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Csore J, Roy TL, Wright G, Karmonik C. Unsupervised classification of multi-contrast magnetic resonance histology of peripheral arterial disease lesions using a convolutional variational autoencoder with a Gaussian mixture model in latent space: A technical feasibility study. Comput Med Imaging Graph 2024; 115:102372. [PMID: 38581959 DOI: 10.1016/j.compmedimag.2024.102372] [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: 11/20/2023] [Revised: 02/09/2024] [Accepted: 03/18/2024] [Indexed: 04/08/2024]
Abstract
PURPOSE To investigate the feasibility of a deep learning algorithm combining variational autoencoder (VAE) and two-dimensional (2D) convolutional neural networks (CNN) for automatically quantifying hard tissue presence and morphology in multi-contrast magnetic resonance (MR) images of peripheral arterial disease (PAD) occlusive lesions. METHODS Multi-contrast MR images (T2-weighted and ultrashort echo time) were acquired from lesions harvested from six amputated legs with high isotropic spatial resolution (0.078 mm and 0.156 mm, respectively) at 9.4 T. A total of 4014 pseudo-color combined images were generated, with 75% used to train a VAE employing custom 2D CNN layers. A Gaussian mixture model (GMM) was employed to classify the latent space data into four tissue classes: I) concentric calcified (c), II) eccentric calcified (e), III) occluded with hard tissue (h) and IV) occluded with soft tissue (s). Test image probabilities, encoded by the trained VAE were used to evaluate model performance. RESULTS GMM component classification probabilities ranged from 0.92 to 0.97 for class (c), 1.00 for class (e), 0.82-0.95 for class (h) and 0.56-0.93 for the remaining class (s). Due to the complexity of soft-tissue lesions reflected in the heterogeneity of the pseudo-color images, more GMM components (n=17) were attributed to class (s), compared to the other three (c, e and h) (n=6). CONCLUSION Combination of 2D CNN VAE and GMM achieves high classification probabilities for hard tissue-containing lesions. Automatic recognition of these classes may aid therapeutic decision-making and identifying uncrossable lesions prior to endovascular intervention.
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Affiliation(s)
- Judit Csore
- DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin Street, Houston, TX 77030, USA; Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest 1122, Hungary.
| | - Trisha L Roy
- DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin Street, Houston, TX 77030, USA
| | - Graham Wright
- Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada
| | - Christof Karmonik
- MRI Core, Translational Imaging Center, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, TX 77030, USA
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2
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Rastogi T, Girerd N. Enhancing machine learning-based survival prediction models for patients with cardiovascular diseases. Int J Cardiol 2024; 410:132195. [PMID: 38782072 DOI: 10.1016/j.ijcard.2024.132195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 04/26/2024] [Accepted: 05/20/2024] [Indexed: 05/25/2024]
Affiliation(s)
- Tripti Rastogi
- Université de Lorraine, INSERM, Centre d'Investigations Cliniques Plurithématique 1433, Inserm U1116, CHRU de Nancy and F-CRIN INI-CRCT, Nancy, France
| | - Nicolas Girerd
- Université de Lorraine, INSERM, Centre d'Investigations Cliniques Plurithématique 1433, Inserm U1116, CHRU de Nancy and F-CRIN INI-CRCT, Nancy, France.
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3
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Campbell A, Alslaim H, Duson S, Rowe VL. Educating Trainees to Treat Peripheral Arterial Disease: Challenges and Opportunities. Ann Vasc Surg 2024:S0890-5096(24)00150-X. [PMID: 38582208 DOI: 10.1016/j.avsg.2023.12.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 04/08/2024]
Abstract
Educating trainees to treat Peripheral Artery Disease (PAD) carries specific contemporary challenges. The national increase of the prevalence of PAD combined with a significant shortage of vascular surgeons creates a need for concern for future management of this complex disease. Over the past 2 decades, traditional (5 + 2) and integrated (0 + 5) paradigms have fostered trainee annual growth and comparable case distribution and volumes in endovascular and open surgical treatment options have been maintained. Close evaluation into not only the absolute numbers of surgical cases, but the level of trainee involvement in each logged case is recommended. Future implementation of the Entrustable Professional Activity (EPA) modules will hopefully assist in ensuring linear development of surgical skill and judgment. Additionally, advances in individual and systems level techniques to enhance skill acquisition in the form of "off-the job training" and simulation-based training may provide an enhancement to traditional technical training methods. Finally, the possibility and role of artificial intelligence in vascular surgery skill training must not be ignored, but carefully explored and utilized to modernize cognitive and technical skill preparation for trainees in the and delivery of care for PAD patients. Overall, the training residents for the treatment of PAD patients will be associated with new challenges that vascular surgery must embrace and surmount to advance our specialty.
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Affiliation(s)
- Anthony Campbell
- Department of General Surgery, Medical College of Georgia, Augusta, GA
| | - Hossam Alslaim
- Department of Vascular Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Sira Duson
- University of Tennessee Health Science Center, Memphis, TN
| | - Vincent L Rowe
- Division of Vascular and Endovascular Surgery, Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA.
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4
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Song JH, Tomihama RT, Roh D, Cabrera A, Dardik A, Kiang SC. Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients. Ann Vasc Surg 2024:S0890-5096(24)00143-2. [PMID: 38582202 DOI: 10.1016/j.avsg.2023.11.057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 11/23/2023] [Indexed: 04/08/2024]
Abstract
Peripheral artery disease is a major atherosclerotic disease that is associated with poor outcomes such as limb loss, cardiovascular morbidity, and death. Artificial intelligence (AI) has seen increasing integration in medicine, and its various applications can optimize the care of peripheral artery disease (PAD) patients in diagnosis, predicting patient outcomes, and imaging interpretation. In this review, we introduce various AI applications such as natural language processing, supervised machine learning, and deep learning, and we analyze the current literature in which these algorithms have been applied to PAD.
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Affiliation(s)
- Jee Hoon Song
- Division of Vascular Surgery, Department of Surgery, Linda University School of Medicine, Loma Linda, CA
| | - Roger T Tomihama
- Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA
| | - Daniel Roh
- Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA
| | - Andrew Cabrera
- Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA
| | - Alan Dardik
- Division of Vascular Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT
| | - Sharon C Kiang
- Division of Vascular Surgery, Department of Surgery, Linda University School of Medicine, Loma Linda, CA; Division of Vascular Surgery, Department of Surgery, VA Loma Linda Healthcare System, Loma Linda, CA.
