1
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Khalid SI, Massaad E, Roy JM, Thomson K, Mirpuri P, Kiapour A, Shin JH. An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery: A Systematic Review. Neurosurgery 2024:00006123-990000000-01255. [PMID: 38940578 DOI: 10.1227/neu.0000000000003074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 05/10/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Significant evidence has indicated that the reporting quality of novel predictive models is poor because of confounding by small data sets, inappropriate statistical analyses, and a lack of validation and reproducibility. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement was developed to increase the generalizability of predictive models. This study evaluated the quality of predictive models reported in neurosurgical literature through their compliance with the TRIPOD guidelines. METHODS Articles reporting prediction models published in the top 5 neurosurgery journals by SCImago Journal Rank-2 (Neurosurgery, Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of NeuroInterventional Surgery, and Journal of Neurology, Neurosurgery, and Psychiatry) between January 1st, 2018, and January 1st, 2023, were identified through a PubMed search strategy that combined terms related to machine learning and prediction modeling. These original research articles were analyzed against the TRIPOD criteria. RESULTS A total of 110 articles were assessed with the TRIPOD checklist. The median compliance was 57.4% (IQR: 50.0%-66.7%). Models using machine learning-based models exhibited lower compliance on average compared with conventional learning-based models (57.1%, 50.0%-66.7% vs 68.1%, 50.2%-68.1%, P = .472). Among the TRIPOD criteria, the lowest compliance was observed in blinding the assessment of predictors and outcomes (n = 7, 12.7% and n = 10, 16.9%, respectively), including an informative title (n = 17, 15.6%) and reporting model performance measures such as confidence intervals (n = 27, 24.8%). Few studies provided sufficient information to allow for the external validation of results (n = 26, 25.7%). CONCLUSION Published predictive models in neurosurgery commonly fall short of meeting the established guidelines laid out by TRIPOD for optimal development, validation, and reporting. This lack of compliance may represent the minor extent to which these models have been subjected to external validation or adopted into routine clinical practice in neurosurgery.
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Affiliation(s)
- Syed I Khalid
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Elie Massaad
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Joanna Mary Roy
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Kyle Thomson
- Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois, USA
| | - Pranav Mirpuri
- Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois, USA
| | - Ali Kiapour
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - John H Shin
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
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2
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Leinweber ME, Schmandra T, Karl T, Torsello G, Böckler D, Walensi M, Geisbuesch P, Schmitz-Rixen T, Jung G, Hofmann AG. Deciphering Popliteal Artery Aneurysm Patient Diversity: Insights From a Cluster Analysis of the POPART Registry. J Am Heart Assoc 2024; 13:e034429. [PMID: 38879461 DOI: 10.1161/jaha.124.034429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 05/23/2024] [Indexed: 06/19/2024]
Abstract
BACKGROUND Popliteal artery aneurysms (PAAs) are the most common peripheral aneurysm. However, due to its rarity, the cumulative body of evidence regarding patient patterns, treatment strategies, and perioperative outcomes is limited. This analysis aims to investigate distinct phenotypical patient profiles and associated treatment and outcomes in patients with a PAA by performing an unsupervised clustering analysis of the POPART (Practice of Popliteal Artery Aneurysm Repair and Therapy) registry. METHODS AND RESULTS A cluster analysis (using k-means clustering) was performed on data obtained from the multicenter POPART registry (42 centers from Germany and Luxembourg). Sensitivity analyses were conducted to explore validity and stability. Using 2 clusters, patients were primarily separated by the absence or presence of clinical symptoms. Within the cluster of symptomatic patients, the main difference between patients with acute limb ischemia presentation and nonemergency symptomatic patients was PAA diameter. When using 6 clusters, patients were primarily grouped by comorbidities, with patients with acute limb ischemia forming a separate cluster. Despite markedly different risk profiles, perioperative complication rates appeared to be positively associated with the proportion of emergency patients. However, clusters with a higher proportion of patients having any symptoms before treatment experienced a lower rate of perioperative complications. CONCLUSIONS The conducted analyses revealed both an insight to the public health reality of PAA care as well as patients with PAA at elevated risk for adverse outcomes. This analysis suggests that the preoperative clinic is a far more crucial adjunct to the patient's preoperative risk assessment than the patient's epidemiological profile by itself.
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Affiliation(s)
- Maria Elisabeth Leinweber
- FIFOS-Forum for Integrative Research and Systems Biology Vienna Austria
- Department of Vascular and Endovascular Surgery, Klinik Ottakring Vienna Austria
| | - Thomas Schmandra
- Department of Vascular Surgery, Sana Klinikum Offenbach Offenbach Germany
| | - Thomas Karl
- Department of Vascular and Endovascular Surgery, Klinikum am Plattenwald, SLK-Kliniken Heilbronn GmbH Bad Friedrichshall Germany
| | - Giovanni Torsello
- Department for Vascular Surgery Franziskus Hospital Münster Münster Germany
| | - Dittmar Böckler
- Department of Vascular and Endovascular Surgery University Hospital Heidelberg Heidelberg Germany
| | - Mikolaj Walensi
- Department of Vascular Surgery and Phlebology, Contilia Heart and Vascular Center Essen Germany
| | - Phillip Geisbuesch
- Department of Vascular and Endovascular Surgery, Klinikum Stuttgart Stuttgart Germany
| | | | - Georg Jung
- Department of Vascular and Endovascular Surgery, Luzerner Kantonsspital Lucern Switzerland
| | - Amun Georg Hofmann
- FIFOS-Forum for Integrative Research and Systems Biology Vienna Austria
- Department of Vascular and Endovascular Surgery, Klinik Ottakring Vienna Austria
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3
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Raj A, Allababidi A, Kayed H, Gerken ALH, Müller J, Schoenberg SO, Zöllner FG, Rink JS. Streamlining Acute Abdominal Aortic Dissection Management-An AI-based CT Imaging Workflow. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01164-0. [PMID: 38864947 DOI: 10.1007/s10278-024-01164-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/24/2024] [Accepted: 06/04/2024] [Indexed: 06/13/2024]
Abstract
Life-threatening acute aortic dissection (AD) demands timely diagnosis for effective intervention. To streamline intrahospital workflows, automated detection of AD in abdominal computed tomography (CT) scans seems useful to assist humans. We aimed at creating a robust convolutional neural network (CNN)-based pipeline capable of real-time screening for signs of abdominal AD in CT. In this retrospective study, abdominal CT data from AD patients presenting with AD and from non-AD patients were collected (n 195, AD cases 94, mean age 65.9 years, female ratio 35.8%). A CNN-based algorithm was developed with the goal of enabling a robust, automated, and highly sensitive detection of abdominal AD. Two sets from internal (n = 32, AD cases 16) and external sources (n = 1189, AD cases 100) were procured for validation. The abdominal region was extracted, followed by the automatic isolation of the aorta region of interest (ROI) and highlighting of the membrane via edge extraction, followed by classification of the aortic ROI as dissected/healthy. A fivefold cross-validation was employed on the internal set, and an ensemble of the 5 trained models was used to predict the internal and external validation set. Evaluation metrics included receiver operating characteristic curve (AUC) and balanced accuracy. The AUC, balanced accuracy, and sensitivity scores of the internal dataset were 0.932 (CI 0.891-0.963), 0.860, and 0.885, respectively. For the internal validation dataset, the AUC, balanced accuracy, and sensitivity scores were 0.887 (CI 0.732-0.988), 0.781, and 0.875, respectively. Furthermore, for the external validation dataset, AUC, balanced accuracy, and sensitivity scores were 0.993 (CI 0.918-0.994), 0.933, and 1.000, respectively. The proposed automated pipeline could assist humans in expediting acute aortic dissection management when integrated into clinical workflows.
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Affiliation(s)
- Anish Raj
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany.
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany.
| | - Ahmad Allababidi
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Hany Kayed
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Andreas L H Gerken
- Department of Surgery, Medical Faculty Mannheim, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Julia Müller
- Mediri GmbH, Eppelheimer Straße 13, D-69115, Heidelberg, Germany
| | - Stefan O Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Johann S Rink
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
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4
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Khan H, Zamzam A, Shaikh F, Saposnik G, Mamdani M, Qadura M. Predicting Major Adverse Carotid Cerebrovascular Events in Patients with Carotid Stenosis: Integrating a Panel of Plasma Protein Biomarkers and Clinical Features-A Pilot Study. J Clin Med 2024; 13:3382. [PMID: 38929911 PMCID: PMC11203750 DOI: 10.3390/jcm13123382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/06/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
Background: Carotid stenosis (CS) is an atherosclerotic disease of the carotid artery that can lead to devastating cardiovascular outcomes such as stroke, disability, and death. The currently available treatment for CS is medical management through risk reduction, including control of hypertension, diabetes, and/or hypercholesterolemia. Surgical interventions are currently suggested for patients with symptomatic disease with stenosis >50%, where patients have suffered from a carotid-related event such as a cerebrovascular accident, or asymptomatic disease with stenosis >60% if the long-term risk of death is <3%. There is a lack of current plasma protein biomarkers available to predict patients at risk of such adverse events. Methods: In this study, we investigated several growth factors and biomarkers of inflammation as potential biomarkers for adverse CS events such as stroke, need for surgical intervention, myocardial infarction, and cardiovascular-related death. In this pilot study, we use a support vector machine (SVM), random forest models, and the following four significantly elevated biomarkers: C-X-C Motif Chemokine Ligand 6 (CXCL6); Interleukin-2 (IL-2); Galectin-9; and angiopoietin-like protein (ANGPTL4). Results: Our SVM model best predicted carotid cerebrovascular events with an area under the curve (AUC) of >0.8 and an accuracy of 0.88, demonstrating strong prognostic capability. Conclusions: Our SVM model may be used for risk stratification of patients with CS to determine those who may benefit from surgical intervention.
