron M, Love CJ, Robledo A, O'Leary S, Husain A, Von Waaden N, Garcia R, Edhayan G, Shaltoni H, Memon MZ, Kan P. Estimation of Ventricular and Intracranial Hemorrhage Volumes and Midline Shift on an External Validation Data Set Using a Convolutional Neural Network Algorithm. Neurosurgery 2025:00006123-990000000-01571. [PMID: 40227036 DOI: 10.1227/neu.0000000000003455] [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: 05/13/2024] [Accepted: 01/01/2025] [Indexed: 04/15/2025] Open
Abstract
BACKGROUND AND OBJECTIVES Noncontrast head computed tomography is the mainstay imaging modality to guide the management of intracranial hemorrhage (ICH); however, manual measurements can be time-consuming. In our study, we evaluate the performance of an artificial intelligence (AI) machine learning algorithm, Viz ICH-Plus, to automatically quantify ICH and bilateral lateral ventricular (BLV) volumes as well as midline shift (MLS). METHODS ICH patients considered for external ventricular drain with an initial noncontrast head computed tomography, and at least 1 follow-up scan within 48 hours was identified from a single center. Viz ICH-Plus estimations of ICH volume, BLV volume, and MLS were generated for each scan and compared with manually contoured and measured values. Median absolute errors and the ability of Viz ICH-Plus to detect clinically meaningful change from initial to follow-up scans (ICH volume growth ≥10 mL, BLV volume change ≥10 mL, or MLS increase ≥4 mm) were assessed. RESULTS Thirty patients were included for a total of 78 scans. The median absolute error was 2.9 mL (IQR 1.2 to 5.8) for ICH, 5.3 mL (IQR 2.5 to 7.9) for BLV volume, and 1.1 mm (IQR 0.7 to 2.0) for MLS. The ability of Viz ICH-Plus to detect a clinically significant change between scans was robust with sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of 91.7%, 92.6%, 84.6%, 96.2%, and 92.3%, respectively. CONCLUSION The described Viz ICH-Plus algorithm performed moderately well at quantifying ICH, BLV volume, and MLS with satisfying spatial overlap of artificial intelligence and manual segmentations. The system demonstrated good predictive power when using predetermined thresholds to estimate clinically significant changes.
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Affiliation(s)
- Marco Colasurdo
- Department of Interventional Radiology, Oregon Health and Science University, Portland , Oregon , USA
| | - Dor Amran
- Viz.ai Inc., San Francisco , California , USA
| | - Huanwen Chen
- Department of Neurology, MedStar Georgetown University Hospital, Washington , District of Columbia , USA
| | - Keren Ziv
- Viz.ai Inc., San Francisco , California , USA
| | | | | | - Ariadna Robledo
- Department of Neurosurgery, The University of Texas Medical Branch, Galveston , Texas , USA
| | - Sean O'Leary
- Department of Neurosurgery, The University of Texas Medical Branch, Galveston , Texas , USA
| | - Adam Husain
- Department of Neurosurgery, The University of Texas Medical Branch, Galveston , Texas , USA
| | - Nicholas Von Waaden
- Department of Neurosurgery, The University of Texas Medical Branch, Galveston , Texas , USA
| | - Roberto Garcia
- Department of Neurosurgery, The University of Texas Medical Branch, Galveston , Texas , USA
| | - Gautam Edhayan
- Department of Radiology, Division of Neuroradiology, The University of Texas Medical Branch, Galveston , Texas , USA
| | - Hashem Shaltoni
- Department of Neurology, University of Texas Medical Branch, Galveston , Texas , USA
| | | | - Peter Kan
- Department of Neurosurgery, The University of Texas Medical Branch, Galveston , Texas , USA
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Wang X, Liu X. Exploration of the shared gene signatures and molecular mechanisms between cardioembolic stroke and ischemic stroke. Front Neurol 2025; 16:1567902. [PMID: 40264650 PMCID: PMC12011848 DOI: 10.3389/fneur.2025.1567902] [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: 01/28/2025] [Accepted: 03/24/2025] [Indexed: 04/24/2025] Open
Abstract
Introduction This study aimed to investigate the shared molecular mechanisms underlying cardioembolic stroke (CS) and ischemic stroke (IS) using integrated bioinformatics analysis. Methods Microarray datasets for the CS (GSE58294, blood samples from CS and controls) and IS (GSE16561, blood from IS and controls; GSE22255, peripheral blood mononuclear cells from IS and matched controls) were acquired from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis were utilized to identify shared genes between the two diseases. Protein-protein interaction (PPI) network and topology analyses were conducted to identify the core shared genes. Three machine learning algorithms were employed to detect biomarkers from the core shared genes, and the diagnostic value of the hub genes was evaluated by establishing a predictive nomogram. Immune infiltration was evaluated using single-sample gene set enrichment analysis (ssGSEA), and pathways were analyzed with gene set enrichment analysis. Results There were 125 shared up-regulated genes and 2 shared down-regulated between CS and IS, which were mainly involved in immune inflammatory response-related biological functions. The Maximum Clique Centrality algorithm identified 25 core shared genes in the PPI network constructed using the shared genes. ABCA1, CLEC4E, and IRS2 were identified as biomarkers for both CS and IS and performed well in predicting the onset risk of CS and IS. All three biomarkers were highly expressed in both CS and IS compared to their corresponding controls. These biomarkers significantly correlated with neutrophil infiltration and autophagy activation in both CS and IS. Particularly, all three biomarkers were associated with the activation of neutrophil extracellular trap formation, but only in the IS. Conclusion ABCA1, CLEC4E, and IRS2 were identified as potential key biomarkers and therapeutic targets for CS and IS. Autophagy and neutrophil infiltration may represent the common mechanisms linking these two diseases.