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5
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Li B, Shaikh F, Zamzam A, Syed MH, Abdin R, Qadura M. A machine learning algorithm for peripheral artery disease prognosis using biomarker data. iScience 2024; 27:109081. [PMID: 38361633 PMCID: PMC10867451 DOI: 10.1016/j.isci.2024.109081] [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: 08/17/2023] [Revised: 01/11/2024] [Accepted: 01/26/2024] [Indexed: 02/17/2024] Open
Abstract
Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada
| | - Farah Shaikh
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Abdelrahman Zamzam
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Muzammil H. Syed
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Rawand Abdin
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Mohammad Qadura
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
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Khagi B, Belousova T, Short CM, Taylor A, Nambi V, Ballantyne CM, Bismuth J, Shah DJ, Brunner G. A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging. Magn Reson Imaging 2024; 106:31-42. [PMID: 38065273 DOI: 10.1016/j.mri.2023.11.014] [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: 08/29/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 01/12/2024]
Abstract
Diagnosing and assessing the risk of peripheral artery disease (PAD) has long been a focal point for medical practitioners. The impaired blood circulation in PAD patients results in altered microvascular perfusion patterns in the calf muscles which is the primary location of intermittent claudication pain. Consequently, we hypothesized that changes in perfusion and increase in connective tissue could lead to alterations in the appearance or texture patterns of the skeletal calf muscles, as visualized with non-invasive imaging techniques. We designed an automatic pipeline for textural feature extraction from contrast-enhanced magnetic resonance imaging (CE-MRI) scans and used the texture features to train machine learning models to detect the heterogeneity in the muscle pattern among PAD patients and matched controls. CE-MRIs from 36 PAD patients and 20 matched controls were used for preparing training and testing data at a 7:3 ratio with cross-validation (CV) techniques. We employed feature arrangement and selection methods to optimize the number of features. The proposed method achieved a peak accuracy of 94.11% and a mean testing accuracy of 84.85% in a 2-class classification approach (controls vs. PAD). A three-class classification approach was performed to identify a high-risk PAD sub-group which yielded an average test accuracy of 83.23% (matched controls vs. PAD without diabetes vs. PAD with diabetes). Similarly, we obtained 78.60% average accuracy among matched controls, PAD treadmill exercise completers, and PAD exercise treadmill non-completers. Machine learning and imaging-based texture features may be of interest in the study of lower extremity ischemia.
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Affiliation(s)
- Bijen Khagi
- Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Tatiana Belousova
- Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX, USA
| | - Christina M Short
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Addison Taylor
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA; Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Vijay Nambi
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA; Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA; Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Christie M Ballantyne
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA; Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Jean Bismuth
- Division of Vascular Surgery, USF Health Morsani School of Medicine, Tampa, FL, USA
| | - Dipan J Shah
- Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX, USA
| | - Gerd Brunner
- Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine, Hershey, PA, USA; Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
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Long B, Cremat DL, Serpa E, Qian S, Blebea J. Applying Artificial Intelligence to Predict Complications After Endovascular Aneurysm Repair. Vasc Endovascular Surg 2024; 58:65-75. [PMID: 37429299 DOI: 10.1177/15385744231189024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Objective: Complications after Endovascular Aneurysm Repair (EVAR) can be fatal. Patient follow-up for surveillance imaging is becoming more challenging as fewer patients are seen, particularly after the first year. The aim of this study was to develop an artificial intelligence model to predict the complication probability of individual patients to better identify those needing more intensive post-operative surveillance. Methods: Pre-operative CTA 3D reconstruction images of AAA from 273 patients who underwent EVAR from 2011-2020 were collected. Of these, 48 patients had post-operative complications including endoleak, AAA rupture, graft limb occlusion, renal artery occlusion, and neck dilation. A deep convolutional neural network model (VascAI©) was developed which utilized pre-operative 3D CT images to predict risk of complications after EVAR. The model was built with TensorFlow software and run on the Google Colab Platform. An initial training subset of 40 randomly selected patients with complications and 189 without were used to train the AI model while the remaining 8 positive and 36 negative cases tested its performance and prediction accuracy. Data down-sampling was used to alleviate data imbalance and data augmentation methodology to further boost model performance. Results: Successful training was completed on the 229 cases in the training set and then applied to predict the complication probability of each individual in the held-out performance testing cases. The model provided a complication sensitivity of 100% and identified all the patients who later developed complications after EVAR. Of 36 patients without complications, 16 (44%) were falsely predicted to develop complications. The results therefore demonstrated excellent sensitivity for identifying patients who would benefit from more stringent surveillance and decrease the frequency of surveillance in 56% of patients unlike to develop complications. Conclusion: AI models can be developed to predict the risk of post-operative complications with high accuracy. Compared to existing methods, the model developed in this study did not require any expert-annotated data but only the AAA CTA images as inputs. This model can play an assistive role in identifying patients at high risk for post-EVAR complications and the need for greater compliance in surveillance.
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Affiliation(s)
- Becky Long
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
| | - Danielle L Cremat
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
| | - Eduardo Serpa
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
| | - Sinong Qian
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
| | - John Blebea
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
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Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS DIGITAL HEALTH 2023; 2:e0000347. [PMID: 37819910 PMCID: PMC10566734 DOI: 10.1371/journal.pdig.0000347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/14/2023] [Indexed: 10/13/2023]
Abstract
Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.
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Affiliation(s)
- Jana Sedlakova
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
| | - Andrea Horn Wintsch
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center for Gerontology, University of Zurich, Zurich, Switzerland
- CoupleSense: Health and Interpersonal Emotion Regulation Group, University Research Priority Program (URPP) Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Markus Wolf
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Mina Stanikic
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christina Haag
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Chloé Sieber
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Gerold Schneider
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Kaspar Staub
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland
| | - Dominik Alois Ettlin
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Oliver Grübner
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Geography, University of Zurich, Zurich, Switzerland
| | - Fabio Rinaldi
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Dalle Molle Institute for Artificial Intelligence (IDSIA), Switzerland
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Fondazione Bruno Kessler, Trento, Italy
- Swiss Institute of Bioinformatics, Switzerland
| | - Viktor von Wyl
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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9
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Dossabhoy SS, Ho VT, Ross EG, Rodriguez F, Arya S. Artificial intelligence in clinical workflow processes in vascular surgery and beyond. Semin Vasc Surg 2023; 36:401-412. [PMID: 37863612 PMCID: PMC10956485 DOI: 10.1053/j.semvascsurg.2023.07.002] [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: 04/11/2023] [Revised: 06/23/2023] [Accepted: 07/17/2023] [Indexed: 10/22/2023]
Abstract
In the past decade, artificial intelligence (AI)-based applications have exploded in health care. In cardiovascular disease, and vascular surgery specifically, AI tools such as machine learning, natural language processing, and deep neural networks have been applied to automatically detect underdiagnosed diseases, such as peripheral artery disease, abdominal aortic aneurysms, and atherosclerotic cardiovascular disease. In addition to disease detection and risk stratification, AI has been used to identify guideline-concordant statin therapy use and reasons for nonuse, which has important implications for population-based cardiovascular disease health. Although many studies highlight the potential applications of AI, few address true clinical workflow implementation of available AI-based tools. Specific examples, such as determination of optimal statin treatment based on individual patient risk factors and enhancement of intraoperative fluoroscopy and ultrasound imaging, demonstrate the potential promise of AI integration into clinical workflow. Many challenges to AI implementation in health care remain, including data interoperability, model bias and generalizability, prospective evaluation, privacy and security, and regulation. Multidisciplinary and multi-institutional collaboration, as well as adopting a framework for integration, will be critical for the successful implementation of AI tools into clinical practice.
