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Mehta VS, Ma Y, Wijesuriya N, DeVere F, Howell S, Elliott MK, Mannkakara NN, Hamakarim T, Wong T, O'Brien H, Niederer S, Razavi R, Rinaldi CA. Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models. Heart Rhythm 2024; 21:919-928. [PMID: 38354872 DOI: 10.1016/j.hrthm.2024.02.015] [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: 12/06/2023] [Revised: 01/22/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE). OBJECTIVE The purpose of this study was to test whether integrating imaging data into an existing ML model increases its ability to predict major adverse events (MAEs; procedure-related major complications and procedure-related deaths) and lengthy procedures (≥100 minutes). METHODS We hypothesized certain features-(1) lead angulation, (2) coil percentage inside the superior vena cava (SVC), and (3) number of overlapping leads in the SVC-detected from a pre-TLE plain anteroposterior chest radiograph (CXR) would improve prediction of MAE and long procedural times. A deep-learning convolutional neural network was developed to automatically detect these CXR features. RESULTS A total of 1050 cases were included, with 24 MAEs (2.3%) . The neural network was able to detect (1) heart border with 100% accuracy; (2) coils with 98% accuracy; and (3) acute angle in the right ventricle and SVC with 91% and 70% accuracy, respectively. The following features significantly improved MAE prediction: (1) ≥50% coil within the SVC; (2) ≥2 overlapping leads in the SVC; and (3) acute lead angulation. Balanced accuracy (0.74-0.87), sensitivity (68%-83%), specificity (72%-91%), and area under the curve (AUC) (0.767-0.962) all improved with imaging biomarkers. Prediction of lengthy procedures also improved: balanced accuracy (0.76-0.86), sensitivity (75%-85%), specificity (63%-87%), and AUC (0.684-0.913). CONCLUSION Risk prediction tools integrating imaging biomarkers significantly increases the ability of ML models to predict risk of MAE and long procedural time related to TLE.
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Affiliation(s)
- Vishal S Mehta
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - YingLiang Ma
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; School of Computing Sciences, University of East Anglia, Norwich, United Kingdom
| | - Nadeev Wijesuriya
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Felicity DeVere
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sandra Howell
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Mark K Elliott
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Nilanka N Mannkakara
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Tatiana Hamakarim
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Tom Wong
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Hugh O'Brien
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Reza Razavi
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher A Rinaldi
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Heart Vascular & Thoracic Institute, Cleveland Clinic London, London, United Kingdom
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Riaz Gondal MU, Atta Mehdi H, Khenhrani RR, Kumari N, Ali MF, Kumar S, Faraz M, Malik J. Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology. Cardiol Rev 2024:00045415-990000000-00270. [PMID: 38761137 DOI: 10.1097/crd.0000000000000715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/20/2024]
Abstract
Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.
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Affiliation(s)
| | - Hassan Atta Mehdi
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Raja Ram Khenhrani
- Department of Medicine, Internal Medicine Fellow, Shaheed Mohtarma Benazir Bhutto Medical College and Lyari General Hospital, Karachi, Pakistan
| | - Neha Kumari
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Muhammad Faizan Ali
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Sooraj Kumar
- Department of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan; and
| | - Maria Faraz
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
| | - Jahanzeb Malik
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Barnsley H, Uzoukwu S, Hassan S, Borri M. The use of low dose CT scouts for MR safety screening: A multi-reader evaluation. Radiography (Lond) 2024; 30:168-175. [PMID: 38035429 DOI: 10.1016/j.radi.2023.11.002] [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: 05/21/2023] [Revised: 10/30/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023]
Abstract
INTRODUCTION Plain film radiographs are recommended to assist in MRI safety screening of patients with unknown medical histories, especially in an emergency setting where patients might be unable to answer a safety questionnaire. This study assesses the performance of CT scout images, which have low radiation dose and are faster and easier to acquire compared to plain film radiographs, in finding and naming a range of head and body implants. METHODS A retrospective analysis of 40 CT Head and Neck (HN) scout images and 40 CT Chest, Abdomen and Pelvis (CAP) scout images was undertaken. A subset of these were chosen to include a range of common internal implants not identifiable externally to the patient. The images were assessed by three readers with varying levels of clinical experience in MRI who were asked to find and name any implants seen. RESULTS Collectively, all readers reached a sensitivity of 85 % in finding internal implants, regardless of their clinical experience or experience in reviewing CT images, and a minimum specificity of 95 %. Implants were correctly named in 74 % of the images presented. CONCLUSION CT scout images were able to reveal most of the implants included. However, clinical experience in reviewing the images enhances a reader's ability to identify the type of implant. IMPLICATIONS FOR PRACTICE In an emergency setting, imaging can be critical in the management of patients presenting with acute illnesses. In the unconscious or unresponsive patient, the use of CT scouts, where this is the only option available, could provide valuable MRI safety information prior to a scan, improving access to the MRI scan in a timely manner.