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Affiliation(s)
- Xuan Wang
- Department of Neurology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
- School of Medicine, Tongji University, Shanghai, China
| | - Xueyuan Liu
- School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Chen H, Skorseth P, Rewinkel S, Kim D, Amin S, Shakal S, Priest R, Nesbit G, Clark W, Colasurdo M. Development of a machine learning model to predict changes in neuroimaging profiles among acute ischemic stroke patients following delayed transfer for endovascular thrombectomy. Neuroradiology 2025:10.1007/s00234-025-03600-6. [PMID: 40172643 DOI: 10.1007/s00234-025-03600-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2025] [Accepted: 03/22/2025] [Indexed: 04/04/2025]
Abstract
INTRODUCTION Endovascular thrombectomy (EVT) patient selection depends on neuroimaging. However, interhospital transfer delays can lead to neuroimaging changes, whether and when repeat imaging is necessary are unclear. Herein, we develop a machine learning model (MLM) to predict vessel recanalization, ischemia progression, and imaging stability for EVT candidates who experience delayed interhospital transfer. METHODS This retrospective study included EVT candidates with internal carotid or middle cerebral artery occlusion stroke transferred 1.5-6.0 h after initial imaging. Clinical and radiographic data were collected. A gradient-boosted tree-based MLM (XGBoost) was trained and optimized on 66% of the cohort (randomly selected) using 10-fold cross-validation, and the MLM was independently validated on the remaining, untouched 33% of the study cohort. Model performance was assessed using areas under the receiver operating characteristics curve (AUC) for discrimination, F1 scores for precision/recall, and Brier scores for calibration. RESULTS Among 317 patients, 69.4% had stable imaging, 14.5% showed ischemia progression (ASPECTS drop ≥ 2), and 16.1% had vessel recanalization. The MLM was developed and optimized in the training cohort (n = 212). NIH stroke scale improvement, onset-to-imaging time, intravenous thrombolysis, initial ASPECTS, and collateral score were important features. In the validation cohort (n = 105), the MLM achieved AUCs of 0.81 (95%CI 0.72-0.90) for imaging stability, 0.82 (95%CI 0.72-0.91) for ischemia progression, and 0.89 (95%CI 0.77-1.00) for vessel recanalization. F1 scores were 0.87 and 0.95 for stability and no recanalization, with Brier scores of 0.17 and 0.08, respectively. CONCLUSION Our MLM accurately predicts imaging changes among EVT candidates who experienced transfer delays.
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Affiliation(s)
- Huanwen Chen
- MedStar Georgetown University Hospital, Washington D.C., USA
| | | | | | - Daniel Kim
- Oregon Health & Science University, Portland, USA
| | - Sonesh Amin
- Oregon Health & Science University, Portland, USA
| | - Scott Shakal
- Oregon Health & Science University, Portland, USA
| | - Ryan Priest
- Oregon Health & Science University, Portland, USA
| | - Gary Nesbit
- Oregon Health & Science University, Portland, USA
| | - Wayne Clark
- Oregon Health & Science University, Portland, USA
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Takács TT, Magyar-Stang R, Szatmári S, Sipos I, Saftics K, Berki ÁJ, Évin S, Bereczki D, Varga C, Nyilas N, Bíró I, Barsi P, Magyar M, Maurovich-Horvat P, Böjti PP, Pásztor M, Szikora I, Nardai S, Gunda B. Workload and clinical impact of MRI-based extension of reperfusion therapy time window in acute ischaemic stroke-a prospective single-centre study. GeroScience 2025:10.1007/s11357-025-01549-1. [PMID: 39913034 DOI: 10.1007/s11357-025-01549-1] [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: 10/15/2024] [Accepted: 01/27/2025] [Indexed: 02/07/2025] Open
Abstract
Current European Stroke Organisation (ESO) guidelines recommend extended time window reperfusion therapies (4.5-9 h for thrombolysis, 6-24 h for thrombectomy) based on advanced imaging. However, the workload and clinical benefit of this strategy on a population basis are not known. To determine the caseload, treatment rates, and outcomes in the extended as compared to the standard time windows. All consecutive ischaemic stroke patients within 24 h of last known well between 1st March 2021 and 28th February 2022 were included in a prospective single-centre study. Treatment eligibility in the extended time windows or wake-up strokes recognized within 4 h was based on current ESO guideline criteria using MRI DWI-PWI or DWI-FLAIR mismatch. MRI was only available during working hours (8-20 h); otherwise, CT/CTA was used. Clinical outcome in treated patients was assessed at three months. Among the 777 admitted patients, 252 (32.4%) had MRI. The thrombolysis rate was 119/304 (39.1%) in standard and 14/231 (6.1%) in the extended time window. The thrombectomy rate was 34/386 (8.8%) in standard and 15/391 (3.8%) in the extended time window. Independent clinical outcomes (mRS ≤ 2) were not statistically different in early and late-treated patients both for thrombolysis (48% vs. 28.6%, p = 0.25) and thrombectomy (38.4% vs. 33.3%, p = 0.99). Even with a limited availability of advanced imaging extending therapeutic time windows resulted in an 11.7% increase in thrombolysis and a 44% increase in thrombectomy with comparable clinical outcomes in early and late-treated patients at the price of a twofold burden in clinical and advanced imaging screening.
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Affiliation(s)
- Tímea Tünde Takács
- Department of Neurology, Semmelweis University, Budapest, Hungary.