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Affiliation(s)
- Shernaz S Dossabhoy
- Division of Vascular Surgery, Stanford University School of Medicine, 780 Welch Road, CJ350, MC 5639, Palo Alto, CA, 94304
| | - Vy T Ho
- Division of Vascular Surgery, Stanford University School of Medicine, 780 Welch Road, CJ350, MC 5639, Palo Alto, CA, 94304
| | - Elsie G Ross
- Division of Vascular Surgery, Stanford University School of Medicine, 780 Welch Road, CJ350, MC 5639, Palo Alto, CA, 94304
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, CA
| | - Shipra Arya
- Division of Vascular Surgery, Stanford University School of Medicine, 780 Welch Road, CJ350, MC 5639, Palo Alto, CA, 94304.
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10
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Lareyre F, Chaudhuri A, Behrendt CA, Pouhin A, Teraa M, Boyle JR, Tulamo R, Raffort J. Artificial intelligence-based predictive models in vascular diseases. Semin Vasc Surg 2023; 36:440-447. [PMID: 37863618 DOI: 10.1053/j.semvascsurg.2023.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/24/2023] [Accepted: 05/24/2023] [Indexed: 10/22/2023]
Abstract
Cardiovascular disease represents a source of major health problems worldwide, and although medical and technical advances have been achieved, they are still associated with high morbidity and mortality rates. Personalized medicine would benefit from novel tools to better predict individual prognosis and outcomes after intervention. Artificial intelligence (AI) has brought new insights to cardiovascular medicine, especially with the use of machine learning techniques that allow the identification of hidden patterns and complex associations in health data without any a priori assumptions. This review provides an overview on the use of artificial intelligence-based prediction models in vascular diseases, specifically focusing on aortic aneurysm, lower extremity arterial disease, and carotid stenosis. Potential benefits include the development of precision medicine in patients with vascular diseases. In addition, the main challenges that remain to be overcome to integrate artificial intelligence-based predictive models in clinical practice are discussed.
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Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France; Université Côte d'Azur, INSERM U1065, C3M, Nice, France
| | - Arindam Chaudhuri
- Bedfordshire-Milton Keynes Vascular Centre, Bedfordshire Hospitals NHS Foundation Trust, Bedford, UK
| | - Christian-Alexander Behrendt
- Brandenburg Medical School Theodor-Fontane, Neuruppin, Germany; Department of Vascular and Endovascular Surgery, Asklepios Medical School Hamburg, Asklepios Clinic Wandsbek, Hamburg, Germany
| | - Alexandre Pouhin
- Division of Vascular Surgery, Dijon University Hospital, Dijon, France
| | - Martin Teraa
- Department of Vascular Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jonathan R Boyle
- Cambridge Vascular Unit, Cambridge University Hospitals NHS Trust and Department of Surgery, University of Cambridge, Cambridge, UK
| | - Riikka Tulamo
- Department of Vascular Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Juliette Raffort
- Université Côte d'Azur, INSERM U1065, C3M, Nice, France; Institute 3IA Côte d'Azur, Université Côte d'Azur, France; Clinical Chemistry Laboratory, University Hospital of Nice, France.
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11
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Thaxton C, Dardik A. Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence. Semin Vasc Surg 2023; 36:419-425. [PMID: 37863614 PMCID: PMC10589450 DOI: 10.1053/j.semvascsurg.2023.05.003] [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: 03/27/2023] [Revised: 05/01/2023] [Accepted: 05/24/2023] [Indexed: 10/22/2023]
Abstract
Artificial intelligence (AI)-based technologies have garnered interest across a range of disciplines in the past several years, with an even more recent interest in various health care fields, including Vascular Surgery. AI offers a unique ability to analyze health data more quickly and efficiently than could be done by humans alone and can be used for clinical applications such as diagnosis, risk stratification, and follow-up, as well as patient-used applications to improve both patient and provider experiences, mitigate health care disparities, and individualize treatment. As with all novel technologies, AI is not without its risks and carries with it unique ethical considerations that will need to be addressed before its broad integration into health care systems. AI has the potential to revolutionize the way care is provided to patients, including those requiring vascular care.
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Affiliation(s)
- Carly Thaxton
- Department of Surgery, Yale School of Medicine, 10 Amistad Street, Room 437, New Haven, CT 06519; The Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT
| | - Alan Dardik
- Department of Surgery, Yale School of Medicine, 10 Amistad Street, Room 437, New Haven, CT 06519; The Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT; Department of Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT.
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12
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Liu Y, Xue J, Jiang J. Application of machine learning algorithms in electronic medical records to predict amputation-free survival after first revascularization in patients with peripheral artery disease. Int J Cardiol 2023:S0167-5273(23)00594-6. [PMID: 37119943 DOI: 10.1016/j.ijcard.2023.04.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/07/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
BACKGROUND This study aimed to apply eight machine learning algorithms to develop the optimal model to predict amputation-free survival (AFS) after first revascularization in patients with peripheral artery disease (PAD). METHODS Among 2130 patients from 2011 to 2020, 1260 patients who underwent revascularization were randomly assigned to training set and validation set in an 8:2 ratio. 67 clinical parameters were analyzed by lasso regression analysis. Logistic regression, gradient boosting machine, random forest, decision tree, eXtreme gradient boosting, neural network, Cox regression, and random survival forest (RSF) were applied to develop prediction models. The optimal model was compared with GermanVasc score in testing set comprising patients from 2010. RESULTS The postoperative 1/3/5-year AFS were 90%, 79.4%, and 74.1%. Age (HR:1.035, 95%CI: 1.015-1.056), atrial fibrillation (HR:2.257, 95%CI: 1.193-4.271), cardiac ejection fraction (HR:0.064, 95%CI: 0.009-0.413), Rutherford grade ≥ 5 (HR:1.899, 95%CI: 1.296-2.782), creatinine (HR:1.03, 95%CI: 1.02-1.04), surgery duration (HR:1.03, 95%CI: 1.01-1.05), and fibrinogen (HR:1.292, 95%CI: 1.098-1.521) were independent risk factors. The optimal model was developed by RSF algorithm, with 1/3/5-year AUCs in training set of 0.866 (95% CI:0.819-0.912), 0.854 (95% CI:0.811-0.896), 0.844 (95% CI:0.793-0.894), in validation set of 0.741 (95% CI:0.580-0.902), 0.768 (95% CI:0.654-0.882), 0.836 (95% CI:0.719-0.953), and in testing set of 0.821 (95%CI: 0.711-0.931), 0.802 (95%CI: 0.684-0.919), 0.798 (95%CI: 0.657-0.939). The c-index of the model outperformed GermanVasc Score (0.788 vs 0.730). A dynamic nomogram was published on shinyapp (https://wyy2023.shinyapps.io/amputation/). CONCLUSION The optimal prediction model for AFS after first revascularization in patients with PAD was developed by RSF algorithm, which exhibited outstanding prediction performance.