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Affiliation(s)
- H Barnsley
- Neuroradiology, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, UK.
| | - S Uzoukwu
- Neuroradiology, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, UK
| | - S Hassan
- Neuroradiology, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, UK
| | - M Borri
- Neuroradiology, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, UK.
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Kufel J, Bargieł-Łączek K, Koźlik M, Czogalik Ł, Dudek P, Magiera M, Bartnikowska W, Lis A, Paszkiewicz I, Kocot S, Cebula M, Gruszczyńska K, Nawrat Z. Chest X-ray Foreign Objects Detection Using Artificial Intelligence. J Clin Med 2023; 12:5841. [PMID: 37762783 PMCID: PMC10531506 DOI: 10.3390/jcm12185841] [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: 08/13/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.
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Affiliation(s)
- Jakub Kufel
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland;
| | - Katarzyna Bargieł-Łączek
- Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, 40-752 Katowice, Poland; (K.B.-Ł.); (W.B.)
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-752 Katowice, Poland;
| | - Maciej Koźlik
- Division of Cardiology and Structural Heart Disease, Medical University of Silesia, 40-635 Katowice, Poland;
| | - Łukasz Czogalik
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Piotr Dudek
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Mikołaj Magiera
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Wiktoria Bartnikowska
- Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, 40-752 Katowice, Poland; (K.B.-Ł.); (W.B.)
| | - Anna Lis
- Cardiology Students’ Scientific Association at the III Department of Cardiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-635 Katowice, Poland;
| | - Iga Paszkiewicz
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Szymon Kocot
- Bright Coders’ Factory, Technologiczna 2, 45-839 Opole, Poland;
| | - Maciej Cebula
- Individual Specialist Medical Practice, 40-754 Katowice, Poland;
| | - Katarzyna Gruszczyńska
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-752 Katowice, Poland;
| | - Zbigniew Nawrat
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland;
- Foundation of Cardiac Surgery Development, 41-800 Zabrze, Poland
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Stühlinger M, Burri H, Vernooy K, Garcia R, Lenarczyk R, Sultan A, Brunner M, Sabbag A, Özcan EE, Ramos JT, Di Stolfo G, Suleiman M, Tinhofer F, Aristizabal JM, Cakulev I, Eidelman G, Yeo WT, Lau DH, Mulpuru SK, Nielsen JC, Heinzel F, Prabhu M, Rinaldi CA, Sacher F, Guillen R, de Pooter J, Gandjbakhch E, Sheldon S, Prenner G, Mason PK, Fichtner S, Nitta T. EHRA consensus on prevention and management of interference due to medical procedures in patients with cardiac implantable electronic devices. Europace 2022; 24:1512-1537. [PMID: 36228183 DOI: 10.1093/europace/euac040] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023] Open
Affiliation(s)
- Markus Stühlinger
- Department of Internal Medicine III - Cardiology and Angiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Haran Burri
- Department of Cardiology, University Hospital of Geneva, Geneva, Switzerland
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Rodrigue Garcia
- Department of Cardiology, University Hospital of Poitiers, Poitiers, France
- Department of Cardiology, Rigshospitalet, Copenhagen, Denmark
| | - Radoslaw Lenarczyk
- Department of Cardiology, Congenital Heart Disease and Electrotherapy, Medical University of Silesia, Silesian Center of Heart Diseases, Zabrze, Poland
- Medical University of Silesia, Division of Medical Sciences, Department of Cardiology, Congenital Heart Diseases and Electrotherapy, Silesian Center for Heart Diseases, Zabrze, Poland
| | - Arian Sultan
- Department of Electrophysiology, Heart Center at University Hospital Cologne, Cologne, Germany
| | - Michael Brunner
- Department of Cardiology and Medical Intensive Care, St Josefskrankenhaus, Freiburg, Germany
| | - Avi Sabbag
- The Davidai Center for Rhythm Disturbances and Pacing, Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Emin Evren Özcan
- Heart Rhythm Management Center, Dokuz Eylul University, İzmir, Turkey
| | - Jorge Toquero Ramos
- Cardiac Arrhythmia and Electrophysiology Unit, Cardiology Department, Puerta de Hierro University Hospital, Majadahonda, Madrid, Spain
| | - Giuseppe Di Stolfo
- Cardiac Intensive Care and Arrhythmology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Mahmoud Suleiman