- Schools of PhD Studies, Doctoral School of Neurosciences "János Szentágothai", Semmelweis University, Budapest, Hungary.
| | - Rita Magyar-Stang
- Department of Neurology, Semmelweis University, Budapest, Hungary
- Schools of PhD Studies, Doctoral School of Neurosciences "János Szentágothai", Semmelweis University, Budapest, Hungary
| | | | - Ildikó Sipos
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Katalin Saftics
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Ádám József Berki
- Department of Neurology, Semmelweis University, Budapest, Hungary
- Schools of PhD Studies, Doctoral School of Neurosciences "János Szentágothai", Semmelweis University, Budapest, Hungary
| | - Sándor Évin
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Dániel Bereczki
- Department of Neurology, Semmelweis University, Budapest, Hungary
- HUN-REN-SU Neuroepidemiological Research Group, Budapest, Hungary
| | - Csaba Varga
- Department of Emergency Medicine, Semmelweis University, Budapest, Hungary
| | - Nóra Nyilas
- Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - István Bíró
- Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Péter Barsi
- Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Máté Magyar
- Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Pál Maurovich-Horvat
- Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Péter Pál Böjti
- Department of Neurosurgery and Neurointervention, Semmelweis University, Budapest, Hungary
| | - Máté Pásztor
- Department of Neurosurgery and Neurointervention, Semmelweis University, Budapest, Hungary
| | - István Szikora
- Department of Neurosurgery and Neurointervention, Semmelweis University, Budapest, Hungary
| | - Sándor Nardai
- Department of Neurosurgery and Neurointervention, Semmelweis University, Budapest, Hungary
| | - Bence Gunda
- Department of Neurology, Semmelweis University, Budapest, Hungary
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Xie S, Peng S, Zhao L, Yang B, Qu Y, Tang X. A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches. Mol Genet Genomics 2025; 300:18. [PMID: 39853452 PMCID: PMC11762205 DOI: 10.1007/s00438-024-02217-3] [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: 10/08/2024] [Accepted: 12/15/2024] [Indexed: 01/26/2025]
Abstract
Stroke is a leading cause of death and disability globally, particularly in China. Identifying risk factors for stroke at an early stage is critical to improving patient outcomes and reducing the overall disease burden. However, the complexity of stroke risk factors requires advanced approaches for accurate prediction. The objective of this study is to identify key risk factors for stroke and develop a predictive model using machine learning techniques to enhance early detection and improve clinical decision-making. Data from the China Health and Retirement Longitudinal Study (2011-2020) were analyzed, classifying participants based on baseline characteristics. We evaluated correlations among 12 chronic diseases and applied machine learning algorithms to identify stroke-associated parameters. A dose-response relationship between these parameters and stroke was assessed using restricted cubic splines with Cox proportional hazards models. A refined predictive model, incorporating age, sex, and key risk factors, was developed. Stroke patients were significantly older (average age 69.03 years) and had a higher proportion of women (53%) compared to non-stroke individuals. Additionally, stroke patients were more likely to reside in rural areas, be unmarried, smoke, and suffer from various diseases. While the 12 chronic diseases were correlated (p < 0.05), the correlation coefficients were generally weak (r < 0.5). Machine learning identified nine parameters significantly associated with stroke risk: TyG-WC, WHtR, TyG-BMI, TyG, TMO, CysC, CREA, SBP, and HDL-C. Of these, TyG-WC, WHtR, TyG-BMI, TyG, CysC, CREA, and SBP exhibited a positive dose-response relationship with stroke risk. In contrast, TMO and HDL-C were associated with reduced stroke risk. In the fully adjusted model, elevated CysC (HR = 2.606, 95% CI 1.869-3.635), CREA (HR = 1.819, 95% CI 1.240-2.668), and SBP (HR = 1.008, 95% CI 1.003-1.012) were significantly associated with increased stroke risk, while higher HDL-C (HR = 0.989, 95% CI 0.984-0.995) and TMO (HR = 0.99995, 95% CI 0.99994-0.99997) were protective. A nomogram model incorporating age, sex, and the identified parameters demonstrated superior predictive accuracy, with a significantly higher Harrell's C-index compared to individual predictors. This study identifies several significant stroke risk factors and presents a predictive model that can enhance early detection of high-risk individuals. Among them, CREA, CysC, SBP, TyG-BMI, TyG, TyG-WC, and WHtR were positively associated with stroke risk, whereas TMO and HDL-C were opposite. This serves as a valuable decision-support resource for clinicians, facilitating more effective prevention and treatment strategies, ultimately improving patient outcomes.
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Affiliation(s)
- Songquan Xie
- Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, NanChong, 637000, Sichuan, China
| | - Shuting Peng
- Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, NanChong, 637000, Sichuan, China
| | - Long Zhao
- Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, NanChong, 637000, Sichuan, China
| | - Binbin Yang
- Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, NanChong, 637000, Sichuan, China
| | - Yukun Qu
- Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, NanChong, 637000, Sichuan, China
| | - Xiaoping Tang
- Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, NanChong, 637000, Sichuan, China.
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Li K, Yang Y, Niu S, Yang Y, Tian B, Huan X, Guo D. A Comparative Study of AI-Based Automated and Manual Postprocessing of Head and Neck CT Angiography: An Independent External Validation with Multi-Vendor and Multi-Center Data. Neuroradiology 2024; 66:1765-1780. [PMID: 38753039 DOI: 10.1007/s00234-024-03379-y] [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: 11/16/2023] [Accepted: 05/09/2024] [Indexed: 09/26/2024]
Abstract
PURPOSE To externally validate the performance of automated postprocessing (AP) on head and neck CT Angiography (CTA) and compare it with manual postprocessing (MP). METHODS This retrospective study included head and neck CTA-exams of patients from three tertiary hospitals acquired on CT scanners from five manufacturers. AP was performed by CerebralDoc. The image quality was assessed using Likert scales, and the qualitative and quantitative diagnostic performance of arterial stenosis and aneurysm, postprocessing time, and scanning radiation dose were also evaluated. RESULTS A total of 250 patients were included. Among these, 55 patients exhibited significant stenosis (≥ 50%), and 33 patients had aneurysms, diagnosed using original CTA datasets and corresponding multiplanar reconstructions as the reference. While the scores of the V4 segment and the edge of the M1 segment on volume rendering (VR), as well as the C4 segment on maximum intensity projection (MIP), were significantly lower with AP compared to MP across vendors (all P < 0.05), most scores in AP demonstrated image quality that was either superior to or comparable with that of MP. Furthermore, the diagnostic performance of AP was either superior to or comparable with that of MP. Moreover, AP also exhibited advantages in terms of postprocessing time and radiation dose when compared to MP (P < 0.001). CONCLUSION The AP of CerebralDoc presents clear advantages over MP and holds significant clinical value. However, further optimization is required in the image quality of the V4 and M1 segments on VR as well as the C4 segment on MIP.