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Affiliation(s)
- Yang Liu
- Department of General surgery, Vascular Surgery, Qilu Hospital of Shandong University, No.107, Road Wen Hua Xi, Jinan, Shandong, China
| | - Junshuai Xue
- Department of General surgery, Qilu Hospital of Shandong University, No.107, Road Wen Hua Xi, Jinan, Shandong, China
| | - Jianjun Jiang
- Department of General surgery, Vascular Surgery, Qilu Hospital of Shandong University, No.107, Road Wen Hua Xi, Jinan, Shandong, China.
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Chaturvedi A, Castro-Dominguez Y, Gertz ZM, Lawson BD, Chandrika P, Gupta R, Milioglou I, Sung JG, Desai NR, Vetrovec G, Kochar A, Guha A. Patterns of Care and Outcomes of Ambulatory Endovascular Interventions in Lower Extremity Peripheral Arterial Disease. Am J Cardiol 2023; 194:17-26. [PMID: 36924641 DOI: 10.1016/j.amjcard.2023.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 01/18/2023] [Accepted: 02/06/2023] [Indexed: 03/18/2023]
Abstract
Lower extremity endovascular intervention (LE-EVI) is gaining popularity as the primary treatment modality for patients with symptomatic peripheral artery disease refractory to noninvasive management. We examined the contemporary patterns of care, regional variation, and outcomes of ambulatory LE-EVI in the United States. The National Ambulatory Surgery Sample was analyzed to identify 266,563 records with peripheral artery disease and LE-EVI between January 1, 2016 and December 31, 2017. The mean age of the study cohort was 68.9 years and 40.5% were women. The majority of the endovascular interventions were performed at large (58.1%), urban teaching (64.1%), private not-for-profit (76.8%) centers, and the southern region accounted for most cases (43%). Periprocedural major adverse renal and cardiovascular events and other complications were 0.5% and 3.3%, respectively. Most patients (97.6%) were discharged home after the procedure. Age, female gender, uncontrolled hypertension, ischemic heart disease, heart failure, arrhythmia, chronic kidney disease, malnutrition, non-Medicare insurance, private for-profit, urban teaching facilities, and southern and midwest regions were associated with higher odds of major adverse renal and cardiovascular events. The mean charges per patient encounter were $56,500, with significant differences across various patient and facility characteristics. In conclusion, our study demonstrates the use, patterns of care, financial aspect, and overall safety of ambulatory LE-EVIs in a real-world setting.
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Affiliation(s)
| | | | - Zachary M Gertz
- Pauley Heart Center, Virginia Commonwealth University, Richmond, Virginia
| | - Barbara D Lawson
- Pauley Heart Center, Virginia Commonwealth University, Richmond, Virginia
| | - Parul Chandrika
- Department of Medicine, East Carolina University, Greenville, North Carolina
| | - Rahul Gupta
- Division of Cardiology, Lehigh Valley Health Network, Allentown, Pennsylvania
| | | | - Jonathan G Sung
- Division of Cardiology, Brigham & Women's Hospital, Boston, Massachusetts
| | - Nihar R Desai
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut
| | - George Vetrovec
- Pauley Heart Center, Virginia Commonwealth University, Richmond, Virginia
| | - Ajar Kochar
- Division of Cardiology, Brigham & Women's Hospital, Boston, Massachusetts
| | - Avirup Guha
- Division of Cardiology, Medical College of Georgia, Augusta, Georgia
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14
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Nabrdalik K, Kwiendacz H, Drożdż K, Irlik K, Hendel M, Wijata AM, Nalepa J, Correa E, Hajzler W, Janota O, Wójcik W, Gumprecht J, Lip GYH. Machine learning predicts cardiovascular events in patients with diabetes: The Silesia Diabetes-Heart Project. Curr Probl Cardiol 2023; 48:101694. [PMID: 36921649 DOI: 10.1016/j.cpcardiol.2023.101694] [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: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 03/15/2023]
Abstract
We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015 - 2020) were analyzed using ML. The occurrence of new CV events following discharge was collected in the follow-up time for up to 5 years and 9 months. An end-to-end ML technique which exploits the neighborhood component analysis for elaborating discriminative predictors, followed by a hybrid sampling/boosting classification algorithm, multiple logistic regression, or unsupervised hierarchical clustering was proposed. In 1735 patients with diabetes (53% female), there were 150 (8.65%) ones with a new CV event in the follow-up. Twelve most discriminative patients' parameters included coronary artery disease, heart failure, peripheral artery disease, stroke, diabetic foot disease, chronic kidney disease, eosinophil count, serum potassium level, and being treated with clopidogrel, heparin, proton pump inhibitor, and loop diuretic. Utilizing those variables resulted in the area under the receiver operating characteristic curve (AUC) ranging from 0.62 (95% Confidence Interval [CI] 0.56-0.68, p<0.01) to 0.72 (95%CI 0.66-0.77, p<0.01) across five non-overlapping test folds, whereas multiple logistic regression correctly determined 111/150 (74.00%) high-risk patients, and 989/1585 (62.40%) low-risk patients, resulting in 1100/1735 (63.40%) correctly classified patients (AUC: 0.72, 95%CI 0.66-0.77). ML algorithms can identify patients with diabetes at a high risk of new CV events based on a small number of interpretable and easy-to-obtain patients' parameters.
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Affiliation(s)
- Katarzyna Nabrdalik
- Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.
| | - Hanna Kwiendacz
- Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Karolina Drożdż
- Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Krzysztof Irlik
- Students' Scientific Association by the Department of Internal Medicine, Diabetology and Nephrology in Zabrze, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Mirela Hendel
- Students' Scientific Association by the Department of Internal Medicine, Diabetology and Nephrology in Zabrze, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Agata M Wijata
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
| | - Jakub Nalepa
- Faculty of Automatic Control, Electronics and Computer Science, Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland
| | - Elon Correa
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom
| | - Weronika Hajzler
- Doctoral School, Department of Pediatric Hematology and Oncology in Zabrze, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Oliwia Janota
- Doctoral School, Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Wiktoria Wójcik
- Students' Scientific Association by the Department of Internal Medicine, Diabetology and Nephrology in Zabrze, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Janusz Gumprecht
- Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
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15
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Lareyre F, Behrendt CA, Chaudhuri A, Lee R, Carrier M, Adam C, Lê CD, Raffort J. Applications of artificial intelligence for patients with peripheral artery disease. J Vasc Surg 2023; 77:650-658.e1. [PMID: 35921995 DOI: 10.1016/j.jvs.2022.07.160] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/06/2022] [Accepted: 07/19/2022] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field. METHODS We performed a narrative review based on studies reporting applications of AI in patients with PAD. The MEDLINE database was independently searched by two authors using a combination of keywords to identify studies published between January 1995 and December 2021. Three main fields of AI were investigated including natural language processing (NLP), computer vision and machine learning (ML). RESULTS NLP and ML brought new tools to improve the screening, the diagnosis and classification of the severity of PAD. ML was also used to develop predictive models to better assess the prognosis of patients and develop real-time prediction models to support clinical decision-making. Studies related to computer vision mainly aimed at creating automatic detection and characterization of arterial lesions based on Doppler ultrasound examination or computed tomography angiography. Such tools could help to improve screening programs, enhance diagnosis, facilitate presurgical planning, and improve clinical workflow. CONCLUSIONS AI offers various applications to support and likely improve the management of patients with PAD. Further research efforts are needed to validate such applications and investigate their accuracy and safety in large multinational cohorts before their implementation in daily clinical practice.