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Affiliation(s)
- Hamzah Khan
- Division of Vascular Surgery, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada; (H.K.); (A.Z.); (F.S.)
- Department of Surgery, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Abdelrahman Zamzam
- Division of Vascular Surgery, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada; (H.K.); (A.Z.); (F.S.)
- Department of Surgery, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Farah Shaikh
- Division of Vascular Surgery, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada; (H.K.); (A.Z.); (F.S.)
- Department of Surgery, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Gustavo Saposnik
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital—Unity Health Toronto, Toronto, ON M5B 1W8, Canada; (G.S.); (M.M.)
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Muhammad Mamdani
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital—Unity Health Toronto, Toronto, ON M5B 1W8, Canada; (G.S.); (M.M.)
| | - Mohammad Qadura
- Division of Vascular Surgery, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada; (H.K.); (A.Z.); (F.S.)
- Department of Surgery, University of Toronto, Toronto, ON M5T 1P5, Canada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital—Unity Health Toronto, Toronto, ON M5B 1W8, Canada; (G.S.); (M.M.)
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5
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Khan H, Abu-Raisi M, Feasson M, Shaikh F, Saposnik G, Mamdani M, Qadura M. Current Prognostic Biomarkers for Abdominal Aortic Aneurysm: A Comprehensive Scoping Review of the Literature. Biomolecules 2024; 14:661. [PMID: 38927064 PMCID: PMC11201473 DOI: 10.3390/biom14060661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Abdominal aortic aneurysm (AAA) is a progressive dilatation of the aorta that can lead to aortic rupture. The pathophysiology of the disease is not well characterized but is known to be caused by the general breakdown of the extracellular matrix within the aortic wall. In this comprehensive literature review, all current research on proteins that have been investigated for their potential prognostic capabilities in patients with AAA was included. A total of 45 proteins were found to be potential prognostic biomarkers for AAA, predicting incidence of AAA, AAA rupture, AAA growth, endoleak, and post-surgical mortality. The 45 proteins fell into the following seven general categories based on their primary function: (1) cardiovascular health, (2) hemostasis, (3) transport proteins, (4) inflammation and immunity, (5) kidney function, (6) cellular structure, (7) and hormones and growth factors. This is the most up-to-date literature review on current prognostic markers for AAA and their functions. This review outlines the wide pathophysiological processes that are implicated in AAA disease progression.
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Affiliation(s)
- Hamzah Khan
- Division of Vascular Surgery, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada
| | - Mohamed Abu-Raisi
- Division of Vascular Surgery, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada
| | - Manon Feasson
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada
| | - Farah Shaikh
- Division of Vascular Surgery, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada
| | - Gustavo Saposnik
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Muhammad Mamdani
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada
| | - Mohammad Qadura
- Division of Vascular Surgery, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada
- Department of Surgery, University of Toronto, Toronto, ON M5T 1P5, Canada
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6
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Lavanga E, Dogbe L, Soucy J, Aziz F, Nguyen SL, Zil-E-Ali A, Aziz F. The state of vascular surgery education in the United States. Front Surg 2024; 11:1409688. [PMID: 38863463 PMCID: PMC11165300 DOI: 10.3389/fsurg.2024.1409688] [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: 03/30/2024] [Accepted: 05/20/2024] [Indexed: 06/13/2024] Open
Abstract
With the growing proportion of elderly population in the US and a relatively fixed supply of well-trained vascular surgeons, there is a serious concern that we will be facing a shortage of vascular surgery workforce in the near future. One of the main reasons why there is a shortage of vascular surgeons in the US is due to the fact that many students don't get exposed to this field throughout their student lives and a recent survey of medical students from a non-urban tertiary care academic institution showed that early exposure of the medical students to the surgical careers is correlated with an increased interest in the surgical field. This review of the state of vascular surgery education in the US at the undergraduate level describes in detail the importance of an early introduction to vascular surgery in the education curricula, the current state of the education, potential avenues to improve the exposure of students to the field of vascular surgery and the importance of this effort in matching the increasing need for vascular surgeons for an aging population which is likely to require dedicated care by vascular surgeons of the future. At the present time, the two pathways by the Accreditation Council for Graduate Medical Education (ACGME) to obtain dedicated vascular surgery training in the US include either enrolling in a two year clinical fellowship after completion of general surgery training or to match in a five year vascular surgery integrated residency program after successful completion of medical degree.
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Affiliation(s)
- Elizabeth Lavanga
- Department of Medical Education, Penn State College of Medicine, Hershey, PA, United States
| | - Leana Dogbe
- Department of Medical Education, Penn State College of Medicine, Hershey, PA, United States
| | - Jacob Soucy
- Department of Medical Education, Penn State College of Medicine, Hershey, PA, United States
| | - Faizaan Aziz
- Departmentof Biology, University of Michigan, Ann Arbor, MI, United States
| | - S. Lauren Nguyen
- Department of Medical Education, Penn State College of Medicine, Hershey, PA, United States
| | - Ahsan Zil-E-Ali
- Division of Vascular Surgery, Penn State Milton S. Hershey Medical Center, Heart and Vascular Institute, Hershey, PA, United States
| | - Faisal Aziz
- Division of Vascular Surgery, Penn State Milton S. Hershey Medical Center, Heart and Vascular Institute, Hershey, PA, United States
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7
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Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024; 30:1257-1268. [PMID: 38740998 DOI: 10.1038/s41591-024-02970-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.
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Affiliation(s)
- Chris Varghese
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Greg O'Grady
- Department of Surgery, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, CA, USA.
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8
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Kwong JCC, Wu J, Malik S, Khondker A, Gupta N, Bodnariuc N, Narayana K, Malik M, van der Kwast TH, Johnson AEW, Zlotta AR, Kulkarni GS. Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI. NPJ Digit Med 2024; 7:98. [PMID: 38637674 PMCID: PMC11026453 DOI: 10.1038/s41746-024-01088-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/29/2024] [Indexed: 04/20/2024] Open
Abstract
Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32-93) and median cohort size of 125 (IQR 93-309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.
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Affiliation(s)
- Jethro C C Kwong
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Jeremy Wu
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Shamir Malik
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Adree Khondker
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Naveen Gupta
- Georgetown University School of Medicine, Georgetown University, Washington, DC, USA
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Nicole Bodnariuc
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - Mikail Malik
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Theodorus H van der Kwast
- Laboratory Medicine Program, University Health Network, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
| | - Alistair E W Johnson
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Alexandre R Zlotta
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Urology, Department of Surgery, Mount Sinai Hospital, Sinai Health System, Toronto, ON, Canada
- Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Girish S Kulkarni
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.
- Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
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9
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Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024; 385:e078378. [PMID: 38626948 PMCID: PMC11019967 DOI: 10.1136/bmj-2023-078378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2024] [Indexed: 04/19/2024]
Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Paula Dhiman
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Science, Leiden University Medical Centre, Leiden, Netherlands
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Johannes B Reitsma
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Anne-Laure Boulesteix
- Institute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-University of Munich and Munich Centre of Machine Learning, Germany
| | - Jennifer Catherine Camaradou
- Patient representative, Health Data Research UK patient and public involvement and engagement group
- Patient representative, University of East Anglia, Faculty of Health Sciences, Norwich Research Park, Norwich, UK
| | - Leo Anthony Celi
- Beth Israel Deaconess Medical Center, Boston, MA, USA
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Alastair K Denniston
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | | | - Emily Lam
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Naomi Lee
- National Institute for Health and Care Excellence, London, UK
| | - Elizabeth W Loder
- The BMJ, London, UK
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lena Maier-Hein
- Department of Intelligent Medical Systems, German Cancer Research Centre, Heidelberg, Germany
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
- Wellcome Trust, London, UK
- Alan Turing Institute, London, UK
| | - Melissa D McCradden
- Department of Bioethics, Hospital for Sick Children Toronto, ON, Canada
- Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Johan Ordish
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Richard Parnell
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Sherri Rose
- Department of Health Policy and Center for Health Policy, Stanford University, Stanford, CA, USA
| | - Karandeep Singh
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Patricia Logullo
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
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Li B, Nassereldine R, Zamzam A, Syed MH, Mamdani M, Al-Omran M, Abdin R, Qadura M. Development and evaluation of a prediction model for peripheral artery disease-related major adverse limb events using novel biomarker data. J Vasc Surg 2024:S0741-5214(24)00951-0. [PMID: 38599293 DOI: 10.1016/j.jvs.2024.03.450] [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: 01/10/2024] [Revised: 03/26/2024] [Accepted: 03/31/2024] [Indexed: 04/12/2024]
Abstract
OBJECTIVE Prognostic tools for individuals with peripheral artery disease (PAD) remain limited. We developed prediction models for 3-year PAD-related major adverse limb events (MALE) using demographic, clinical, and biomarker data previously validated by our group. METHODS We performed a prognostic study using a prospectively recruited cohort of patients with PAD (n = 569). Demographic/clinical data were recorded including sex, age, comorbidities, previous procedures, and medications. Plasma concentrations of three biomarkers (N-terminal pro-B-type natriuretic peptide [NT-proBNP], fatty acid binding protein 3 [FABP3], and FABP4) were measured at baseline. The cohort was followed for 3 years. MALE was the primary outcome (composite of open/endovascular vascular intervention or major amputation). We trained three machine learning models with 10-fold cross-validation using demographic, clinical, and biomarker data (random forest, decision trees, and Extreme Gradient Boosting [XGBoost]) to predict 3-year MALE in patients. Area under the receiver operating characteristic curve (AUROC) was the primary model evaluation metric. RESULTS Three-year MALE was observed in 162 patients (29%). XGBoost was the top-performing predictive model for 3-year MALE, achieving the following performance metrics: AUROC = 0.88 (95% confidence interval [CI], 0.84-0.94); sensitivity, 88%; specificity, 84%; positive predictive value, 83%; and negative predictive value, 91% on test set data. On an independent validation cohort of patients with PAD, XGBoost attained an AUROC of 0.87 (95% CI, 0.82-0.90). The 10 most important predictors of 3-year MALE consisted of: (1) FABP3; (2) FABP4; (3) age; (4) NT-proBNP; (5) active smoking; (6) diabetes; (7) hypertension; (8) dyslipidemia; (9) coronary artery disease; and (10) sex. CONCLUSIONS We built robust machine learning algorithms that accurately predict 3-year MALE in patients with PAD using demographic, clinical, and novel biomarker data. Our algorithms can support risk stratification of patients with PAD for additional vascular evaluation and early aggressive medical management, thereby improving outcomes. Further validation of our models for clinical implementation is warranted.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada
| | - Rakan Nassereldine
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada; Division of Vascular Surgery, American University of Beirut Medical Center, Beirut, Lebanon
| | - Abdelrahman Zamzam
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada
| | - Muzammil H Syed
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada; Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada; Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; ICES, University of Toronto, Toronto, Ontario, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada; Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Rawand Abdin
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Mohammad Qadura
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada.
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11
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Pahwa B, Tayal A, Garg K. Contributions of Machine Learning in the Management of Stroke: A Bibliometric Analysis of the 50 Most Cited Articles. World Neurosurg 2024; 184:152-160. [PMID: 38244687 DOI: 10.1016/j.wneu.2024.01.059] [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: 07/26/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 01/22/2024]
Abstract
BACKGROUND Though currently considered a 'black box,' machine learning (ML) has a promising future to ameliorate the health-care burden of stroke which is the second leading cause of mortality worldwide. Through this study, we sought to review the most influential articles on the applications of ML in stroke. METHODS Web of Sciences database was searched, and a list of the top 50 most cited articles, assessing the application of ML in stroke, was prepared by 2 authors, independently. Subsequently, a detailed analysis was performed to characterize the most impactful studies. RESULTS The total number of citations to the top 50 articles were 2959 (range 35-243 citations) with a median of 47 citations. Highest number of articles were published in the journal Stroke and the United States was the major contributing country. The majority of the studies focused on the utilization of ML to improve stroke risk prediction, diagnosis, and outcome prediction. Statistical analysis revealed an insignificant association between the total and mean number of citations and the impact factor of the journal (P = 0.516 and 0.987, respectively). CONCLUSIONS Recent years have witnessed a surge in the application of ML in stroke, with an enhancement in interest and funding over the years. ML has revolutionized the management of stroke and continues to aid in the neurosurgical decision-making and care in stroke patients.
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Affiliation(s)
- Bhavya Pahwa
- University College of Medical Sciences and GTB Hospital, Delhi, India
| | - Anish Tayal
- Department of Neurosurgery, All India Institute of Medical Sciences, Delhi, India
| | - Kanwaljeet Garg
- Department of Neurosurgery, All India Institute of Medical Sciences, Delhi, India.
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12
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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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13
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Li B, Eisenberg N, Beaton D, Lee DS, Aljabri B, Wijeysundera DN, Rotstein OD, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Using machine learning to predict outcomes following suprainguinal bypass. J Vasc Surg 2024; 79:593-608.e8. [PMID: 37804954 DOI: 10.1016/j.jvs.2023.09.037] [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: 08/19/2023] [Revised: 09/20/2023] [Accepted: 09/24/2023] [Indexed: 10/09/2023]
Abstract
OBJECTIVE Suprainguinal bypass for peripheral artery disease (PAD) carries important surgical risks; however, outcome prediction tools remain limited. We developed machine learning (ML) algorithms that predict outcomes following suprainguinal bypass. METHODS The Vascular Quality Initiative database was used to identify patients who underwent suprainguinal bypass for PAD between 2003 and 2023. We identified 100 potential predictor variables from the index hospitalization (68 preoperative [demographic/clinical], 13 intraoperative [procedural], and 19 postoperative [in-hospital course/complications]). The primary outcomes were major adverse limb events (MALE; composite of untreated loss of patency, thrombectomy/thrombolysis, surgical revision, or major amputation) or death at 1 year following suprainguinal bypass. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). The best performing algorithm was further trained using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, symptom status, procedure type, prior intervention for PAD, concurrent interventions, and urgency. RESULTS Overall, 16,832 patients underwent suprainguinal bypass, and 3136 (18.6%) developed 1-year MALE or death. Patients with 1-year MALE or death were older (mean age, 64.9 vs 63.5 years; P < .001) with more comorbidities, had poorer functional status (65.7% vs 80.9% independent at baseline; P < .001), and were more likely to have chronic limb-threatening ischemia (67.4% vs 47.6%; P < .001) than those without an outcome. Despite being at higher cardiovascular risk, they were less likely to receive acetylsalicylic acid or statins preoperatively and at discharge. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.92 (95% confidence interval [CI], 0.91-0.93). In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). Our XGBoost model maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.93 (95% CI, 0.92-0.94) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.12 (preoperative), 0.11 (intraoperative), and 0.10 (postoperative). Of the top 10 predictors, nine were preoperative features including chronic limb-threatening ischemia, previous procedures, comorbidities, and functional status. Model performance remained robust on all subgroup analyses. CONCLUSIONS We developed ML models that accurately predict outcomes following suprainguinal bypass, performing better than logistic regression. Our algorithms have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes following suprainguinal bypass.
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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, 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
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Derek Beaton
- Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada
| | - Badr Aljabri
- Department of Surgery, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada
| | - Ori D Rotstein
- Department of Surgery, 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, Canada; Division of General Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada
| | - Muhammad Mamdani
- 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; Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health 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; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
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14
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Rusinovich Y, Rusinovich V, Buhayenka A, Liashko V, Sabanov A, Holstein DJF, Aldmour S, Doss M, Branzan D. Classification of anatomic patterns of peripheral artery disease with automated machine learning (AutoML). Vascular 2024:17085381241236571. [PMID: 38404043 DOI: 10.1177/17085381241236571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
AIM The aim of this study was to investigate the potential of novel automated machine learning (AutoML) in vascular medicine by developing a discriminative artificial intelligence (AI) model for the classification of anatomical patterns of peripheral artery disease (PAD). MATERIAL AND METHODS Random open-source angiograms of lower limbs were collected using a web-indexed search. An experienced researcher in vascular medicine labelled the angiograms according to the most applicable grade of femoropopliteal disease in the Global Limb Anatomic Staging System (GLASS). An AutoML model was trained using the Vertex AI (Google Cloud) platform to classify the angiograms according to the GLASS grade with a multi-label algorithm. Following deployment, we conducted a test using 25 random angiograms (five from each GLASS grade). Model tuning through incremental training by introducing new angiograms was executed to the limit of the allocated quota following the initial evaluation to determine its effect on the software's performance. RESULTS We collected 323 angiograms to create the AutoML model. Among these, 80 angiograms were labelled as grade 0 of femoropopliteal disease in GLASS, 114 as grade 1, 34 as grade 2, 25 as grade 3 and 70 as grade 4. After 4.5 h of training, the AI model was deployed. The AI self-assessed average precision was 0.77 (0 is minimal and 1 is maximal). During the testing phase, the AI model successfully determined the GLASS grade in 100% of the cases. The agreement with the researcher was almost perfect with the number of observed agreements being 22 (88%), Kappa = 0.85 (95% CI 0.69-1.0). The best results were achieved in predicting GLASS grade 0 and grade 4 (initial precision: 0.76 and 0.84). However, the AI model exhibited poorer results in classifying GLASS grade 3 (initial precision: 0.2) compared to other grades. Disagreements between the AI and the researcher were associated with the low resolution of the test images. Incremental training expanded the initial dataset by 23% to a total of 417 images, which improved the model's average precision by 11% to 0.86. CONCLUSION After a brief training period with a limited dataset, AutoML has demonstrated its potential in identifying and classifying the anatomical patterns of PAD, operating unhindered by the factors that can affect human analysts, such as fatigue or lack of experience. This technology bears the potential to revolutionize outcome prediction and standardize evidence-based revascularization strategies for patients with PAD, leveraging its adaptability and ability to continuously improve with additional data. The pursuit of further research in AutoML within the field of vascular medicine is both promising and warranted. However, it necessitates additional financial support to realize its full potential.