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Affiliation(s)
- Kunhua Li
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University & Chongqing Medical Imaging Artificial Intelligence Laboratory, Chongqing, China
| | - Yang Yang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Shengwen Niu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University & Chongqing Medical Imaging Artificial Intelligence Laboratory, Chongqing, China
| | - Yongwei Yang
- Department of Radiology, the Fifth People's Hospital of Chongqing, Chongqing, China
| | - Bitong Tian
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University & Chongqing Medical Imaging Artificial Intelligence Laboratory, Chongqing, China
| | - Xinyue Huan
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University & Chongqing Medical Imaging Artificial Intelligence Laboratory, Chongqing, China
| | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University & Chongqing Medical Imaging Artificial Intelligence Laboratory, Chongqing, China.
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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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10
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Aboonq MS, Alqahtani SA. Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability. NEUROSCIENCES (RIYADH, SAUDI ARABIA) 2024; 29:190-196. [PMID: 38981634 PMCID: PMC11305345 DOI: 10.17712/nsj.2024.3.20230100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 05/31/2024] [Indexed: 07/11/2024]
Abstract
OBJECTIVES To develop a machine learning model to accurately predict stroke risk based on demographic and clinical data. It also sought to identify the most significant stroke risk factors and determine the optimal machine learning algorithm for stroke prediction. METHODS This cross-sectional study analyzed data on 438,693 adults from the 2021 Behavioral Risk Factor Surveillance System. Features encompassed demographics and clinical factors. Descriptive analysis profiled the dataset. Logistic regression quantified risk relationships. Adjusted mutual information evaluated feature importance. Multiple machine learning models were built and evaluated on metrics like accuracy, AUC ROC, and F1 score. RESULTS Key factors significantly associated with higher stroke odds included older age, diabetes, hypertension, high cholesterol, and history of myocardial infarction or angina. Random forest model achieved the best performance with accuracy of 72.46%, AUC ROC of 0.72, and F1 score of 0.74. Cross-validation confirmed its reliability. Top features were hypertension, myocardial infarction history, angina, age, diabetes status, and cholesterol. CONCLUSION The random forest model robustly predicted stroke risk using demographic and clinical variables. Feature importance highlighted priorities like hypertension and diabetes for clinical monitoring and intervention. This could help enable data-driven stroke prevention strategies.
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Affiliation(s)
- Moutasem S. Aboonq
- From the Department of Physiology, College of Medicine, Taibah University, Al-Madinah Al-Munawwarah, Kingdom of Saudi Arabia
| | - Saeed A. Alqahtani
- From the Department of Physiology, College of Medicine, Taibah University, Al-Madinah Al-Munawwarah, Kingdom of Saudi Arabia
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11
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Xiong Z, Kwapong WR, Liu S, Chen T, Xu K, Mao H, Hao J, Cao L, Liu J, Zheng Y, Wang H, Yan Y, Ye C, Wu B, Qi H, Zhao Y. Association of Retinal Biomarkers With the Subtypes of Ischemic Stroke and an Automated Classification Model. Invest Ophthalmol Vis Sci 2024; 65:50. [PMID: 39083310 PMCID: PMC11290563 DOI: 10.1167/iovs.65.8.50] [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: 01/02/2024] [Accepted: 05/17/2024] [Indexed: 08/02/2024] Open
Abstract
Purpose Retinal microvascular changes are associated with ischemic stroke, and optical coherence tomography angiography (OCTA) is a potential tool to reveal the retinal microvasculature. We investigated the feasibility of using the OCTA image to automatically identify ischemic stroke and its subtypes (i.e. lacunar and non-lacunar stroke), and exploited the association of retinal biomarkers with the subtypes of ischemic stroke. Methods Two cohorts were included in this study and a total of 1730 eyes from 865 participants were studied. A deep learning model was developed to discriminate the subjects with ischemic stroke from healthy controls and to distinguish the subtypes of ischemic stroke. We also extracted geometric parameters of the retinal microvasculature at different retinal layers to investigate the correlations. Results Superficial vascular plexus (SVP) yielded the highest areas under the receiver operating characteristic curve (AUCs) of 0.922 and 0.871 for the ischemic stroke detection and stroke subtypes classification, respectively. For external data validation, our model achieved an AUC of 0.822 and 0.766 for the ischemic stroke detection and stroke subtypes classification, respectively. When parameterizing the OCTA images, we showed individuals with ischemic strokes had increased SVP tortuosity (B = 0.085, 95% confidence interval [CI] = 0.005-0.166, P = 0.038) and reduced FAZ circularity (B = -0.212, 95% CI = -0.42 to -0.005, P = 0.045); non-lacunar stroke had reduced SVP FAZ circularity (P = 0.027) compared to lacunar stroke. Conclusions Our study demonstrates the applicability of artificial intelligence (AI)-enhanced OCTA image analysis for ischemic stroke detection and its subtypes classification. Biomarkers from retinal OCTA images can provide useful information for clinical decision-making and diagnosis of ischemic stroke and its subtypes.