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Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France; Université Côte d'Azur, INSERM U1065, C3M, Nice, France.
| | - Christian-Alexander Behrendt
- Research Group GermanVasc, Department of Vascular Medicine, University Heart and Vascular Centre UKE Hamburg, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Arindam Chaudhuri
- Bedfordshire-Milton Keynes Vascular Centre, Bedfordshire Hospitals NHS Foundation Trust, Bedford, UK
| | - Regent Lee
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Marion Carrier
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Cédric Adam
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Cong Duy Lê
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France; Université Côte d'Azur, INSERM U1065, C3M, Nice, France
| | - Juliette Raffort
- Université Côte d'Azur, INSERM U1065, C3M, Nice, France; Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France; AI Institute 3IA Côte d'Azur, Université Côte d'Azur, Côte d'Azur, France
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16
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Khairuddin MZF, Hasikin K, Razak NAA, Mohshim SA, Ibrahim SS. Harnessing the Multimodal Data Integration and Deep Learning for Occupational Injury Severity Prediction. IEEE ACCESS 2023; 11:85284-85302. [DOI: 10.1109/access.2023.3304328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia
| | - Nasrul Anuar Abd Razak
- Department of Biomedical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia
| | - Siti Afifah Mohshim
- Medical Engineering Technology Section, British Malaysian Institute, Universiti Kuala Lumpur, Kuala Lumpur, Selangor, Malaysia
| | - Siti Salwa Ibrahim
- Negeri Sembilan State Health Department, Ministry of Health, Seremban, Negeri Sembilan, Malaysia
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17
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Zaizar-Fregoso SA, Lara-Esqueda A, Hernández-Suarez CM, Delgado-Enciso J, Garcia-Nevares A, Canseco-Avila LM, Guzman-Esquivel J, Rodriguez-Sanchez IP, Martinez-Fierro ML, Ceja-Espiritu G, Ochoa-Díaz-Lopez H, Espinoza-Gomez F, Sanchez-Diaz I, Delgado-Enciso I. Using Artificial Intelligence to Develop a Multivariate Model with a Machine Learning Model to Predict Complications in Mexican Diabetic Patients without Arterial Hypertension (National Nested Case-Control Study): Metformin and Elevated Normal Blood Pressure Are Risk Factors, and Obesity Is Protective. J Diabetes Res 2023; 2023:8898958. [PMID: 36846513 PMCID: PMC9949947 DOI: 10.1155/2023/8898958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
Diabetes mellitus is a disease with no cure that can cause complications and even death. Moreover, over time, it will lead to chronic complications. Predictive models have been used to identify people with a tendency to develop diabetes mellitus. At the same time, there is limited information regarding the chronic complications of patients with diabetes. Our study is aimed at creating a machine-learning model that will be able to identify the risk factors of a diabetic patient developing chronic complications such as amputations, myocardial infarction, stroke, nephropathy, and retinopathy. The design is a national nested case-control study with 63,776 patients and 215 predictors with four years of data. Using an XGBoost model, the prediction of chronic complications has an AUC of 84%, and the model has identified the risk factors for chronic complications in patients with diabetes. According to the analysis, the most crucial risk factors based on SHAP values (Shapley additive explanations) are continued management, metformin treatment, age between 68 and 104 years, nutrition consultation, and treatment adherence. But we highlight two exciting findings. The first is a reaffirmation that high blood pressure figures across patients with diabetes without hypertension become a significant risk factor at diastolic > 70 mmHg (OR: 1.095, 95% CI: 1.078-1.113) or systolic > 120 mmHg (OR: 1.147, 95% CI: 1.124-1.171). Furthermore, people with diabetes with a BMI > 32 (overall obesity) (OR: 0.816, 95% CI: 0.8-0.833) have a statistically significant protective factor, which the paradox of obesity may explain. In conclusion, the results we have obtained show that artificial intelligence is a powerful and feasible tool to use for this type of study. However, we suggest that more studies be conducted to verify and elaborate upon our findings.
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Affiliation(s)
| | - Agustin Lara-Esqueda
- Facultad de Psicología y Terapia de la Comunicación Humana de la Universidad Juárez del Estado Durango, Durango 81301, Mexico
| | | | - Josuel Delgado-Enciso
- Fundacion para la Etica Educacion e Investigacion del Cancer del Instituto Estatal de Cancerologia de Colima AC, Colima 28085, Mexico
| | | | - Luis M. Canseco-Avila
- Facultad de Ciencias Químicas Campus IV, Universidad Autónoma de Chiapas, Tapachula, 30700 Chiapas, Mexico
| | - Jose Guzman-Esquivel
- Instituto Mexicano del Seguro Social, Delegación Colima, Villa de Alvarez, 28983 Colima, Mexico
| | - Iram P. Rodriguez-Sanchez
- Facultad de Ciencias Biológicas, Universidad Autonoma de Nuevo Leon, San Nicolás de los Garza, 66455 Nuevo Leon, Mexico
| | | | | | - Hector Ochoa-Díaz-Lopez
- Departamento de Salud, El Colegio de La Frontera Sur, San Cristóbal de Las Casas, 29290 Chiapas, Mexico
| | | | - Iyari Sanchez-Diaz
- Subdirección de Prevención y Protección a la Salud, Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Ciudad de Mexico, 14070, Mexico
| | - Ivan Delgado-Enciso
- Facultad de Medicina, Universidad de Colima, Colima 28040, Mexico
- Instituto Estatal de Cancerología, Servicios de Salud del Estado de Colima, Colima 28085, Mexico
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18
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Application of machine learning in predicting the risk of postpartum depression: A systematic review. J Affect Disord 2022; 318:364-379. [PMID: 36055532 DOI: 10.1016/j.jad.2022.08.070] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/08/2022] [Accepted: 08/22/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Postpartum depression (PPD) presents a serious health problem among women and their families. Machine learning (ML) is a rapidly advancing field with increasing utility in predicting PPD risk. We aimed to synthesize and evaluate the quality of studies on application of ML techniques in predicting PPD risk. METHODS We conducted a systematic search of eight databases, identifying English and Chinese studies on ML techniques for predicting PPD risk and ML techniques with performance metrics. Quality of the studies involved was evaluated using the Prediction Model Risk of Bias Assessment Tool. RESULTS Seventeen studies involving 62 prediction models were included. Supervised learning was the main ML technique employed and the common ML models were support vector machine, random forest and logistic regression. Five studies (30 %) reported both internal and external validation. Two studies involved model translation, but none were tested clinically. All studies showed a high risk of bias, and more than half showed high application risk. LIMITATIONS Including Chinese articles slightly reduced the reproducibility of the review. Model performance was not quantitatively analyzed owing to inconsistent metrics and the absence of methods for correlation meta-analysis. CONCLUSIONS Researchers have paid more attention to model development than to validation, and few have focused on improvement and innovation. Models for predicting PPD risk continue to emerge. However, few have achieved the acceptable quality standards. Therefore, ML techniques for successfully predicting PPD risk are yet to be deployed in clinical environments.