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Affiliation(s)
- Yury Rusinovich
- Department of Vascular Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Volha Rusinovich
- Institute of Hygiene and Environmental Medicine, University Hospital Leipzig, Germany
| | | | - Vitalii Liashko
- Department of Vascular Surgery, Charité University Hospital, Berlin, Germany
| | - Arsen Sabanov
- Department of Vascular Surgery, University Hospital Leipzig, Leipzig, Germany
| | - David J F Holstein
- Department of Vascular Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Samer Aldmour
- Department of Vascular Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Markus Doss
- Department of Vascular Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Daniela Branzan
- Department of Vascular Surgery, University Hospital Leipzig, Leipzig, Germany
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15
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Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, Li HJ, Li-Ling J, Hu W, Yin Z, Gong DX, Zhang GW. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med 2024; 22:56. [PMID: 38317226 PMCID: PMC10845808 DOI: 10.1186/s12916-024-03273-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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Affiliation(s)
- Yue Cai
- China Medical University, Shenyang, 110122, China
| | - Yu-Qing Cai
- China Medical University, Shenyang, 110122, China
| | - Li-Ying Tang
- China Medical University, Shenyang, 110122, China
| | - Yi-Han Wang
- China Medical University, Shenyang, 110122, China
| | - Mengchun Gong
- Digital Health China Co. Ltd, Beijing, 100089, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co. Ltd., Shenyang, 110001, China
- Enduring Medicine Smart Innovation Research Institute, Shenyang, 110001, China
| | - Jesse Li-Ling
- Institute of Genetic Medicine, School of Life Science, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610065, China
| | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, 610017, China
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, China.
| | - Da-Xin Gong
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
| | - Guang-Wei Zhang
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
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16
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Li A, Javidan AP, Namazi B, Madani A, Forbes TL. Development of an Artificial Intelligence Tool for Intraoperative Guidance During Endovascular Abdominal Aortic Aneurysm Repair. Ann Vasc Surg 2024; 99:96-104. [PMID: 37914075 DOI: 10.1016/j.avsg.2023.08.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/02/2023] [Accepted: 08/15/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND Adverse events during surgery can occur in part due to errors in visual perception and judgment. Deep learning is a branch of artificial intelligence (AI) that has shown promise in providing real-time intraoperative guidance. This study aims to train and test the performance of a deep learning model that can identify inappropriate landing zones during endovascular aneurysm repair (EVAR). METHODS A deep learning model was trained to identify a "No-Go" landing zone during EVAR, defined by coverage of the lowest renal artery by the stent graft. Fluoroscopic images from elective EVAR procedures performed at a single institution and from open-access sources were selected. Annotations of the "No-Go" zone were performed by trained annotators. A 10-fold cross-validation technique was used to evaluate the performance of the model against human annotations. Primary outcomes were intersection-over-union (IoU) and F1 score and secondary outcomes were pixel-wise accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS The AI model was trained using 369 images procured from 110 different patients/videos, including 18 patients/videos (44 images) from open-access sources. For the primary outcomes, IoU and F1 were 0.43 (standard deviation ± 0.29) and 0.53 (±0.32), respectively. For the secondary outcomes, accuracy, sensitivity, specificity, NPV, and PPV were 0.97 (±0.002), 0.51 (±0.34), 0.99 (±0.001). 0.99 (±0.002), and 0.62 (±0.34), respectively. CONCLUSIONS AI can effectively identify suboptimal areas of stent deployment during EVAR. Further directions include validating the model on datasets from other institutions and assessing its ability to predict optimal stent graft placement and clinical outcomes.
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Affiliation(s)
- Allen Li
- Faculty of Medicine & The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Arshia P Javidan
- Division of Vascular Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Babak Namazi
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX
| | - Amin Madani
- Department of Surgery, University Health Network & University of Toronto, Toronto, Ontario, Canada; Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, Ontario, Canada
| | - Thomas L Forbes
- Department of Surgery, University Health Network & University of Toronto, Toronto, Ontario, Canada.
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Bhandarkar S, Tsutsumi A, Schneider EB, Ong CS, Paredes L, Brackett A, Ahuja V. Emergent Applications of Machine Learning for Diagnosing and Managing Appendicitis: A State-of-the-Art Review. Surg Infect (Larchmt) 2024; 25:7-18. [PMID: 38150507 DOI: 10.1089/sur.2023.201] [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: 12/29/2023] Open
Abstract
Background: Appendicitis is an inflammatory condition that requires timely and effective intervention. Despite being one of the most common surgically treated diseases, the condition is difficult to diagnose because of atypical presentations. Ultrasound and computed tomography (CT) imaging improve the sensitivity and specificity of diagnoses, yet these tools bear the drawbacks of high operator dependency and radiation exposure, respectively. However, new artificial intelligence tools (such as machine learning) may be able to address these shortcomings. Methods: We conducted a state-of-the-art review to delineate the various use cases of emerging machine learning algorithms for diagnosing and managing appendicitis in recent literature. The query ("Appendectomy" OR "Appendicitis") AND ("Machine Learning" OR "Artificial Intelligence") was searched across three databases for publications ranging from 2012 to 2022. Upon filtering for duplicates and based on our predefined inclusion criteria, 39 relevant studies were identified. Results: The algorithms used in these studies performed with an average accuracy of 86% (18/39), a sensitivity of 81% (16/39), a specificity of 75% (16/39), and area under the receiver operating characteristic curves (AUROCs) of 0.82 (15/39) where reported. Based on accuracy alone, the optimal model was logistic regression in 18% of studies, an artificial neural network in 15%, a random forest in 13%, and a support vector machine in 10%. Conclusions: The identified studies suggest that machine learning may provide a novel solution for diagnosing appendicitis and preparing for patient-specific post-operative complications. However, further studies are warranted to assess the feasibility and advisability of implementing machine learning-based tools in clinical practice.
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Affiliation(s)
| | - Ayaka Tsutsumi
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Eric B Schneider
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Chin Siang Ong
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Lucero Paredes
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alexandria Brackett
- Harvey Cushing/John Hay Whitney Medical Library, Yale School of Medicine, New Haven, Connecticut, USA
| | - Vanita Ahuja
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
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Kong J, Zhang D. Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis. Front Neurol 2024; 14:1335851. [PMID: 38229595 PMCID: PMC10789857 DOI: 10.3389/fneur.2023.1335851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024] Open
Abstract
Background Pre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients. Methods We systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients. Results Sixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84-0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74-0.86) for predicting reperfusion in AIS. Conclusion Radiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.
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Affiliation(s)
- Jinfen Kong
- Department of Radiology, Yuhuan Second People's Hospital, Yuhuan, Taizhou, Zhejiang, China
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19
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Javidan A, Benipal H, Vi L, Li A, Lee Y, Feridooni T, Alaichi J, Naji F. Assessing the robustness of positive vascular surgery randomized controlled trials using their fragility index. J Vasc Surg 2024; 79:148-158.e3. [PMID: 37315910 DOI: 10.1016/j.jvs.2023.05.051] [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: 06/06/2021] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The fragility index (FI) measures the robustness of statistically significant findings in randomized controlled trials (RCTs) by quantifying the minimum number of event conversions required to reverse a dichotomous outcome's statistical significance. In vascular surgery, many clinical guidelines and critical decision-making points are informed by a handful of key RCTs, especially regarding open surgical versus endovascular treatment. The objective of this study is to evaluate the FI of RCTs with statistically significant primary outcomes that compared open vs endovascular surgery in vascular surgery. METHODS In this meta-epidemiological study and systematic review, MEDLINE, Embase, and CENTRAL were searched for RCTs comparing open versus endovascular treatments for abdominal aortic aneurysms, carotid artery stenosis, and peripheral arterial disease to December 2022. RCTs with statistically significant primary outcomes were included. Data screening and extraction were conducted in duplicate. The FI was calculated by adding an event to the group with the smaller number of events while subtracting a nonevent to the same group until Fisher's exact test produced a nonstatistically significant result. The primary outcome was the FI and proportion of outcomes where the loss to follow-up was greater than the FI. The secondary outcomes assessed the relationship of the FI to disease state, presence of commercial funding, and study design. RESULTS Overall, 5133 articles were captured in the initial search with 21 RCTs reporting 23 different primary outcomes being included in the final analysis. The median FI (first quartile, third quartile) was 3 (3, 20) with 16 outcomes (70%) reporting a loss to follow-up greater than its FI. Mann-Whitney U test revealed that commercially funded RCTs and composite outcomes had greater FIs (median, 20.0 [5.5, 24.5] vs median, 3.0 [2.0, 5.5], P = .035; median, 21 [8, 38] vs median, 3.0 [2.0, 8.5], P = .01, respectively). The FI did not vary between disease states (P = .285) or between index and follow-up trials (P = .147). There were significant correlations between the FI and P values (Pearson r = 0.90; 95% confidence interval, 0.77-0.96), and the number of events (r = 0.82; 95% confidence interval, 0.48-0.97). CONCLUSIONS A small number of event conversions (median, 3) are needed to alter the statistical significance of primary outcomes in vascular surgery RCTs evaluating open surgical and endovascular treatments. Most studies had loss to follow-up greater than its FI, which can call into question trial results, and commercially funded studies had a greater FI. The FI and these findings should be considered in future trial design in vascular surgery.
