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Wu Y, Dong D, Zhu L, Luo Z, Liu Y, Xie X. Interpretable machine learning models for detecting peripheral neuropathy and lower extremity arterial disease in diabetics: an analysis of critical shared and unique risk factors. BMC Med Inform Decis Mak 2024; 24:200. [PMID: 39039521 PMCID: PMC11265186 DOI: 10.1186/s12911-024-02595-z] [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] [Accepted: 07/01/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND Diabetic peripheral neuropathy (DPN) and lower extremity arterial disease (LEAD) are significant contributors to diabetic foot ulcers (DFUs), which severely affect patients' quality of life. This study aimed to develop machine learning (ML) predictive models for DPN and LEAD and to identify both shared and distinct risk factors. METHODS This retrospective study included 479 diabetic inpatients, of whom 215 were diagnosed with DPN and 69 with LEAD. Clinical data and laboratory results were collected for each patient. Feature selection was performed using three methods: mutual information (MI), random forest recursive feature elimination (RF-RFE), and the Boruta algorithm to identify the most important features. Predictive models were developed using logistic regression (LR), random forest (RF), and eXtreme Gradient Boosting (XGBoost), with particle swarm optimization (PSO) used to optimize their hyperparameters. The SHapley Additive exPlanation (SHAP) method was applied to determine the importance of risk factors in the top-performing models. RESULTS For diagnosing DPN, the XGBoost model was most effective, achieving a recall of 83.7%, specificity of 86.8%, accuracy of 85.4%, and an F1 score of 83.7%. On the other hand, the RF model excelled in diagnosing LEAD, with a recall of 85.7%, specificity of 92.9%, accuracy of 91.9%, and an F1 score of 82.8%. SHAP analysis revealed top five critical risk factors shared by DPN and LEAD, including increased urinary albumin-to-creatinine ratio (UACR), glycosylated hemoglobin (HbA1c), serum creatinine (Scr), older age, and carotid stenosis. Additionally, distinct risk factors were pinpointed: decreased serum albumin and lower lymphocyte count were linked to DPN, while elevated neutrophil-to-lymphocyte ratio (NLR) and higher D-dimer levels were associated with LEAD. CONCLUSIONS This study demonstrated the effectiveness of ML models in predicting DPN and LEAD in diabetic patients and identified significant risk factors. Focusing on shared risk factors may greatly reduce the prevalence of both conditions, thereby mitigating the risk of developing DFUs.
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Affiliation(s)
- Ya Wu
- Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Danmeng Dong
- School of Medicine, Anhui University of Science and Technology, Huainan, China
| | - Lijie Zhu
- Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zihong Luo
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Yang Liu
- Department of Geriatrics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaoyun Xie
- Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
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Rimmerman ET, Stacy MR. Applications of SPECT and PET Imaging for the Physiological Evaluation of Lower Extremity Peripheral Artery Disease. Int J Mol Sci 2024; 25:7474. [PMID: 39000580 PMCID: PMC11242786 DOI: 10.3390/ijms25137474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/01/2024] [Accepted: 07/04/2024] [Indexed: 07/16/2024] Open
Abstract
Peripheral artery disease (PAD) is classified as the narrowing or complete occlusion of the lower extremity arteries due to atherosclerosis. The risk of developing PAD increases with increased age and risk factors such as smoking, diabetes, hypertension, and hypercholesterolemia. Current treatment for PAD involves lifestyle and symptom management, statin and antiplatelet therapy, and/or surgical interventions to improve quality of life with varying efficacy. PAD affects approximately 5 to 6 percent of the global population, with this global burden continuing to increase. Despite the increase in disease prevalence, no gold standard functional diagnostic tool has been established for enabling early detection of the disease, appropriate medical management, and prediction of adverse outcomes for PAD patients. The visualization and quantification of the physiological consequences of PAD are possible by way of nuclear imaging: specifically, via scintigraphy, single-photon emission computed tomography (SPECT), and positron emission tomography (PET) imaging. These non-invasive modalities, when combined with targeted radionuclides, possess utility for detecting functional perfusion deficits and provide unique insight into muscle tissue- and vascular-level characteristics of PAD patients. This review discusses the past, present, and emerging applications of hybrid nuclear imaging modalities in the evaluation and monitoring of patients with PAD.