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Li B, de Mestral C, Mamdani M, Al-Omran M. Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning. J Vasc Surg Cases Innov Tech 2022; 8:466-472. [PMID: 36016703 PMCID: PMC9396444 DOI: 10.1016/j.jvscit.2022.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Background Artificial intelligence (AI) and machine learning (ML) are rapidly advancing fields with increasing utility in health care. We conducted a survey to determine the perceptions of Canadian vascular surgeons toward AI/ML. Methods An online questionnaire was distributed to 162 members of the Canadian Society for Vascular Surgery. Self-reported knowledge, attitudes, and perceptions with respect to potential applications, limitations, and facilitators of AI/ML were assessed. Results Overall, 50 of the 162 Canadian vascular surgeons (31%) responded to the survey. Most respondents were aged 30 to 59 years (72%), male (80%), and White (67%) and practiced in academic settings (72%). One half of the participants reported that their knowledge of AI/ML was poor or very poor. Most were excited or very excited about AI/ML (66%) and were interested or very interested in learning more about the field (83.7%). The respondents believed that AI/ML would be useful or very useful for diagnosis (62%), prognosis (72%), patient selection (56%), image analysis (64%), intraoperative guidance (52%), research (88%), and education (80%). The limitations that the participants were most concerned about were errors leading to patient harm (42%), bias based on patient demographics (42%), and lack of clinician knowledge and skills in AI/ML (40%). Most were not concerned or were mildly concerned about job replacement (86%). The factors that were most important to encouraging clinicians to use AI/ML models were improvements in efficiency (88%), accurate predictions (84%), and ease of use (84%). The comments from respondents focused on the pressing need for the implementation of AI/ML in vascular surgery owing to the potential to improve care delivery. Conclusions Canadian vascular surgeons have positive views on AI/ML and believe this technology can be applied to multiple aspects of the specialty to improve patient care, research, and education. Current self-reported knowledge is poor, although interest was expressed in learning more about the field. The facilitators and barriers to the effective use of AI/ML identified in the present study can guide future development of these tools in vascular surgery.
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Ghanzouri I, Amal S, Ho V, Safarnejad L, Cabot J, Brown-Johnson CG, Leeper N, Asch S, Shah NH, Ross EG. Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records. Sci Rep 2022; 12:13364. [PMID: 35922657 PMCID: PMC9349186 DOI: 10.1038/s41598-022-17180-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/21/2022] [Indexed: 11/18/2022] Open
Abstract
Peripheral artery disease (PAD) is a common cardiovascular disorder that is frequently underdiagnosed, which can lead to poorer outcomes due to lower rates of medical optimization. We aimed to develop an automated tool to identify undiagnosed PAD and evaluate physician acceptance of a dashboard representation of risk assessment. Data were derived from electronic health records (EHR). We developed and compared traditional risk score models to novel machine learning models. For usability testing, primary and specialty care physicians were recruited and interviewed until thematic saturation. Data from 3168 patients with PAD and 16,863 controls were utilized. Results showed a deep learning model that utilized time engineered features outperformed random forest and traditional logistic regression models (average AUCs 0.96, 0.91 and 0.81, respectively), P < 0.0001. Of interviewed physicians, 75% were receptive to an EHR-based automated PAD model. Feedback emphasized workflow optimization, including integrating risk assessments directly into the EHR, using dashboard designs that minimize clicks, and providing risk assessments for clinically complex patients. In conclusion, we demonstrate that EHR-based machine learning models can accurately detect risk of PAD and that physicians are receptive to automated risk detection for PAD. Future research aims to prospectively validate model performance and impact on patient outcomes.
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Affiliation(s)
- I Ghanzouri
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - S Amal
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - V Ho
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - L Safarnejad
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - J Cabot
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - C G Brown-Johnson
- Department of Medicine, Primary Care and Population Health, Stanford, CA, USA
| | - N Leeper
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - S Asch
- Department of Medicine, Primary Care and Population Health, Stanford, CA, USA
| | - N H Shah
- Department of Medicine, Center for Biomedical Informatics Research, Stanford University School of Medicine, 780 Welch Road, CJ350, Stanford, CA, 94305, USA
| | - E G Ross
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA. .,Department of Medicine, Center for Biomedical Informatics Research, Stanford University School of Medicine, 780 Welch Road, CJ350, Stanford, CA, 94305, USA.
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21
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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: 0] [Impact Index Per Article: 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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22
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Li B, Feridooni T, Cuen-Ojeda C, Kishibe T, de Mestral C, Mamdani M, Al-Omran M. Machine learning in vascular surgery: a systematic review and critical appraisal. NPJ Digit Med 2022; 5:7. [PMID: 35046493 PMCID: PMC8770468 DOI: 10.1038/s41746-021-00552-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991-1996) to 118 (2016-2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61-1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, 149 College St, Toronto, ON, M5T 1P5, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada
| | - Tiam Feridooni
- Department of Surgery, University of Toronto, 149 College St, Toronto, ON, M5T 1P5, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada
| | - Cesar Cuen-Ojeda
- Department of Surgery, University of Toronto, 149 College St, Toronto, ON, M5T 1P5, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada
| | - Teruko Kishibe
- Health Sciences Library, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, 149 College St, Toronto, ON, M5T 1P5, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON, M5T 3M7, Canada
| | - Muhammad Mamdani
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON, M5T 3M7, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College St, Toronto, ON, M5S 3M2, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, 149 College St, Toronto, ON, M5T 1P5, Canada.
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada.
- Institute of Medical Science, University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
- Department of Surgery, King Saud University, ZIP 4545, Riyadh, 11451, Kingdom of Saudi Arabia.
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23
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Zarkowsky DS, Stonko DP. Artificial intelligence's role in vascular surgery decision-making. Semin Vasc Surg 2021; 34:260-267. [PMID: 34911632 DOI: 10.1053/j.semvascsurg.2021.10.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/17/2021] [Accepted: 10/18/2021] [Indexed: 12/28/2022]
Abstract
Artificial intelligence (AI) is the next great advance informing medical science. Several disciplines, including vascular surgery, use AI-based decision-making tools to improve clinical performance. Although applied widely, AI functions best when confronted with voluminous, accurate data. Consistent, predictable analytic technique selection also challenges researchers. This article contextualizes AI analyses within evidence-based medicine, focusing on "big data" and health services research, as well as discussing opportunities to improve data collection and realize AI's promise.