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Affiliation(s)
- Zhouwei Xiong
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, China
| | | | - Shouyue Liu
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, China
| | - Tao Chen
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, China
| | - Keyi Xu
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, China
| | - Haiting Mao
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, China
| | - Jinkui Hao
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Le Cao
- Department of Neurology, West China Hospital, Sichuan, China
| | - Jiang Liu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yalin Zheng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
| | - Hang Wang
- Department of Neurology, West China Hospital, Sichuan, China
| | - Yuying Yan
- Department of Neurology, West China Hospital, Sichuan, China
| | - Chen Ye
- Department of Neurology, West China Hospital, Sichuan, China
| | - Bo Wu
- Department of Neurology, West China Hospital, Sichuan, China
| | - Hong Qi
- Department of Ophthalmology, Peking University Third Hospital, Beijing, China
| | - Yitian Zhao
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, China
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12
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Teghipco A, Newman-Norlund R, Fridriksson J, Rorden C, Bonilha L. Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity. COMMUNICATIONS MEDICINE 2024; 4:115. [PMID: 38866977 PMCID: PMC11169346 DOI: 10.1038/s43856-024-00541-8] [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: 08/09/2023] [Accepted: 06/03/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the lesion. While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, substantial interindividual variability remains unaccounted. One explanatory factor may be the spatial distribution of morphometry beyond the lesion (e.g., atrophy), including not just specific brain areas, but distinct three-dimensional patterns. METHODS Here, we test whether deep learning with Convolutional Neural Networks (CNNs) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy better predicts chronic stroke individuals with severe aphasia (N = 231) than classical machine learning (Support Vector Machines; SVMs), evaluating whether encoding spatial dependencies identifies uniquely predictive patterns. RESULTS CNNs achieve higher balanced accuracy and F1 scores, even when SVMs are nonlinear or integrate linear or nonlinear dimensionality reduction. Parity only occurs when SVMs access features learned by CNNs. Saliency maps demonstrate that CNNs leverage distributed morphometry patterns, whereas SVMs focus on the area around the lesion. Ensemble clustering of CNN saliencies reveals distinct morphometry patterns unrelated to lesion size, consistent across individuals, and which implicate unique networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions depend on both ipsilateral and contralateral features outside the lesion. CONCLUSIONS Three-dimensional network distributions of morphometry are directly associated with aphasia severity, underscoring the potential for CNNs to improve outcome prognostication from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.
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Affiliation(s)
- Alex Teghipco
- Department of Communication Sciences and Disorders, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
| | - Roger Newman-Norlund
- Department of Psychology, College of Arts and Sciences, University of South Carolina, Columbia, SC, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Christopher Rorden
- Department of Psychology, College of Arts and Sciences, University of South Carolina, Columbia, SC, USA
| | - Leonardo Bonilha
- Department of Neurology, School of Medicine, University of South Carolina, Columbia, SC, USA
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13
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Vitt JR, Mainali S. Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients. Semin Neurol 2024; 44:342-356. [PMID: 38569520 DOI: 10.1055/s-0044-1785504] [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: 04/05/2024]
Abstract
The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.
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Affiliation(s)
- Jeffrey R Vitt
- Department of Neurological Surgery, UC Davis Medical Center, Sacramento, California
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, Virginia
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14
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Warman R, Warman PI, Warman A, Bueso T, Ota R, Windisch T, Neves G. A deep learning method to identify and localize large-vessel occlusions from cerebral digital subtraction angiography. J Neuroimaging 2024; 34:366-375. [PMID: 38506407 DOI: 10.1111/jon.13193] [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: 11/09/2023] [Revised: 01/25/2024] [Accepted: 01/27/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND AND PURPOSE An essential step during endovascular thrombectomy is identifying the occluded arterial vessel on a cerebral digital subtraction angiogram (DSA). We developed an algorithm that can detect and localize the position of occlusions in cerebral DSA. METHODS We retrospectively collected cerebral DSAs from a single institution between 2018 and 2020 from 188 patients, 86 of whom suffered occlusions of the M1 and proximal M2 segments. We trained an ensemble of deep-learning models on fewer than 60 large-vessel occlusion (LVO)-positive patients. We evaluated the model on an independent test set and evaluated the truth of its predicted localizations using Intersection over Union and expert review. RESULTS On an independent test set of 166 cerebral DSA frames with an LVO prevalence of 0.19, the model achieved a specificity of 0.95 (95% confidence interval [CI]: 0.90, 0.99), a precision of 0.7450 (95% CI: 0.64, 0.88), and a sensitivity of 0.76 (95% CI: 0.66, 0.91). The model correctly localized the LVO in at least one frame in 13 of the 14 LVO-positive patients in the test set. The model achieved a precision of 0.67 (95% CI: 0.52, 0.79), recall of 0.69 (95% CI: 0.46, 0.81), and a mean average precision of 0.75 (95% CI: 0.56, 0.91). CONCLUSION This work demonstrates that a deep learning strategy using a limited dataset can generate effective representations used to identify LVOs. Generating an expanded and more complete dataset of LVOs with obstructed LVOs is likely the best way to improve the model's ability to localize LVOs.
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Affiliation(s)
- Roshan Warman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Pranav I Warman
- Duke University School of Medicine, Durham, North Carolina, USA
| | - Anmol Warman
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tulio Bueso
- Department of Neurology, Texas Tech University Medical Sciences Center, Lubbock, Texas, USA
| | - Riichi Ota
- Department of Neurology, Texas Tech University Medical Sciences Center, Lubbock, Texas, USA
| | - Thomas Windisch
- Department of Neurology, Texas Tech University Medical Sciences Center, Lubbock, Texas, USA
- Covenant Health, Lubbock, Texas, USA
| | - Gabriel Neves
- Department of Neurology, Section of Neurocritical Care, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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Hastings N, Samuel D, Ansari AN, Kaurani P, J JW, Bhandary VS, Gautam P, Tayyil Purayil AL, Hassan T, Dinesh Eshwar M, Nuthalapati BST, Pothuri JK, Ali N. The Role of Artificial Intelligence-Powered Imaging in Cerebrovascular Accident Detection. Cureus 2024; 16:e59768. [PMID: 38846243 PMCID: PMC11153838 DOI: 10.7759/cureus.59768] [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] [Accepted: 05/04/2024] [Indexed: 06/09/2024] Open
Abstract
Cerebrovascular accidents (CVAs) often occur suddenly and abruptly, leaving patients with long-lasting disabilities that place a huge emotional and economic burden on everyone involved. CVAs result when emboli or thrombi travel to the brain and impede blood flow; the subsequent lack of oxygen supply leads to ischemia and eventually tissue infarction. The most important factor determining the prognosis of CVA patients is time, specifically the time from the onset of disease to treatment. Artificial intelligence (AI)-assisted neuroimaging alleviates the time constraints of analysis faced using traditional diagnostic imaging modalities, thus shortening the time from diagnosis to treatment. Numerous recent studies support the increased accuracy and processing capabilities of AI-assisted imaging modalities. However, the learning curve is steep, and huge barriers still exist preventing a full-scale implementation of this technology. Thus, the potential for AI to revolutionize medicine and healthcare delivery demands attention. This paper aims to elucidate the progress of AI-powered imaging in CVA diagnosis while considering traditional imaging techniques and suggesting methods to overcome adoption barriers in the hope that AI-assisted neuroimaging will be considered normal practice in the near future. There are multiple modalities for AI neuroimaging, all of which require collecting sufficient data to establish inclusive, accurate, and uniform detection platforms. Future efforts must focus on developing methods for data harmonization and standardization. Furthermore, transparency in the explainability of these technologies needs to be established to facilitate trust between physicians and AI-powered technology. This necessitates considerable resources, both financial and expertise wise which are not available everywhere.