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Affiliation(s)
- Eleanor T. Rimmerman
- Interdisciplinary Biophysics Graduate Program, The Ohio State University, Columbus, OH 43210, USA
- Center for Regenerative Medicine, Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215, USA
| | - Mitchel R. Stacy
- Interdisciplinary Biophysics Graduate Program, The Ohio State University, Columbus, OH 43210, USA
- Center for Regenerative Medicine, Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215, USA
- Division of Vascular Diseases and Surgery, Department of Surgery, The Ohio State University College of Medicine, Columbus, OH 43210, USA
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Ravindhran B, Prosser J, Lim A, Mishra B, Lathan R, Hitchman LH, Smith GE, Carradice D, Chetter IC, Thakker D, Pymer S. Tailored risk assessment and forecasting in intermittent claudication. BJS Open 2024; 8:zrad166. [PMID: 38411507 PMCID: PMC10898330 DOI: 10.1093/bjsopen/zrad166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/23/2023] [Accepted: 12/14/2023] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. The development of a precise risk stratification tool is proposed through a machine-learning algorithm that aims to provide personalized outcome predictions for different management strategies. METHODS Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample based on patients with intermittent claudication from a vascular centre to predict chronic limb-threatening ischaemia, two or more revascularization procedures, major adverse cardiovascular events, and major adverse limb events. Algorithm performance was evaluated using the area under the receiver operating characteristic curve. Calibration curves were generated to assess the consistency between predicted and actual outcomes. Decision curve analysis was employed to evaluate the clinical utility. Validation was performed using a similar dataset. RESULTS The bootstrapped sample of 10 000 patients was based on 255 patients. The model was validated using a similar sample of 254 patients. The area under the receiver operating characteristic curves for risk of progression to chronic limb-threatening ischaemia at 2 years (0.892), risk of progression to chronic limb-threatening ischaemia at 5 years (0.866), likelihood of major adverse cardiovascular events within 5 years (0.836), likelihood of major adverse limb events within 5 years (0.891), and likelihood of two or more revascularization procedures within 5 years (0.896) demonstrated excellent discrimination. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analysis confirmed clinical utility. Logistic regression yielded slightly lower area under the receiver operating characteristic curves for these outcomes compared with the least absolute shrinkage and selection operator algorithm (0.728, 0.717, 0.746, 0.756, and 0.733 respectively). External calibration curve and decision curve analysis confirmed the reliability and clinical utility of the model, surpassing traditional logistic regression. CONCLUSION The machine-learning algorithm successfully predicts outcomes for patients with intermittent claudication across various initial treatment strategies, offering potential for improved risk stratification and patient outcomes.
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Affiliation(s)
- Bharadhwaj Ravindhran
- Academic Vascular Surgical Unit, Allam Diabetes Centre, Hull Royal Infirmary, Hull, UK
- Department of Health Sciences, University of York, York, UK
| | - Jonathon Prosser
- Academic Vascular Surgical Unit, Allam Diabetes Centre, Hull Royal Infirmary, Hull, UK
| | - Arthur Lim
- Academic Vascular Surgical Unit, Allam Diabetes Centre, Hull Royal Infirmary, Hull, UK
| | - Bhupesh Mishra
- School of Computer Science, University of Hull, Hull, UK
| | - Ross Lathan
- Academic Vascular Surgical Unit, Allam Diabetes Centre, Hull Royal Infirmary, Hull, UK
| | - Louise H Hitchman
- Academic Vascular Surgical Unit, Allam Diabetes Centre, Hull Royal Infirmary, Hull, UK
| | - George E Smith
- Academic Vascular Surgical Unit, Allam Diabetes Centre, Hull Royal Infirmary, Hull, UK
| | - Daniel Carradice
- Academic Vascular Surgical Unit, Allam Diabetes Centre, Hull Royal Infirmary, Hull, UK
| | - Ian C Chetter
- Academic Vascular Surgical Unit, Allam Diabetes Centre, Hull Royal Infirmary, Hull, UK
| | - Dhaval Thakker
- School of Computer Science, University of Hull, Hull, UK
| | - Sean Pymer
- Academic Vascular Surgical Unit, Allam Diabetes Centre, Hull Royal Infirmary, Hull, UK
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Ho V, Brown Johnson C, Ghanzouri I, Amal S, Asch S, Ross E. Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning-Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders. JMIR Cardio 2023; 7:e44732. [PMID: 37930755 PMCID: PMC10660241 DOI: 10.2196/44732] [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: 11/30/2022] [Revised: 07/23/2023] [Accepted: 08/21/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Peripheral arterial disease (PAD) is underdiagnosed, partially due to a high prevalence of atypical symptoms and a lack of physician and patient awareness. Implementing clinical decision support tools powered by machine learning algorithms may help physicians identify high-risk patients for diagnostic workup. OBJECTIVE This study aims to evaluate barriers and facilitators to the implementation of a novel machine learning-based screening tool for PAD among physician and patient stakeholders using the Consolidated Framework for Implementation Research (CFIR). METHODS We performed semistructured interviews with physicians and patients from the Stanford University Department of Primary Care and Population Health, Division of Cardiology, and Division of Vascular Medicine. Participants answered questions regarding their perceptions toward machine learning and clinical decision support for PAD detection. Rapid thematic analysis was performed using templates incorporating codes from CFIR constructs. RESULTS A total of 12 physicians (6 primary care physicians and 6 cardiovascular specialists) and 14 patients were interviewed. Barriers to implementation arose from 6 CFIR constructs: complexity, evidence strength and quality, relative priority, external policies and incentives, knowledge and beliefs about intervention, and individual identification with the organization. Facilitators arose from 5 CFIR constructs: intervention source, relative advantage, learning climate, patient needs and resources, and knowledge and beliefs about intervention. Physicians felt that a machine learning-powered diagnostic tool for PAD would improve patient care but cited limited time and authority in asking patients to undergo additional screening procedures. Patients were interested in having their physicians use this tool but raised concerns about such technologies replacing human decision-making. CONCLUSIONS Patient- and physician-reported barriers toward the implementation of a machine learning-powered PAD diagnostic tool followed four interdependent themes: (1) low familiarity or urgency in detecting PAD; (2) concerns regarding the reliability of machine learning; (3) differential perceptions of responsibility for PAD care among primary care versus specialty physicians; and (4) patient preference for physicians to remain primary interpreters of health care data. Facilitators followed two interdependent themes: (1) enthusiasm for clinical use of the predictive model and (2) willingness to incorporate machine learning into clinical care. Implementation of machine learning-powered diagnostic tools for PAD should leverage provider support while simultaneously educating stakeholders on the importance of early PAD diagnosis. High predictive validity is necessary for machine learning models but not sufficient for implementation.
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Affiliation(s)
- Vy Ho
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Cati Brown Johnson
- Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Ilies Ghanzouri
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Saeed Amal
- College of Engineering, Northeastern University, Boston, MA, United States
| | - Steven Asch
- Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
- Center for Innovation to Implementation, Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, United States
| | - Elsie Ross
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
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Chen Z, Ying TC, Chen J, Wang Y, Wu C, Su Z. Assessment of Renal Fibrosis in Patients With Chronic Kidney Disease Using Shear Wave Elastography and Clinical Features: A Random Forest Approach. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1665-1671. [PMID: 37105772 DOI: 10.1016/j.ultrasmedbio.2023.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVE Renal fibrosis is the common pathological hallmark of chronic kidney disease (CKD) progression. In this study, a random forest (RF) classifier based on 2-D shear wave elastography (SWE) and clinical features for the differential severity of renal fibrosis in patients with CKD is proposed. METHODS A total of 162 patients diagnosed with CKD who underwent 2-D SWE and renal biopsy were prospectively enrolled from April 2019 to December 2021 and then randomized into training (n = 114) and validation (n = 48) cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression and recursive feature elimination for support vector machines (SVM-RFE) algorithm were employed to select renal fibrosis-related features from clinical information and elastosonographic findings. An RF model was subsequently constructed using the aforementioned informative parameters in the training cohort and evaluated in terms of discrimination, calibration and clinical utility in both cohorts. RESULTS The LASSO and SVM-RFE analyses revealed that age, sex, blood urea nitrogen, renal resistive index, hypertension and the 2D-SWE value were independent risk variables associated with renal fibrosis severity. The established RF model incorporating these six variables exhibited fine discrimination in both the derivation (area under the curve [AUC]: 0.84, 95% confidence interval [CI]: 0.76-0.91) and validation (AUC: 0.88, 95% CI: 0.77-0.98) cohorts. Moreover, the calibration curve revealed satisfactory predictive accuracy, and the decision curve analysis revealed a significant clinical net benefit. CONCLUSION The developed RF model, via a combination of the 2-D SWE value and clinical information, indicated satisfactory diagnostic performance and clinical practicality toward differentiating moderate-severe from mild renal fibrosis, which may provide critical insight into risk stratification for patients with CKD.