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Affiliation(s)
- Devin S Zarkowsky
- Division of Vascular Surgery and Endovascular Therapy, University of Colorado School of Medicine, 12615 E 17(th) Place, AO1, Aurora, CO, 80045.
| | - David P Stonko
- Department of Surgery, The Johns Hopkins Hospital, Baltimore, MD
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24
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Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management. J Pers Med 2021; 11:jpm11121280. [PMID: 34945749 PMCID: PMC8705683 DOI: 10.3390/jpm11121280] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/22/2021] [Accepted: 11/24/2021] [Indexed: 12/14/2022] Open
Abstract
Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care.
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25
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Sarraju A, Ward A, Chung S, Li J, Scheinker D, Rodríguez F. Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients. Open Heart 2021; 8:openhrt-2021-001802. [PMID: 34667093 PMCID: PMC8527119 DOI: 10.1136/openhrt-2021-001802] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 10/05/2021] [Indexed: 12/15/2022] Open
Abstract
Objectives Identifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health record (EHR)-based machine-learning (ML) models can improve CVD risk stratification compared with a secondary prevention risk score developed from randomised clinical trials (Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention, TRS 2°P). Methods We identified patients with CVD in a large health system, including atherosclerotic CVD (ASCVD), split into 80% training and 20% test sets. A rich set of EHR patient features was extracted. ML models were trained to estimate 5-year CVD event risk (random forests (RF), gradient-boosted machines (GBM), extreme gradient-boosted models (XGBoost), logistic regression with an L2 penalty and L1 penalty (Lasso)). ML models and TRS 2°P were evaluated by the area under the receiver operating characteristic curve (AUC). Results The cohort included 32 192 patients (median age 74 years, with 46% female, 63% non-Hispanic white and 12% Asian patients and 23 475 patients with ASCVD). There were 4010 events over 5 years of follow-up. ML models demonstrated good overall performance; XGBoost demonstrated AUC 0.70 (95% CI 0.68 to 0.71) in the full CVD cohort and AUC 0.71 (95% CI 0.69 to 0.73) in patients with ASCVD, with comparable performance by GBM, RF and Lasso. TRS 2°P performed poorly in all CVD (AUC 0.51, 95% CI 0.50 to 0.53) and ASCVD (AUC 0.50, 95% CI 0.48 to 0.52) patients. ML identified nontraditional predictive variables including education level and primary care visits. Conclusions In a multiethnic real-world population, EHR-based ML approaches significantly improved CVD risk stratification for secondary prevention.
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Affiliation(s)
- Ashish Sarraju
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Andrew Ward
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Sukyung Chung
- Palo Alto Medical Foundation Research Institute, Palo Alto, California, USA
| | - Jiang Li
- Palo Alto Medical Foundation Research Institute, Palo Alto, California, USA
| | - David Scheinker
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA.,Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA
| | - Fàtima Rodríguez
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, USA
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26
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Bikia V, Fong T, Climie RE, Bruno RM, Hametner B, Mayer C, Terentes-Printzios D, Charlton PH. Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:676-690. [PMID: 35316972 PMCID: PMC7612526 DOI: 10.1093/ehjdh/ztab089] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology (LHTC), Swiss Federal Institute of Technology, CH-1015 Lausanne, Vaud, Switzerland
| | - Terence Fong
- Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, Victoria, 3004 Australia,Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Grattan Street, Parkville, Victoria, 3010 Australia
| | - Rachel E Climie
- Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, Victoria, 3004 Australia,Université de Paris, INSERM U970, Paris Cardiovascular Research Centre, Integrative Epidemiology of Cardiovascular Disease, Paris, France
| | - Rosa-Maria Bruno
- Université de Paris, INSERM U970, Paris Cardiovascular Research Centre, Integrative Epidemiology of Cardiovascular Disease, Paris, France
| | - Bernhard Hametner
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
| | - Christopher Mayer
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
| | - Dimitrios Terentes-Printzios
- First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, 114 Vasilissis Sofias Avenue, 11527, Athens, Greece
| | - Peter H Charlton
- Department of Public Health and Primary Care, Strangeways Research Laboratory, 2 Worts' Causeway, Cambridge, CB1 8RN, UK,Research Centre for Biomedical Engineering, City, University of London, Northampton Square, London, EC1V 0HB, UK,Corresponding author.
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27
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Flores AM, Demsas F, Leeper NJ, Ross EG. Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes. Circ Res 2021; 128:1833-1850. [PMID: 34110911 PMCID: PMC8285054 DOI: 10.1161/circresaha.121.318224] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.
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Affiliation(s)
- Alyssa M Flores
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
| | - Falen Demsas
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
| | - Nicholas J Leeper
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
- Department of Medicine, Division of Cardiovascular Medicine (N.J.L.), Stanford University School of Medicine, CA
- Stanford Cardiovascular Institute, CA (N.J.L., E.G.R.)
| | - Elsie Gyang Ross
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, CA. (E.G.R.)
- Stanford Cardiovascular Institute, CA (N.J.L., E.G.R.)
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28
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Saenz-Pipaon G, Martinez-Aguilar E, Orbe J, González Miqueo A, Fernandez-Alonso L, Paramo JA, Roncal C. The Role of Circulating Biomarkers in Peripheral Arterial Disease. Int J Mol Sci 2021; 22:ijms22073601. [PMID: 33808453 PMCID: PMC8036489 DOI: 10.3390/ijms22073601] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/25/2021] [Accepted: 03/26/2021] [Indexed: 12/18/2022] Open
Abstract
Peripheral arterial disease (PAD) of the lower extremities is a chronic illness predominantly of atherosclerotic aetiology, associated to traditional cardiovascular (CV) risk factors. It is one of the most prevalent CV conditions worldwide in subjects >65 years, estimated to increase greatly with the aging of the population, becoming a severe socioeconomic problem in the future. The narrowing and thrombotic occlusion of the lower limb arteries impairs the walking function as the disease progresses, increasing the risk of CV events (myocardial infarction and stroke), amputation and death. Despite its poor prognosis, PAD patients are scarcely identified until the disease is advanced, highlighting the need for reliable biomarkers for PAD patient stratification, that might also contribute to define more personalized medical treatments. In this review, we will discuss the usefulness of inflammatory molecules, matrix metalloproteinases (MMPs), and cardiac damage markers, as well as novel components of the liquid biopsy, extracellular vesicles (EVs), and non-coding RNAs for lower limb PAD identification, stratification, and outcome assessment. We will also explore the potential of machine learning methods to build prediction models to refine PAD assessment. In this line, the usefulness of multimarker approaches to evaluate this complex multifactorial disease will be also discussed.