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Affiliation(s)
- Arshia Javidan
- Division of Vascular Surgery, University of Toronto, Toronto, Ontario, Canada.
| | - Harsukh Benipal
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Lisa Vi
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Allen Li
- Faculty of Medicine/The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Yung Lee
- Division of General Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada; Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA
| | - Tiam Feridooni
- Division of Vascular Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Jacob Alaichi
- Division of Vascular Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Faysal Naji
- Division of Vascular Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
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20
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Li B, Verma R, Beaton D, Tamim H, Hussain MA, Hoballah JJ, Lee DS, Wijeysundera DN, de Mestral C, Mamdani M, Al-Omran M. Predicting outcomes following open revascularization for aortoiliac occlusive disease using machine learning. J Vasc Surg 2023; 78:1449-1460.e7. [PMID: 37454952 DOI: 10.1016/j.jvs.2023.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/12/2023] [Accepted: 07/08/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVE Open surgical treatment options for aortoiliac occlusive disease carry significant perioperative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following open aortoiliac revascularization. METHODS The National Surgical Quality Improvement Program (NSQIP) targeted vascular database was used to identify patients who underwent open aortoiliac revascularization for atherosclerotic disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. The 30-day secondary outcomes were individual components of the primary outcome, major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death), individual components of MACE, wound complication, bleeding, other morbidity, non-home discharge, and unplanned readmission. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. Variable importance scores were calculated to determine the top 10 predictive features. Performance was assessed on subgroups based on age, sex, race, ethnicity, symptom status, procedure type, and urgency. RESULTS Overall, 9649 patients were included. The primary outcome of 30-day MALE or death occurred in 1021 patients (10.6%). Our best performing prediction model for 30-day MALE or death was XGBoost, achieving an AUROC of 0.95 (95% confidence interval [CI], 0.94-0.96). In comparison, logistic regression had an AUROC of 0.79 (95% CI, 0.77-0.81). For 30-day secondary outcomes, XGBoost achieved AUROCs between 0.87 and 0.97 (untreated loss of patency [0.95], major reintervention [0.88], major amputation [0.96], death [0.97], MACE [0.95], myocardial infarction [0.88], stroke [0.93], wound complication [0.94], bleeding [0.87], other morbidity [0.96], non-home discharge [0.90], and unplanned readmission [0.91]). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. The strongest predictive feature in our algorithm was chronic limb-threatening ischemia. Model performance remained robust on all subgroup analyses of specific demographic/clinical populations. CONCLUSIONS Our ML models accurately predict 30-day outcomes following open aortoiliac revascularization using preoperative data, performing better than logistic regression. They have potential for important utility in guiding risk-mitigation strategies for patients being considered for open aortoiliac revascularization to improve outcomes.
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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
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland
| | - Derek Beaton
- Department of Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Hani Tamim
- Faculty of Medicine, Clinical Research Institute, American University of Beirut Medical Center, Beirut, Lebanon; College of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia
| | - Mohamad A Hussain
- Division of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jamal J Hoballah
- Division of Vascular and Endovascular Surgery, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Charles de Mestral
- 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 Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Muhammad Mamdani
- 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; Department of Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Mohammed Al-Omran
- 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; College of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
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21
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Li B, Aljabri B, Verma R, Beaton D, Eisenberg N, Lee DS, Wijeysundera DN, Forbes TL, Rotstein OD, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Machine learning to predict outcomes following endovascular abdominal aortic aneurysm repair. Br J Surg 2023; 110:1840-1849. [PMID: 37710397 DOI: 10.1093/bjs/znad287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/27/2023] [Accepted: 08/27/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) carries important perioperative risks; however, there are no widely used outcome prediction tools. The aim of this study was to apply machine learning (ML) to develop automated algorithms that predict 1-year mortality following EVAR. METHODS The Vascular Quality Initiative database was used to identify patients who underwent elective EVAR for infrarenal AAA between 2003 and 2023. Input features included 47 preoperative demographic/clinical variables. The primary outcome was 1-year all-cause mortality. Data were split into training (70 per cent) and test (30 per cent) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features with logistic regression as the baseline comparator. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. RESULTS Some 63 655 patients were included. One-year mortality occurred in 3122 (4.9 per cent) patients. The best performing prediction model for 1-year mortality was XGBoost, achieving an AUROC (95 per cent c.i.) of 0.96 (0.95-0.97). Comparatively, logistic regression had an AUROC (95 per cent c.i.) of 0.69 (0.68-0.71). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.04. The top 3 predictive features in the algorithm were 1) unfit for open AAA repair, 2) functional status, and 3) preoperative dialysis. CONCLUSIONS In this data set, machine learning was able to predict 1-year mortality following EVAR using preoperative data and outperformed standard logistic regression models.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada
| | - Badr Aljabri
- Department of Surgery, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland
| | - Derek Beaton
- Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- ICES, University of Toronto, Toronto, Ontario, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- ICES, University of Toronto, Toronto, Ontario, Canada
- Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Thomas L Forbes
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Ori D Rotstein
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Division of General Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- ICES, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada
- Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- ICES, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia
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Lareyre F, Chaudhuri A, Nasr B, Raffort J. Machine Learning and Omics Analysis in Aortic Aneurysm. Angiology 2023:33197231206427. [PMID: 37817423 DOI: 10.1177/00033197231206427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field.
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Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Nice, France
- Inserm U1065, C3M, Université Côte d'Azur, Nice, France
| | - Arindam Chaudhuri
- Bedfordshire-Milton Keynes Vascular Centre, Bedfordshire Hospitals NHS Foundation Trust, Bedford, UK
| | - Bahaa Nasr
- Department of Vascular and Endovascular Surgery, Brest University Hospital, Brest, France
- INSERM UMR 1101, LaTIM, Brest, France
| | - Juliette Raffort
- Inserm U1065, C3M, Université Côte d'Azur, Nice, France
- Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France
- 3IA Institute, Université Côte d'Azur, Nice, France
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23
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Li B, Beaton D, Eisenberg N, Lee DS, Wijeysundera DN, Lindsay TF, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Using machine learning to predict outcomes following carotid endarterectomy. J Vasc Surg 2023; 78:973-987.e6. [PMID: 37211142 DOI: 10.1016/j.jvs.2023.05.024] [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: 02/17/2023] [Revised: 05/08/2023] [Accepted: 05/13/2023] [Indexed: 05/23/2023]
Abstract
OBJECTIVE Prediction of outcomes following carotid endarterectomy (CEA) remains challenging, with a lack of standardized tools to guide perioperative management. We used machine learning (ML) to develop automated algorithms that predict outcomes following CEA. METHODS The Vascular Quality Initiative (VQI) database was used to identify patients who underwent CEA between 2003 and 2022. We identified 71 potential predictor variables (features) from the index hospitalization (43 preoperative [demographic/clinical], 21 intraoperative [procedural], and 7 postoperative [in-hospital complications]). The primary outcome was stroke or death at 1 year following CEA. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, insurance status, symptom status, and urgency of surgery. RESULTS Overall, 166,369 patients underwent CEA during the study period. In total, 7749 patients (4.7%) had the primary outcome of stroke or death at 1 year. Patients with an outcome were older with more comorbidities, had poorer functional status, and demonstrated higher risk anatomic features. They were also more likely to undergo intraoperative surgical re-exploration and have in-hospital complications. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.90 (95% confidence interval [CI], 0.89-0.91). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67), and existing tools in the literature demonstrate AUROCs ranging from 0.58 to 0.74. Our XGBoost models maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.90 (95% CI, 0.89-0.91) and 0.94 (95% CI, 0.93-0.95), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.15 (preoperative), 0.14 (intraoperative), and 0.11 (postoperative). Of the top 10 predictors, eight were preoperative features, including comorbidities, functional status, and previous procedures. Model performance remained robust on all subgroup analyses. CONCLUSIONS We developed ML models that accurately predict outcomes following CEA. Our algorithms perform better than logistic regression and existing tools, and therefore, have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes.