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Affiliation(s)
- Natasha Hastings
- School of Medicine, St. George's University School of Medicine, St. George's, GRD
| | - Dany Samuel
- Radiology, Medical University of Varna, Varna, BGR
| | - Aariz N Ansari
- Internal Medicine, Era's Lucknow Medical College and Hospital, Lucknow, IND
| | - Purvi Kaurani
- Neurology, Dnyandeo Yashwantrao (DY) Patil University School of Medicine, Navi Mumbai, IND
| | - Jenkin Winston J
- Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, IND
| | - Vaibhav S Bhandary
- Radiology, Srinivas Institute of Medical Sciences and Research Center, Mangaluru, IND
| | - Prabin Gautam
- Emergency Medicine, Kettering General Hospital, Kettering, GBR
| | | | - Taimur Hassan
- Neurosurgery, Houston Methodist Neurological Institute, Houston, USA
| | | | | | | | - Noor Ali
- Medicine and Surgery, Dubai Medical College, Dubai, ARE
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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17
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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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Fu H, Novak A, Robert D, Kumar S, Tanamala S, Oke J, Bhatia K, Shah R, Romsauerova A, Das T, Espinosa A, Grzeda MT, Narbone M, Dharmadhikari R, Harrison M, Vimalesvaran K, Gooch J, Woznitza N, Salik N, Campbell A, Khan F, Lowe DJ, Shuaib H, Ather S. AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study. BMJ Open 2024; 14:e079824. [PMID: 38346874 PMCID: PMC10862304 DOI: 10.1136/bmjopen-2023-079824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/28/2024] [Indexed: 02/15/2024] Open
Abstract
INTRODUCTION A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. METHODS AND ANALYSIS A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers' performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. ETHICS AND DISSEMINATION The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal. TRIAL REGISTRATION NUMBER NCT06018545.
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Affiliation(s)
- Howell Fu
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Alex Novak
- Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | | | | | - Jason Oke
- Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Kanika Bhatia
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Ruchir Shah
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Tilak Das
- Department of Clinical Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Abdalá Espinosa
- Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | | | | | - Mark Harrison
- Emergency Department, Northumbria Specialist Emergency Care Hospital, Cramlington, UK
| | - Kavitha Vimalesvaran
- Clinical Scientific Computing, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Jane Gooch
- College of Health, Psychology & Social Care, University of Derby, Derby, UK
| | - Nicholas Woznitza
- Radiology Department, University College London Hospitals NHS Foundation Trust, London, UK
- School of Allied and Public Health Professions, Canterbury Christ Church University, Canterbury, UK
| | | | - Alan Campbell
- Radiology Department, University College London Hospitals NHS Foundation Trust, London, UK
| | - Farhaan Khan
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Haris Shuaib
- Clinical Scientific Computing, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Sarim Ather
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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19
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Wu Y, Egan C, Olvera-Barrios A, Scheppke L, Peto T, Charbel Issa P, Heeren TFC, Leung I, Rajesh AE, Tufail A, Lee CS, Chew EY, Friedlander M, Lee AY. Developing a Continuous Severity Scale for Macular Telangiectasia Type 2 Using Deep Learning and Implications for Disease Grading. Ophthalmology 2024; 131:219-226. [PMID: 37739233 PMCID: PMC10841914 DOI: 10.1016/j.ophtha.2023.09.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 09/24/2023] Open
Abstract
PURPOSE Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for macular telangiectasia (MacTel) type 2 by combining a DL classification model with uniform manifold approximation and projection (UMAP). DESIGN We used a DL network to learn a feature representation of MacTel severity from discrete severity labels and applied UMAP to embed this feature representation into 2 dimensions, thereby creating a continuous MacTel severity scale. PARTICIPANTS A total of 2003 OCT volumes were analyzed from 1089 MacTel Project participants. METHODS We trained a multiview DL classifier using multiple B-scans from OCT volumes to learn a previously published discrete 7-step MacTel severity scale. The classifiers' last feature layer was extracted as input for UMAP, which embedded these features into a continuous 2-dimensional manifold. The DL classifier was assessed in terms of test accuracy. Rank correlation for the continuous UMAP scale against the previously published scale was calculated. Additionally, the UMAP scale was assessed in the κ agreement against 5 clinical experts on 100 pairs of patient volumes. For each pair of patient volumes, clinical experts were asked to select the volume with more severe MacTel disease and to compare them against the UMAP scale. MAIN OUTCOME MEASURES Classification accuracy for the DL classifier and κ agreement versus clinical experts for UMAP. RESULTS The multiview DL classifier achieved top 1 accuracy of 63.3% (186/294) on held-out test OCT volumes. The UMAP metric showed a clear continuous gradation of MacTel severity with a Spearman rank correlation of 0.84 with the previously published scale. Furthermore, the continuous UMAP metric achieved κ agreements of 0.56 to 0.63 with 5 clinical experts, which was comparable with interobserver κ values. CONCLUSIONS Our UMAP embedding generated a continuous MacTel severity scale, without requiring continuous training labels. This technique can be applied to other diseases and may lead to more accurate diagnosis, improved understanding of disease progression, and key imaging features for pathologic characteristics. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, Washington; The Roger and Angie Karalis Johnson Retina Center, Seattle, Washington
| | - Catherine Egan
- Moorfields Eye Hospital, London, United Kingdom; University College London, Institute of Ophthalmology, London, United Kingdom
| | - Abraham Olvera-Barrios
- Moorfields Eye Hospital, London, United Kingdom; University College London, Institute of Ophthalmology, London, United Kingdom
| | - Lea Scheppke
- Lowy Medical Research Institute, La Jolla, California; The Scripps Research Institute, La Jolla, California
| | - Tunde Peto
- Center for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Peter Charbel Issa
- Oxford Eye Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Laboratory of Ophthalmology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | | | - Irene Leung
- Moorfields Eye Hospital, London, United Kingdom
| | - Anand E Rajesh
- Department of Ophthalmology, University of Washington, Seattle, Washington; The Roger and Angie Karalis Johnson Retina Center, Seattle, Washington
| | - Adnan Tufail
- Moorfields Eye Hospital, London, United Kingdom; University College London, Institute of Ophthalmology, London, United Kingdom
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington; The Roger and Angie Karalis Johnson Retina Center, Seattle, Washington
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Martin Friedlander
- Lowy Medical Research Institute, La Jolla, California; The Scripps Research Institute, La Jolla, California
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington; The Roger and Angie Karalis Johnson Retina Center, Seattle, Washington.