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Affiliation(s)
- Ziman Chen
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Tin Cheung Ying
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Jiaxin Chen
- Department of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Yingli Wang
- Ultrasound Department, EDAN Instruments, Inc., Shenzhen, China
| | - Chaoqun Wu
- Department of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Zhongzhen Su
- Department of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
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Kampaktsis PN, Emfietzoglou M, Al Shehhi A, Fasoula NA, Bakogiannis C, Mouselimis D, Tsarouchas A, Vassilikos VP, Kallmayer M, Eckstein HH, Hadjileontiadis L, Karlas A. Artificial intelligence in atherosclerotic disease: Applications and trends. Front Cardiovasc Med 2023; 9:949454. [PMID: 36741834 PMCID: PMC9896100 DOI: 10.3389/fcvm.2022.949454] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 12/28/2022] [Indexed: 01/21/2023] Open
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.
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Affiliation(s)
- Polydoros N. Kampaktsis
- Division of Cardiology, Columbia University Irving Medical Center, New York, NY, United States,*Correspondence: Polydoros N. Kampaktsis,
| | - Maria Emfietzoglou
- Heart Centre, John Radcliffe Hospital, Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Aamna Al Shehhi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Nikolina-Alexia Fasoula
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany,School of Medicine, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, Germany
| | - Constantinos Bakogiannis
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Mouselimis
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anastasios Tsarouchas
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilios P. Vassilikos
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Michael Kallmayer
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Hans-Henning Eckstein
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Leontios Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates,Healthcare Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates,Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Angelos Karlas
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany,School of Medicine, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, Germany,Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
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Khanna NN, Maindarkar M, Puvvula A, Paul S, Bhagawati M, Ahluwalia P, Ruzsa Z, Sharma A, Munjral S, Kolluri R, Krishnan PR, Singh IM, Laird JR, Fatemi M, Alizad A, Dhanjil SK, Saba L, Balestrieri A, Faa G, Paraskevas KI, Misra DP, Agarwal V, Sharma A, Teji J, Al-Maini M, Nicolaides A, Rathore V, Naidu S, Liblik K, Johri AM, Turk M, Sobel DW, Pareek G, Miner M, Viskovic K, Tsoulfas G, Protogerou AD, Mavrogeni S, Kitas GD, Fouda MM, Kalra MK, Suri JS. Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report. J Cardiovasc Dev Dis 2022; 9:jcdd9080268. [PMID: 36005433 PMCID: PMC9409845 DOI: 10.3390/jcdd9080268] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 07/30/2022] [Accepted: 08/09/2022] [Indexed: 12/15/2022] Open
Abstract
The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Mahesh Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Annu’s Hospitals for Skin and Diabetes, Nellore 524101, India
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
| | - Zoltan Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Smiksha Munjral
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Raghu Kolluri
- Ohio Health Heart and Vascular, Columbus, OH 43214, USA
| | | | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Surinder K. Dhanjil
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
| | - Antonella Balestrieri
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124 Cagliari, Italy
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Aman Sharma
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Jagjit Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, 2408 Nicosia, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA 95119, USA
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
| | - Kiera Liblik
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
| | - David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 17674 Athens, Greece
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Manudeep K. Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Correspondence: ; Tel.: +1-916-749-5628
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8
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Effect of transcutaneous auricular vagus nerve stimulation on delayed neurocognitive recovery in elderly patients. Aging Clin Exp Res 2022; 34:2421-2429. [PMID: 35809206 DOI: 10.1007/s40520-022-02177-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/08/2022] [Indexed: 11/01/2022]
Abstract
BACKGROUND The aim of this study was to investigate whether transauricular vagus nerve stimulation (taVNS) could decrease the incidence of delayed neurocognitive recovery (dNCR) in elderly adults after total joint arthroplasty (TJA). METHODS A prospective, randomized, double-blind, sham-controlled trial was designed. In total, 124 elderly patients undergoing TJA were enrolled and randomly assigned to taVNS group (n = 62), who received taVNS at 1 h before anesthetic induction until the end of surgery, or sham stimulation (SS) group (n = 62), who received SS in the same manner. Neuropsychological batteries were performed before and at 1 week after surgery to assess the incidence of dNCR. Blood samples were collected before surgery and at 1 day after surgery to detect the activity of cholinesterase (AChE and BChE), as well as the levels of inflammatory factors (TNF-α, IL-1β, IL-6, and HMGB1) and brain damage factor S100β. RESULTS Of 124 patients, 119 completed 1 week neuropsychological tests. The incidence of dNCR was significantly decreased in taVNS group [10% (6/60)] compared with the SS group [27.1% (16/59)] (P < 0.05). Patients who received taVNS had lower blood levels of AChE, BChE, IL-6, HMGB1, and S100β after surgery (P < 0.05), as compared with those in the SS group. There was no difference in TNF-α between the two groups. CONCLUSION The taVNS can decrease the incidence of dNCR after TJA in elderly patients, which may be related to the inhibition of inflammatory cytokine production and the reduction of cholinesterase activity.