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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, 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
| | - Derek Beaton
- Data Science and Advanced Analytics Department, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Thomas F Lindsay
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Muhammad Mamdani
- 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; Data Science and Advanced Analytics Department, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health 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; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
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Roumengas R, Di Lorenzo G, Salhi A, de Buyer P, Chaudhuri A, Lareyre F, Raffort J. Natural Language Processing for Literature Search in Vascular Surgery: A Pilot Study Testing an Artificial Intelligence Based Application. EJVES Vasc Forum 2023; 60:48-52. [PMID: 37799295 PMCID: PMC10550400 DOI: 10.1016/j.ejvsvf.2023.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 08/01/2023] [Accepted: 09/12/2023] [Indexed: 10/07/2023] Open
Abstract
Introduction The use of natural language processing (NLP) for a literature search has been poorly investigated in vascular surgery so far. The aim of this pilot study was to test the applicability of an artificial intelligence (AI) based mobile application for literature searching in a topic related to vascular surgery. Technique A focused scientific question was defined to evaluate the performance of the AI application for a literature search and compare the results with the ground truth provided via a traditional literature search performed by human experts. Using pre-defined keywords, the literature search was performed automatically by the AI application through different steps, including quality assessment based on evaluation of the information available and quality filters using indicators of level of evidence, selection of publications based on relevancy filters using NLP, summarisation, and visualisation of the publications via the mobile app. A traditional literature search performed by human experts required 10 hours to check 154 original articles, among which 26 (16.9%) were truly related to the question, 63 (40.9%) related to the field but not to the specific question, and 65 (42.2%) were unrelated. The AI based search was performed in less than one hour, and, compared with traditional search, the method identified 17 original articles (48.6%) truly related to the question (p < .010), 18 (51.4%) related to the field but not to the specific question (p = .26), and no unrelated publications (p < .001). Fifteen truly related articles (88.2%) were identified jointly by the two methods. No significant difference was observed regarding the median number of citations, year of publications, and impact factor of journals. Discussion The AI based method enabled a targeted, focused, and time saving literature search, although the selection of publications was not completely exhaustive. These results suggest that such an AI driven application is a complementary tool to help researchers and clinicians for continuous education and dissemination of knowledge.
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Affiliation(s)
| | - Gilles Di Lorenzo
- Department of Vascular Surgery, Hospital of Antibes-Juan-les-Pins, Antibes, France
| | - Amel Salhi
- Juisci (Juisci SAS), Neuilly-sur-Seine, France
| | | | - Arindam Chaudhuri
- Bedfordshire – Milton Keynes Vascular Centre, Bedfordshire Hospitals, NHS Foundation Trust, Bedford, UK
| | - Fabien Lareyre
- Department of Vascular Surgery, Hospital of Antibes-Juan-les-Pins, Antibes, France
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France
| | - Juliette Raffort
- Université Côte d'Azur, CHU, 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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Jiang T, Zhao Z, Liu X, Shen C, Mu M, Cai Z, Zhang B. Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study. Front Oncol 2023; 13:1161237. [PMID: 37731636 PMCID: PMC10507631 DOI: 10.3389/fonc.2023.1161237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 08/16/2023] [Indexed: 09/22/2023] Open
Abstract
Background Machine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance. Methods The PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared. Results Out of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias. Conclusion The current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction. Study registration This protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b).
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Affiliation(s)
- Tianxiang Jiang
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhou Zhao
- Department of Gastrointestinal Cancer Center, Chongqing University Cancer Hospital, Chongqing, China
| | - Xueting Liu
- Department of Medical Discipline Construction, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyong Shen
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Mingchun Mu
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaolun Cai
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Zhang
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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Lareyre F. Ethics and Legal Framework for Trustworthy Artificial Intelligence in Vascular Surgery. EJVES Vasc Forum 2023; 60:42-44. [PMID: 37790247 PMCID: PMC10542591 DOI: 10.1016/j.ejvsvf.2023.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 10/05/2023] Open
Affiliation(s)
- Fabien Lareyre
- Corresponding author. Department of Vascular Surgery, Hospital of Antibes-Juan-les-Pins, 107 avenue de, Nice, 06 600 Antibes, France.
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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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Alonso A, Siracuse JJ. Protecting patient safety and privacy in the era of artificial intelligence. Semin Vasc Surg 2023; 36:426-429. [PMID: 37863615 DOI: 10.1053/j.semvascsurg.2023.06.002] [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: 04/08/2023] [Revised: 06/14/2023] [Accepted: 06/20/2023] [Indexed: 10/22/2023]
Abstract
The promise of artificial intelligence (AI) in health care has propelled a significant uptrend in the number of clinical trials in AI and global market spending in this novel technology. In vascular surgery, this technology has the ability to diagnose disease, predict disease outcomes, and assist with image-guided surgery. As we enter an era of rapid change, it is critical to evaluate the ethical concerns of AI, particularly as it may impact patient safety and privacy. This is particularly important to discuss in the early stages of AI, as technology frequently outpaces the policies and ethical guidelines regulating it. Issues at the forefront include patient privacy and confidentiality, protection of patient autonomy and informed consent, accuracy and applicability of this technology, and propagation of health care disparities. Vascular surgeons should be equipped to work with AI, as well as discuss its novel risks to patient safety and privacy.
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Affiliation(s)
- Andrea Alonso
- Division of Vascular and Endovascular Surgery, Department of Surgery, Boston Medical Center, Chobanian and Avedisian School of Medicine, Boston University, 85 E. Concord St, Boston, MA 02118
| | - Jeffrey J Siracuse
- Division of Vascular and Endovascular Surgery, Department of Surgery, Boston Medical Center, Chobanian and Avedisian School of Medicine, Boston University, 85 E. Concord St, Boston, MA 02118.
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Stonko DP, Hicks CW. Mature artificial intelligence- and machine learning-enabled medical tools impacting vascular surgical care: A scoping review of late-stage, US Food and Drug Administration-approved or cleared technologies relevant to vascular surgeons. Semin Vasc Surg 2023; 36:460-470. [PMID: 37863621 PMCID: PMC10589449 DOI: 10.1053/j.semvascsurg.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/14/2023] [Accepted: 06/20/2023] [Indexed: 10/22/2023]
Abstract
Artificial intelligence and machine learning (AI/ML)-enabled tools are shifting from theoretical or research-only applications to mature, clinically useful tools. The goal of this article was to provide a scoping review of the most mature AI/ML-enabled technologies reviewed and cleared by the US Food and Drug Administration relevant to the field of vascular surgery. Despite decades of slow progress, this landscape is now evolving rapidly, with more than 100 AI/ML-powered tools being approved by the US Food and Drug Administration each year. Within the field of vascular surgery specifically, this review identified 17 companies with mature technologies that have at least one US Food and Drug Administration clearance, all occurring between 2016 and 2022. The maturation of these technologies appears to be accelerating, with improving regulatory clarity and clinical uptake. The early AI/ML-powered devices extend or amplify clinically entrenched platform technologies and tend to be focused on the diagnosis or evaluation of time-sensitive, clinically important pathologies (eg, reading Digital Imaging and Communications in Medicine-compliant computed tomography images to identify pulmonary embolism), or when physician efficiency or time savings is improved (eg, preoperative planning and intraoperative guidance). The majority (>75%) of these technologies are at the intersection of radiology and vascular surgery. It is becoming increasingly important that the contemporary vascular surgeon understands this shifting paradigm, as these once-nascent technologies are finally maturing and will be encountered with increasingly regularity in daily clinical practice.
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Affiliation(s)
- David P Stonko
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, The Johns Hopkins Hospital, 600 North Wolfe Street, Halsted 668, Baltimore, MD 21287
| | - Caitlin W Hicks
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, The Johns Hopkins Hospital, 600 North Wolfe Street, Halsted 668, Baltimore, MD 21287.
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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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31
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Tran Z, Byun J, Lee HY, Boggs H, Tomihama EY, Kiang SC. Bias in artificial intelligence in vascular surgery. Semin Vasc Surg 2023; 36:430-434. [PMID: 37863616 DOI: 10.1053/j.semvascsurg.2023.07.003] [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: 07/13/2023] [Revised: 07/28/2023] [Accepted: 07/28/2023] [Indexed: 10/22/2023]
Abstract
Application of artificial intelligence (AI) has revolutionized the utilization of big data, especially in patient care. The potential of deep learning models to learn without a priori assumption, or without prior learning, to connect seemingly unrelated information mixes excitement alongside hesitation to fully understand AI's limitations. Bias, ranging from data collection and input to algorithm development to finally human review of algorithm output affects AI's application to clinical patient presents unique challenges that differ significantly from biases in traditional analyses. Algorithm fairness, a new field of research within AI, aims to mitigate bias by evaluating the data at the preprocessing stage, optimizing during algorithm development, and evaluating algorithm output at the postprocessing stage. As the field continues to develop, being cognizant of the inherent biases and limitations related to black box decision making, biased data sets agnostic to patient-level disparities, wide variation of present methodologies, and lack of common reporting standards will require ongoing research to provide transparency to AI and its applications.
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Affiliation(s)
- Zachary Tran
- Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, 11175 Campus Street, Suite 21123, Loma Linda, CA 92350
| | - Julianne Byun
- Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, 11175 Campus Street, Suite 21123, Loma Linda, CA 92350
| | - Ha Yeon Lee
- Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, 11175 Campus Street, Suite 21123, Loma Linda, CA 92350
| | - Hans Boggs
- Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, 11175 Campus Street, Suite 21123, Loma Linda, CA 92350
| | - Emma Y Tomihama
- Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, 11175 Campus Street, Suite 21123, Loma Linda, CA 92350
| | - Sharon C Kiang
- Department of Surgery, Division of Vascular Surgery, Linda University School of Medicine, 11175 Campus Street, Suite 21123, Loma Linda, CA 92350; Department of Surgery, Division of Vascular Surgery, VA Loma Linda Healthcare System, 11201 Benton Street, Loma Linda, CA 92357.