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20
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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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21
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Kong J, Zhang D. Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis. Front Neurol 2024; 14:1335851. [PMID: 38229595 PMCID: PMC10789857 DOI: 10.3389/fneur.2023.1335851] [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: 11/09/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024] Open
Abstract
Background Pre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients. Methods We systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients. Results Sixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84-0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74-0.86) for predicting reperfusion in AIS. Conclusion Radiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.
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Affiliation(s)
- Jinfen Kong
- Department of Radiology, Yuhuan Second People's Hospital, Yuhuan, Taizhou, Zhejiang, China
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22
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Liang J, Feng J, Lin Z, Wei J, Luo X, Wang QM, He B, Chen H, Ye Y. Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data. Front Neurol 2023; 14:1294723. [PMID: 38192576 PMCID: PMC10773779 DOI: 10.3389/fneur.2023.1294723] [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: 09/18/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024] Open
Abstract
Accurately assessing the prognostic outcomes of patients with acute ischemic stroke and adjusting treatment plans in a timely manner for those with poor prognosis is crucial for intervening in modifiable risk factors. However, there is still controversy regarding the correlation between imaging-based predictions of complications in acute ischemic stroke. To address this, we developed a cross-modal attention module for integrating multidimensional data, including clinical information, imaging features, treatment plans, prognosis, and complications, to achieve complementary advantages. The fused features preserve magnetic resonance imaging (MRI) characteristics while supplementing clinical relevant information, providing a more comprehensive and informative basis for clinical diagnosis and treatment. The proposed framework based on multidimensional data for activity of daily living (ADL) scoring in patients with acute ischemic stroke demonstrates higher accuracy compared to other state-of-the-art network models, and ablation experiments confirm the effectiveness of each module in the framework.
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Affiliation(s)
- Jiabin Liang
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
- Medical Imaging Institute of Panyu, Guangzhou, China
| | - Jie Feng
- Radiology Department of Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhijie Lin
- Laboratory for Intelligent Information Processing, Guangdong University of Technology, Guangzhou, China
| | - Jinbo Wei
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China
| | - Xun Luo
- Kerry Rehabilitation Medicine Research Institute, Shenzhen, China
| | - Qing Mei Wang
- Stroke Biological Recovery Laboratory, Spaulding Rehabilitation Hospital, Teaching Affiliate of Harvard Medical School, Charlestown, MA, United States
| | - Bingjie He
- Panyu Health Management Center, Guangzhou, China
| | - Hanwei Chen
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China
- Medical Imaging Institute of Panyu, Guangzhou, China
- Panyu Health Management Center, Guangzhou, China
| | - Yufeng Ye
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China
- Medical Imaging Institute of Panyu, Guangzhou, China
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23
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Yang Y, Huan X, Guo D, Wang X, Niu S, Li K. Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study. LA RADIOLOGIA MEDICA 2023; 128:1103-1115. [PMID: 37464200 DOI: 10.1007/s11547-023-01683-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 07/10/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE To externally validate the performance of automated stenosis detection on head and neck CT angiography (CTA) and investigate the impact factors using an independent bi-center dataset with digital subtraction angiography (DSA) as the ground truth. MATERIAL AND METHODS Patients who underwent head and neck CTA and DSA between January 2019 and December 2021 were retrospectively included. The degree of stenosis was automatically evaluated using CerebralDoc based on CTA. The performance of CerebralDoc across levels (per-patient, per-region, per-vessel, and per-segment) and thresholds (≥ 50%, ≥ 70%, and = 100%) was evaluated. Logistic regression was performed to identify independent factors associated with false negative results. RESULTS 296 patients were analyzed. Specificity across levels and thresholds was high, exceeding 92%. The area under the curve ranged from poor (0.615, 95% CI: 0.544, 0.686; at the region-based analysis for stenosis ≥ 70%) to excellent (0.945, 95% CI: 0.905, 0.985; at the patient-based analysis for stenosis ≥ 50%). Sensitivity ranged from 0.714 (95% CI: 0.675, 0.750) at the segment-based analysis for stenosis ≥ 70% to 0.895 (95% CI: 0.849, 0.919) at the patient-based analysis for stenosis ≥ 50%. The multiple logistic regression analysis revealed that false negative results were primarily more likely to specific stenosis locations (particularly the M2 segment and skull base segment of the internal carotid artery) and occlusion. CONCLUSIONS CerebralDoc has the potential to automated stenosis detection on head and neck CTA, but further efforts are needed to optimize its performance.