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Affiliation(s)
- Goren Saenz-Pipaon
- Laboratory of Atherothrombosis, Program of Cardiovascular Diseases, Cima Universidad de Navarra, 31008 Pamplona, Spain; (G.S.-P.); (J.O.); (J.A.P.)
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, 31008 Pamplona, Spain; (E.M.-A.); (A.G.M.); (L.F.-A.)
| | - Esther Martinez-Aguilar
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, 31008 Pamplona, Spain; (E.M.-A.); (A.G.M.); (L.F.-A.)
- Departamento de Angiología y Cirugía Vascular, Complejo Hospitalario de Navarra, 31008 Pamplona, Spain
| | - Josune Orbe
- Laboratory of Atherothrombosis, Program of Cardiovascular Diseases, Cima Universidad de Navarra, 31008 Pamplona, Spain; (G.S.-P.); (J.O.); (J.A.P.)
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, 31008 Pamplona, Spain; (E.M.-A.); (A.G.M.); (L.F.-A.)
- CIBERCV, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Arantxa González Miqueo
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, 31008 Pamplona, Spain; (E.M.-A.); (A.G.M.); (L.F.-A.)
- CIBERCV, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Laboratory of Heart Failure, Program of Cardiovascular Diseases, Cima Universidad de Navarra, 31008 Pamplona, Spain
| | - Leopoldo Fernandez-Alonso
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, 31008 Pamplona, Spain; (E.M.-A.); (A.G.M.); (L.F.-A.)
- Departamento de Angiología y Cirugía Vascular, Complejo Hospitalario de Navarra, 31008 Pamplona, Spain
| | - Jose Antonio Paramo
- Laboratory of Atherothrombosis, Program of Cardiovascular Diseases, Cima Universidad de Navarra, 31008 Pamplona, Spain; (G.S.-P.); (J.O.); (J.A.P.)
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, 31008 Pamplona, Spain; (E.M.-A.); (A.G.M.); (L.F.-A.)
- CIBERCV, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Hematology Service, Clínica Universidad de Navarra, 31008 Pamplona, Spain
| | - Carmen Roncal
- Laboratory of Atherothrombosis, Program of Cardiovascular Diseases, Cima Universidad de Navarra, 31008 Pamplona, Spain; (G.S.-P.); (J.O.); (J.A.P.)
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, 31008 Pamplona, Spain; (E.M.-A.); (A.G.M.); (L.F.-A.)
- CIBERCV, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Correspondence: ; Tel.: +34-948194700
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Lee S, Doktorchik C, Martin EA, D'Souza AG, Eastwood C, Shaheen AA, Naugler C, Lee J, Quan H. Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review. JMIR Med Inform 2021; 9:e23934. [PMID: 33522976 PMCID: PMC7884219 DOI: 10.2196/23934] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/20/2020] [Accepted: 12/05/2020] [Indexed: 12/16/2022] Open
Abstract
Background Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. Objective This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. Results A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. Conclusions Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.
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Affiliation(s)
- Seungwon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Chelsea Doktorchik
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elliot Asher Martin
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Adam Giles D'Souza
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Cathy Eastwood
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Abdel Aziz Shaheen
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Christopher Naugler
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Joon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Hude Quan
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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30
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Abstract
PURPOSE OF REVIEW Healthcare has already been impacted by the fourth industrial revolution exemplified by tip of spear technology, such as artificial intelligence and quantum computing. Yet, there is much to be accomplished as systems remain suboptimal, and full interoperability of digital records is not realized. Given the footprint of technology in healthcare, the field of clinical immunology will certainly see improvements related to these tools. RECENT FINDINGS Biomedical informatics spans the gamut of technology in biomedicine. Within this distinct field, advances are being made, which allow for engineering of systems to automate disease detection, create computable phenotypes and improve record portability. Within clinical immunology, technologies are emerging along these lines and are expected to continue. SUMMARY This review highlights advancements in digital health including learning health systems, electronic phenotyping, artificial intelligence and use of registries. Technological advancements for improving diagnosis and care of patients with primary immunodeficiency diseases is also highlighted.
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31
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Qiu J, Li P, Dong M, Xin X, Tan J. Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method. J Transl Med 2019; 17:317. [PMID: 31547822 PMCID: PMC6757430 DOI: 10.1186/s12967-019-2062-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 09/06/2019] [Indexed: 01/08/2023] Open
Abstract
Background Infertility has become a global health issue with the number of couples seeking in vitro fertilization (IVF) worldwide continuing to rise. Some couples remain childless after several IVF cycles. Women undergoing IVF face greater risks and financial burden. A prediction model to predict the live birth chance prior to the first IVF treatment is needed in clinical practice for patients counselling and shaping expectations. Methods Clinical data of 7188 women who underwent their first IVF treatment at the Reproductive Medical Center of Shengjing Hospital of China Medical University during 2014–2018 were retrospectively collected. Machine-learning based models were developed on 70% of the dataset using pre-treatment variables, and prediction performances were evaluated on the remaining 30% using receiver operating characteristic (ROC) analysis and calibration plot. Nested cross-validation was used to make an unbiased estimate of the generalization performance of the machine learning algorithms. Results The XGBoost model achieved an area under the ROC curve of 0.73 on the validation dataset and showed the best calibration compared with other machine learning algorithms. Nested cross-validation resulted in an average accuracy score of 0.70 ± 0.003 for the XGBoost model. Conclusions A prediction model based on XGBoost was developed using age, AMH, BMI, duration of infertility, previous live birth, previous miscarriage, previous abortion and type of infertility as predictors. This study might be a promising step to provide personalized estimates of the cumulative live birth chance of the first complete IVF cycle before treatment.
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Affiliation(s)
- Jiahui Qiu
- Reproductive Medical Center of Gynecology and Obstetrics Department, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.,Key Laboratory of Reproductive Dysfunction Diseases and Fertility Remodeling of Liaoning Province, Shenyang, Liaoning, China
| | - Pingping Li
- Reproductive Medical Center of Gynecology and Obstetrics Department, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.,Key Laboratory of Reproductive Dysfunction Diseases and Fertility Remodeling of Liaoning Province, Shenyang, Liaoning, China
| | - Meng Dong
- Reproductive Medical Center of Gynecology and Obstetrics Department, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.,Key Laboratory of Reproductive Dysfunction Diseases and Fertility Remodeling of Liaoning Province, Shenyang, Liaoning, China
| | - Xing Xin
- Reproductive Medical Center of Gynecology and Obstetrics Department, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.,Key Laboratory of Reproductive Dysfunction Diseases and Fertility Remodeling of Liaoning Province, Shenyang, Liaoning, China
| | - Jichun Tan
- Reproductive Medical Center of Gynecology and Obstetrics Department, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China. .,Key Laboratory of Reproductive Dysfunction Diseases and Fertility Remodeling of Liaoning Province, Shenyang, Liaoning, China.
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