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Vasey B, Collins GS. Invited Commentary: Transparent reporting of artificial intelligence models development and evaluation in surgery: The TRIPOD and DECIDE-AI checklists. Surgery 2023; 174:727-729. [PMID: 37244769 DOI: 10.1016/j.surg.2023.04.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 04/27/2023] [Indexed: 05/29/2023]
Affiliation(s)
- Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, UK; Department of Surgery, Geneva University Hospital, Switzerland.
| | - Gary S Collins
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, UK. http://www.twitter.com/GSCollins
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Lareyre F, Wanhainen A, Raffort J. Artificial Intelligence-Powered Technologies for the Management of Vascular Diseases: Building Guidelines and Moving Forward Evidence Generation. J Endovasc Ther 2023:15266028231187599. [PMID: 37464795 DOI: 10.1177/15266028231187599] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
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
| | - Anders Wanhainen
- Section of Vascular Surgery, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden
| | - Juliette Raffort
- Université Côte d'Azur, Inserm U1065, C3M, Nice, France
- 3IA Institute, Université Côte d'Azur, Nice, France
- Department of Clinical Biochemistry, University Hospital of Nice, Nice, France
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Li Q, Chi L, Zhao W, Wu L, Jiao C, Zheng X, Zhang K, Li X. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis. Front Neurol 2023; 14:1039794. [PMID: 37388543 PMCID: PMC10299899 DOI: 10.3389/fneur.2023.1039794] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 05/25/2023] [Indexed: 07/01/2023] Open
Abstract
Background Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke. Methods A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters. Results A total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively. Conclusion ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.
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Affiliation(s)
- Qinglin Li
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Chi
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Weiying Zhao
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Wu
- Department of Acupuncture, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Chuanxu Jiao
- Department of Neurorehabilitation, Taizhou Enze Medical Center Luqiao Hospital, Taizhou, Zhejiang, China
| | - Xue Zheng
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Kaiyue Zhang
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Xiaoning Li
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
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Marwaha JS, Raza MM, Kvedar JC. The digital transformation of surgery. NPJ Digit Med 2023; 6:103. [PMID: 37258642 PMCID: PMC10232406 DOI: 10.1038/s41746-023-00846-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 05/15/2023] [Indexed: 06/02/2023] Open
Abstract
Rapid advances in digital technology and artificial intelligence in recent years have already begun to transform many industries, and are beginning to make headway into healthcare. There is tremendous potential for new digital technologies to improve the care of surgical patients. In this piece, we highlight work being done to advance surgical care using machine learning, computer vision, wearable devices, remote patient monitoring, and virtual and augmented reality. We describe ways these technologies can be used to improve the practice of surgery, and discuss opportunities and challenges to their widespread adoption and use in operating rooms and at the bedside.
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Affiliation(s)
- Jayson S Marwaha
- Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | | | - Joseph C Kvedar
- Harvard Medical School, Boston, MA, USA
- Mass General Brigham, Boston, MA, USA
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Lareyre F, Caradu C, Chaudhuri A, Lê CD, Di Lorenzo G, Adam C, Carrier M, Raffort J. Automatic Detection of Visceral Arterial Aneurysms on Computed Tomography Angiography Using Artificial Intelligence Based Segmentation of the Vascular System. EJVES Vasc Forum 2023; 59:15-19. [PMID: 37396440 PMCID: PMC10310472 DOI: 10.1016/j.ejvsvf.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/25/2023] [Accepted: 05/02/2023] [Indexed: 07/04/2023] Open
Abstract
Introduction Visceral arterial aneurysms (VAAs) are life threatening. Due to the paucity of symptoms and rarity of the disease, VAAs are underdiagnosed and underestimated. Artificial intelligence (AI) offers new insights into segmentation of the vascular system, and opportunities to better detect VAAs. This pilot study aimed to develop an AI based method to automatically detect VAAs from computed tomography angiography (CTA). Methods A hybrid method combining a feature based expert system with a supervised deep learning algorithm (convolutional neural network) was used to enable fully automatic segmentation of the abdominal vascular tree. Centrelines were built and reference diameters of each visceral artery were calculated. An abnormal dilatation (VAAs) was defined as a substantial increase in diameter at the pixel of interest compared with the mean diameter of the reference portion. The automatic software provided 3D rendered images with a flag on the identified VAA areas. The performance of the method was tested in a dataset of 33 CTA scans and compared with the ground truth provided by two human experts. Results Forty-three VAAs were identified by human experts (32 in the coeliac trunk branches, eight in the superior mesenteric artery, one in the left renal, and two in the right renal arteries). The automatic system accurately detected 40 of the 43 VAAs, with a sensitivity of 0.93 and a positive predictive value of 0.51. The mean number of flag areas per CTA was 3.5 ± 1.5 and they could be reviewed and checked by a human expert in less than 30 seconds per CTA. Conclusion Although the specificity needs to be improved, this study demonstrates the potential of an AI based automatic method to develop new tools to improve screening and detection of VAAs by automatically attracting clinicians' attention to suspicious dilatations of the visceral arteries.
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Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France
| | - Caroline Caradu
- Department of Vascular Surgery, University Hospital of Bordeaux, France
| | - Arindam Chaudhuri
- Bedfordshire - Milton Keynes Vascular Centre, Bedford Hospital NHS Trust, Bedford, UK
| | - Cong Duy Lê
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France
| | - Gilles Di Lorenzo
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France
| | - Cédric Adam
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, France
| | - Marion Carrier
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, France
| | - Juliette Raffort
- Université Côte d'Azur, CHU, 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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Lareyre F, Behrendt CA, Chaudhuri A, Raffort J. Artificial Intelligence in Vascular Surgical Departments: Slowly But Surely. Angiology 2023; 74:399-400. [PMID: 36042693 DOI: 10.1177/00033197221124759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, 70607Hospital of Antibes, Juan-les-Pins, France.,Université Côte d'Azur, 477107Inserm U1065, C3M, France
| | - Christian-Alexander Behrendt
- 575329Brandenburg Medical School Theodor-Fontane, Neuruppin, Germany.,Research Group GermanVasc, 06000University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.,Department of Vascular and Endovascular Surgery, Asklepios Clinic Wandsbek, 477107Asklepios Medical School Hamburg, Germany
| | - Arindam Chaudhuri
- Bedfordshire - Milton Keynes Vascular Centre, 575329Bedfordshire Hospitals NHS Foundation Trust, UK
| | - Juliette Raffort
- Université Côte d'Azur, 477107Inserm U1065, C3M, France.,Department of Clinical Biochemistry, University Hospital of Nice, France.,3IA Institute, Université Côte d'Azur, Sophia Antipolis, France
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Winarski A, Tambyraja AL. Looking Through the Crystal Ball of Data Science. Eur J Vasc Endovasc Surg 2023; 65:608. [PMID: 36682403 DOI: 10.1016/j.ejvs.2023.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 01/12/2023] [Indexed: 01/21/2023]
Affiliation(s)
- Allison Winarski
- Edinburgh Vascular Service, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Andrew L Tambyraja
- Edinburgh Vascular Service, Royal Infirmary of Edinburgh, Edinburgh, UK.
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Zhou M, Luo X, Wang X, Xie T, Wang Y, Shi Z, Wang M, Fu W. Deep Learning Prediction for Distal Aortic Remodeling After Thoracic Endovascular Aortic Repair in Stanford Type B Aortic Dissection. J Endovasc Ther 2023:15266028231160101. [PMID: 36927177 DOI: 10.1177/15266028231160101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
PURPOSE This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiography (CTA). METHODS A total of 147 patients with acute or subacute TBAD who underwent proximal TEVAR at a single center were retrospectively reviewed. The boundary of aorta was manually segmented, and the point clouds of each aorta were obtained. Prediction of negative aortic remodeling or reintervention was accomplished by a convolutional neural network (CNN) and a point cloud neural network (PC-NN), respectively. The discriminatory value of the established models was mainly evaluated by the area under the receiver operating characteristic curve (AUC) in the test set. RESULTS The mean follow-up time was 34.0 months (range: 12-108 months). During follow-up, a total of 25 (17.0%) patients were identified as having negative aortic remodeling, and 16 (10.9%) patients received reintervention. The AUC (0.876) by PC-NN for predicting negative aortic remodeling was superior to that obtained by CNN (0.612, p=0.034) and similar to the AUC by PC-NN combined with clinical features (0.884, p=0.92). As to reintervention, the AUC by PC-NN was significantly higher than that by CNN (0.805 vs 0.579; p=0.042), and AUCs by PC-NN combined with clinical features and PC-NN alone were comparable (0.836 vs 0.805; p=0.81). CONCLUSION The CTA-based deep learning algorithms may assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD. CLINICAL IMPACT Negative aortic remodeling is the leading cause of late reintervention after proximal thoracic endovascular aortic repair (TEVAR) for Stanford type B aortic dissection (TBAD), and possesses great challenge to endovascular repair. Early recognizing high-risk patients is of supreme importance for optimizing the follow-up interval and therapy strategy. Currently, clinicians predict the prognosis of these patients based on several imaging signs, which is subjective. The computed tomography angiography-based deep learning algorithms may incorporate abundant morphological information of aorta, provide with a definite and objective output value, and finally assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD.
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Affiliation(s)
- Min Zhou
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Xiaoyuan Luo
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Xia Wang
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Tianchen Xie
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Yonggang Wang
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Zhenyu Shi
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Manning Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Weiguo Fu
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Institute of Vascular Surgery, Fudan University, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
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40
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Machine Learning in Dentistry: A Scoping Review. J Clin Med 2023; 12:jcm12030937. [PMID: 36769585 PMCID: PMC9918184 DOI: 10.3390/jcm12030937] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/06/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Abstract
Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.