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Affiliation(s)
- Yongwei Yang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
- Department of Radiology, the Fifth People's Hospital of Chongqing, Chongqing, China
| | - Xinyue Huan
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Xiaolin Wang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Shengwen Niu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China
| | - Kunhua Li
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China.
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24
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Zhan Z, Gu F, Ji Y, Zhang Y, Ge Y, Wang Z. Thrombectomy with and without computed tomography perfusion imaging for large-vessel occlusion stroke in the extended time window: a meta-analysis of randomized clinical trials. Front Neurol 2023; 14:1185554. [PMID: 37669248 PMCID: PMC10470654 DOI: 10.3389/fneur.2023.1185554] [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: 03/13/2023] [Accepted: 07/27/2023] [Indexed: 09/07/2023] Open
Abstract
Objective In recent years, several studies have used computed tomography perfusion (CTP) to assess whether mechanical thrombectomy can be performed in patients with large-vessel occlusion (LVO) stroke in an extended time window. However, it has the disadvantage of being time-consuming and expensive. This study aimed to compare the impact of the CTP group with the non-CTP group [non-contrast CT (NCCT) ± CT angiography (CTA)] on the prognosis of this patient population. Methods A search of PubMed, EMBASE, and the Cochrane Library databases was conducted to collect randomized controlled trials (RCTs) comparing the two strategies. Outcome indicators and factors influencing prognosis were summarized by standardized mean differences, ratios, and relative risks with 95% confidence intervals using a random-effects model. Results A total of two RCTs were included in the combined analysis. There were no significant differences in the main outcome indicators (modified Rankin Scale score at 90 days, successful postoperative reperfusion rate) or the incidence of adverse events (90-day mortality and symptomatic intracranial hemorrhage) between the NCCT ± CTA and CTP groups. The time from the last puncture appeared to be significantly shorter in the NCCT ± CTA group than in the CTP group (SMD: -0.14; 95% CI: -0.24, -0.04). Among them, age (OR: 0.96; 95% CI: 0.94, 0.98), ASPECTS (OR: 1.18; 95% CI: 1.12, 1.24), NIHSS score (OR: 0.90; 95% CI: 0.89, 0.91), and diabetes (OR: 0.69; 95% CI: 0.54, 0.88) were associated with a 90-day independent functional outcome. Conclusion These findings suggest that the choice of NCCT ± CTA (without CTP) for the assessment of mechanical thrombectomy within 6-24 h after LVO in the anterior circulation is not significantly different from CTP; instead, the choice of NCCT ± CTA significantly reduces the time from onset to arterial puncture.
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Affiliation(s)
- Zheng Zhan
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Feng Gu
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Yi Ji
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Yu Zhang
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Yi Ge
- Department of Neurology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Zhong Wang
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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25
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Pierre K, Haneberg AG, Kwak S, Peters KR, Hochhegger B, Sananmuang T, Tunlayadechanont P, Tighe PJ, Mancuso A, Forghani R. Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond. Semin Roentgenol 2023; 58:158-169. [PMID: 37087136 DOI: 10.1053/j.ro.2023.02.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 04/24/2023]
Abstract
There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication. These non-diagnostic workflow and process optimization tasks are an important part of the arsenal of potential AI tools that can streamline day to day clinical practice and patient care.
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Affiliation(s)
- Kevin Pierre
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Adam G Haneberg
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Sean Kwak
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL
| | - Keith R Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Thiparom Sananmuang
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Padcha Tunlayadechanont
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Patrick J Tighe
- Departments of Anesthesiology & Orthopaedic Surgery, University of Florida College of Medicine, Gainesville, FL
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
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26
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Chen M. JNIS spotlight: commissioned reviews. J Neurointerv Surg 2023; 15:jnis-2022-020019. [PMID: 36593117 DOI: 10.1136/jnis-2022-020019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2022] [Indexed: 01/04/2023]
Affiliation(s)
- Michael Chen
- Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
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27
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Cui L, Fan Z, Yang Y, Liu R, Wang D, Feng Y, Lu J, Fan Y. Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2456550. [PMID: 36420096 PMCID: PMC9678444 DOI: 10.1155/2022/2456550] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 09/27/2022] [Accepted: 10/20/2022] [Indexed: 09/15/2023]
Abstract
Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.
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Affiliation(s)
- Liyuan Cui
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhiyuan Fan
- Centre of Intelligent Medical Technology and Equipment, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China
| | - Yingjian Yang
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Rui Liu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Dajiang Wang
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yingying Feng
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Jiahui Lu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yifeng Fan
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
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28
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Rohde S, Münnich N. [Artificial intelligence in orthopaedic and trauma surgery imaging]. ORTHOPADIE (HEIDELBERG, GERMANY) 2022; 51:748-756. [PMID: 35980460 DOI: 10.1007/s00132-022-04293-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Artificial intelligence (AI) is playing an increasing role in radiological imaging in orthopaedics and trauma surgery. The algorithms available to date are predominantly used in the detection of (occult) fractures and in length and angle measurements in conventional X‑ray images. However, current AI solutions also enable the analysis and pattern recognition of CT datasets, e.g. in the detection of rib or vertebral body fractures. A special application is EOS™ (ATEC Spine Group, Paris, France), which enables a 3‑D simulation of the axial skeleton and semi-automatic length and angle calculations based on a digital 2‑D X‑ray image. In this paper, the current spectrum of AI applications for orthopaedics and trauma surgery is presented and discussed.
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Affiliation(s)
- Stefan Rohde
- Klinik für Radiologie und Neuroradiologie, Klinikum Dortmund gGmbH, Beurhausstr. 40, 44137, Dortmund, Deutschland.
- Fakultät für Gesundheit, Universität Witten-Herdecke, Witten, Deutschland.
| | - Nico Münnich
- Klinik für Radiologie und Neuroradiologie, Klinikum Dortmund gGmbH, Beurhausstr. 40, 44137, Dortmund, Deutschland
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