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Spanos K, Giannoukas AD, Kouvelos G, Tsougos I, Mavroforou A. Artificial Intelligence application in Vascular Diseases. J Vasc Surg 2022; 76:615-619. [PMID: 35661694 DOI: 10.1016/j.jvs.2022.03.895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/11/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Konstantinos Spanos
- Department of Vascular Surgery, School of Health Sciences, University of Thessaly, Larissa, Greece.
| | - Athanasios D Giannoukas
- Department of Vascular Surgery, School of Health Sciences, University of Thessaly, Larissa, Greece.
| | - George Kouvelos
- Department of Vascular Surgery, School of Health Sciences, University of Thessaly, Larissa, Greece.
| | - Ioannis Tsougos
- Department of Medical Physics and Informatics, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece.
| | - Anna Mavroforou
- Deontology and Bioethics Lab, Faculty of Nursing, School of Health Sciences, University of Thessaly, Larissa, Greece.
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Pan T, Jiang X, Liu H, Liu Y, Fu W, Dong Z. Prediction of 2-Year Major Adverse Limb Event-Free Survival After Percutaneous Transluminal Angioplasty and Stenting for Lower Limb Atherosclerosis Obliterans: A Machine Learning-Based Study. Front Cardiovasc Med 2022; 9:783336. [PMID: 35224037 PMCID: PMC8863671 DOI: 10.3389/fcvm.2022.783336] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 01/06/2022] [Indexed: 11/23/2022] Open
Abstract
Background The current scoring systems could not predict prognosis after endovascular therapy for peripheral artery disease. Machine learning could make predictions for future events by learning a specific pattern from existing data. This study aimed to demonstrate machine learning could make an accurate prediction for 2-year major adverse limb event-free survival (MFS) after percutaneous transluminal angioplasty (PTA) and stenting for lower limb atherosclerosis obliterans (ASO). Methods A lower limb ASO cohort of 392 patients who received PTA and stenting was split to the training set and test set by 4:1 in chronological order. Demographic, medical, and imaging data were used to build machine learning models to predict 2-year MFS. The discrimination and calibration of artificial neural network (ANN) and random forest models were compared with the logistic regression model, using the area under the receiver operating curve (ROCAUC) with DeLong test, and the calibration curve with Hosmer–Lemeshow goodness-of-fit test, respectively. Results The ANN model (ROCAUC = 0.80, 95% CI: 0.68–0.89) but not the random forest model (ROCAUC = 0.78, 95% CI: 0.66–0.87) significantly outperformed the logistic regression model (ROCAUC = 0.73, 95% CI: 0.60–0.83, P = 0.01 and P = 0.24). The ANN model the logistic regression model demonstrated good calibration performance (P = 0.73 and P = 0.28), while the random forest model showed poor calibration (P < 0.01). The calibration curve of the ANN model was visually the closest to the perfectly calibrated line. Conclusion Machine learning models could accurately predict 2-year MFS after PTA and stenting for lower limb ASO, in which the ANN model had better discrimination and calibration. Machine learning-derived prediction tools might be clinically useful to automatically identify candidates for PTA and stenting.
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Affiliation(s)
- Tianyue Pan
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Xiaolang Jiang
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Hao Liu
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Yifan Liu
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Weiguo Fu
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
- *Correspondence: Weiguo Fu
| | - Zhihui Dong
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
- Zhihui Dong
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