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Assessing the robustness of negative vascular surgery randomized controlled trials using their reverse fragility index. J Vasc Surg 2022:S0741-5214(22)02650-7. [PMID: 36572321 DOI: 10.1016/j.jvs.2022.12.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/15/2022] [Accepted: 12/17/2022] [Indexed: 12/25/2022]
Abstract
OBJECTIVE The reverse fragility index (RFI) describes the number of event conversions needed to convert a statistically nonsignificant dichotomous outcome to a significant one. The objective of the present study was to assess the RFI of vascular surgery randomized controlled trials (RCTs) comparing endovascular vs open surgery for the treatment of abdominal aortic aneurysms (AAAs), carotid artery stenosis (CAS), and peripheral artery disease (PAD). METHODS MEDLINE and Embase were searched for RCTs that had investigated AAAs, CAS, or PAD with statistically nonsignificant binary primary outcomes. The primary outcome for the present study was the median RFI. Calculation of the RFI was performed by creating two-by-two contingency tables and subtracting events from the group with fewer events and adding nonevents to the same group until a two-tailed Fisher exact test had produced a statistically significant result (P ≤ .05). RESULTS Of 4187 reports, 49 studies reporting 103 different primary end points were included. The overall median RFI was 7 (interquartile range [IQR], 5-13). The specific RFIs for AAA, CAS, and PAD were 10 (IQR, 6-15.5), 6 (IQR, 5-9.5), and 7 (IQR, 5.5-10), respectively. Of the 103 end points, 42 (47%) had had a loss to follow-up greater than the RFI, of which 10 were AAA trials (24%), 23 were CAS trials (55%), and 9 were PAD trials (21%). The Pearson correlation demonstrated a significant positive relationship between a study's RFI and the impact factor of its publishing journal (r = 0.38; 95% confidence interval [CI], 0.20-0.54; P < .01), length of follow-up (r = 0.43; 95% CI, 0.26-0.58; P < .01), and sample size (r = 0.28; 95% CI, 0.09-0.45; P < .01). CONCLUSIONS A small number of events (median, 7) was required to change the outcome of negative RCTs from statistically nonsignificant to significant, with 47% of the studies having missing data that could have reversed the finding of its primary outcome. Reporting of the RFI relative to the loss to follow-up could be of benefit in future trials and provide confidence regarding the robustness of the P value.
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A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future. Plast Reconstr Surg Glob Open 2022; 10:e4608. [PMID: 36479133 PMCID: PMC9722565 DOI: 10.1097/gox.0000000000004608] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 08/24/2022] [Indexed: 01/25/2023]
Abstract
UNLABELLED Artificial intelligence (AI) is presently employed in several medical specialties, particularly those that rely on large quantities of standardized data. The integration of AI in surgical subspecialties is under preclinical investigation but is yet to be widely implemented. Plastic surgeons collect standardized data in various settings and could benefit from AI. This systematic review investigates the current clinical applications of AI in plastic and reconstructive surgery. METHODS A comprehensive literature search of the Medline, EMBASE, Cochrane, and PubMed databases was conducted for AI studies with multiple search terms. Articles that progressed beyond the title and abstract screening were then subcategorized based on the plastic surgery subspecialty and AI application. RESULTS The systematic search yielded a total of 1820 articles. Forty-four studies met inclusion criteria warranting further analysis. Subcategorization of articles by plastic surgery subspecialties revealed that most studies fell into aesthetic and breast surgery (27%), craniofacial surgery (23%), or microsurgery (14%). Analysis of the research study phase of included articles indicated that the current research is primarily in phase 0 (discovery and invention; 43.2%), phase 1 (technical performance and safety; 27.3%), or phase 2 (efficacy, quality improvement, and algorithm performance in a medical setting; 27.3%). Only one study demonstrated translation to clinical practice. CONCLUSIONS The potential of AI to optimize clinical efficiency is being investigated in every subfield of plastic surgery, but much of the research to date remains in the preclinical status. Future implementation of AI into everyday clinical practice will require collaborative efforts.
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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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Lareyre F, Behrendt CA, Chaudhuri A, Ayache N, Delingette H, Raffort J. Big Data and Artificial Intelligence in Vascular Surgery: Time for Multidisciplinary Cross-Border Collaboration. Angiology 2022; 73:697-700. [PMID: 35815537 DOI: 10.1177/00033197221113146] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, 70607Hospital 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, 575329Bedfordshire Hospitals NHS Foundation Trust, Bedford, UK
| | - Nicholas Ayache
- Université Côte d'Azur84436 Inria, EPIONE Team, Sophia Antipolis, France.,Université Côte d'Azur 3IA Institute, France
| | - Hervé Delingette
- Université Côte d'Azur84436 Inria, EPIONE Team, Sophia Antipolis, France.,Université Côte d'Azur 3IA Institute, France
| | - Juliette Raffort
- Université Côte d'Azur, Inserm U1065, C3M, Nice, France.,Université Côte d'Azur 3IA Institute, France.,Department of clinical Biochemistry, 37045University Hospital of Nice, Nice, France
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e-Health in Vascular Diseases: Integrating Digital Innovation in Everyday Clinical Practice. J Clin Med 2022; 11:jcm11164757. [PMID: 36012995 PMCID: PMC9410488 DOI: 10.3390/jcm11164757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 12/11/2022] Open
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Lareyre F, Lê CD, Adam C, Carrier M, Raffort J. Bibliometric Analysis on Artificial Intelligence and Machine Learning in Vascular Surgery. Ann Vasc Surg 2022; 86:e1-e2. [PMID: 35798225 DOI: 10.1016/j.avsg.2022.06.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/02/2022] [Indexed: 12/17/2022]
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.
| | - Cong Duy Lê
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France; Université Côte d'Azur, Inserm U1065, C3M, Nice, France
| | - Cédric Adam
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Marion Carrier
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Juliette Raffort
- Université Côte d'Azur, Inserm U1065, C3M, Nice, France; Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France; 3IA Institute, Université Côte d'Azur, Sophia-Antipolis, France
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Javidan AP, Li A, Lee MH, Forbes TL, Naji F. A Systematic Review and Bibliometric Analysis of Applications of Artificial Intelligence and Machine Learning in Vascular Surgery. Ann Vasc Surg 2022; 85:395-405. [PMID: 35339595 DOI: 10.1016/j.avsg.2022.03.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 02/22/2022] [Accepted: 03/12/2022] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) and machine learning (ML) have seen increasingly intimate integration with medicine and healthcare in the last two decades. The objective of this study was to summarize all current applications of AI and ML in the vascular surgery literature and to conduct a bibliometric analysis of the published studies. METHODS A comprehensive literature search was conducted through EMBASE, MEDLINE, and Ovid HealthStar from inception until February 19, 2021. Reporting of this study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Title and abstract screening, full-text screening, and data extraction were conducted in duplicate. Data extracted included study meta-data, the clinical area of study within vascular surgery, type of AI/ML method used, data set, and the application of AI/ML. Publishing journals were classified as having either a clinical scope or technical scope. Author academic background was classified as clinical, non-clinical (e.g., engineering) or both, depending on author affiliation. RESULTS The initial search identified 7434 studies, of which 249 were included for final analysis. The rate of publications is exponentially increasing, with 158 (63%) studies being published in the last 5 years. Studies were most commonly related to carotid artery disease (118, 47%), abdominal aortic aneurysms (51, 20%), and peripheral arterial disease (26, 10%). Study authors employed an average of 1.50 (range: 1-6) distinct AI methods in their studies. The application of AI/ML methods broadly related to predictive models (54, 22%), image segmentation (49, 19.4%), diagnostic methods (46, 18%), or multiple combined applications (91, 37%). The most commonly used AI/ML methods were artificial neural networks (155/378 use cases, 41%), support vector machines (64, 17%), k-nearest neighbors algorithm (26, 7%), and random forests (23, 6%). Data sets to which these AI/ML methods were applied frequently involved ultrasound images (87, 35%), CT images (42, 17%), clinical data (34, 14%) or multiple data sets (36, 14%). Overall, 22 (9%) studies were published in journals specific to vascular surgery, with the majority (147/249, 59%) being published in journals with a scope related to computer science or engineering. Among 1576 publishing authors, 46% had exclusively a clinical background, 48% a non-clinical background, and 5% had both a clinical and non-clinical background. CONCLUSION There is an exponentially growing body of literature describing the use of AI and ML in vascular surgery. There is a focus on carotid artery disease and abdominal aortic disease, with many other areas of vascular surgery underrepresented. Neural networks and support vector machines composed most AI methods in the literature. As AI/ML continues to see more expanded applications in the field, it is important that vascular surgeons appreciate its potential and limitations. Additionally, as it sees increasing use, there is a need for clinicians with expertise in AI/ML methods who can optimize its transition into daily practice.
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Affiliation(s)
- Arshia P Javidan
- Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Institute of Health Policy Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada.
| | - Allen Li
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Michael H Lee
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Thomas L Forbes
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Faysal Naji
- Department of Vascular Surgery, McMaster University, Hamilton, Ontario, Canada
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