- Cardiology/Electrophysiology, Rambam Health Care Campus, Haifa, Israel
| | | | | | - Ivan Cakulev
- University Hospitals of Cleveland, Case Western University, Cleveland, OH, USA
| | - Gabriel Eidelman
- San Isidro's Central Hospital, Diagnóstico Maipú, Buenos Aires Province, Argentina
| | - Wee Tiong Yeo
- Department of Cardiology, National University Heart Centre, Singapore, Singapore
| | - Dennis H Lau
- Centre for Heart Rhythm Disorders, The University of Adelaide and Royal Adelaide Hospital, Adelaide, SA, Australia
| | | | - Jens Cosedis Nielsen
- Department of Cardiology, Aarhus University Hospital, and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Frank Heinzel
- Department of Cardiology, Charité University Medicine, Campus Virchow-Klinikum, 13353 Berlin, Germany
| | - Mukundaprabhu Prabhu
- Associate Professor in Cardiology, In charge of EP Division, Kasturba Medical College Manipal, Manipal, Karnataka, India
| | | | - Frederic Sacher
- Bordeaux University Hospital, Univ. Bordeaux, Bordeaux, France
| | - Raul Guillen
- Sanatorio Adventista del Plata, Del Plata Adventist University Entre Rios Argentina, Entre Rios, Argentina
| | - Jan de Pooter
- Professor of Cardiology, Ghent University, Deputy Head of Clinic, Heart Center UZ Gent, Ghent, Belgium
| | - Estelle Gandjbakhch
- AP-HP Sorbonne Université, Hôpital Pitié-Salpêtrière, Institut de Cardiologie, ICAN, Paris, France
| | - Seth Sheldon
- The Department of Cardiovascular Medicine, University of Kansas Health System, Kansas City, KS 66160, USA
| | | | - Pamela K Mason
- Director, Electrophysiology Laboratory, University of Virginia, Charlottesville, VA, USA
| | - Stephanie Fichtner
- LMU Klinikum, Medizinische Klinik und Poliklinik I, Campus Großhadern, München, Germany
| | - Takashi Nitta
- Emeritus Professor, Nippon Medical School, Presiding Consultant of Cardiology, Hanyu General Hospital, Saitama, Japan
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White RD, Demirer M, Gupta V, Sebro RA, Kusumoto FM, Erdal BS. Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions. J Med Imaging (Bellingham) 2022; 9:054504. [PMID: 36310648 PMCID: PMC9603740 DOI: 10.1117/1.jmi.9.5.054504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/23/2022] [Indexed: 09/29/2023] Open
Abstract
Purpose Chest X-ray (CXR) use in pre-MRI safety screening, such as for lead-less implanted electronic device (LLIED) recognition, is common. To assist CXR interpretation, we "pre-deployed" an artificial intelligence (AI) model to assess (1) accuracies in LLIED-type (and consequently safety-level) identification, (2) safety implications of LLIED nondetections or misidentifications, (3) infrastructural or workflow requirements, and (4) demands related to model adaptation to real-world conditions. Approach A two-tier cascading methodology for LLIED detection/localization and identification on a frontal CXR was applied to evaluate the performance of the original nine-class AI model. With the unexpected early appearance of LLIED types during simulated real-world trialing, retraining of a newer 12-class version preceded retrialing. A zero footprint (ZF) graphical user interface (GUI)/viewer with DICOM-based output was developed for inference-result display and adjudication, supporting end-user engagement and model continuous learning and/or modernization. Results During model testing or trialing using both the nine-class and 12-class models, robust detection/localization was consistently 100%, with mAP 0.99 from fivefold cross-validation. Safety-level categorization was high during both testing ( AUC ≥ 0.98 and ≥ 0.99 , respectively) and trialing (accuracy 98% and 97%, respectively). LLIED-type identifications by the two models during testing (1) were 98.9% and 99.5% overall correct and (2) consistently showed AUC ≥ 0.92 (1.00 for 8/9 and 9/12 LLIED-types, respectively). Pre-deployment trialing of both models demonstrated overall type-identification accuracies of 94.5% and 95%, respectively. Of the small number of misidentifications, none involved MRI-stringently conditional or MRI-unsafe types of LLIEDs. Optimized ZF GUI/viewer operations led to greater user-friendliness for radiologist engagement. Conclusions Our LLIED-related AI methodology supports (1) 100% detection sensitivity, (2) high identification (including MRI-safety) accuracy, and (3) future model deployment with facilitated inference-result display and adjudication for ongoing model adaptation to future real-world experiences.
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Affiliation(s)
- Richard D. White
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
| | - Mutlu Demirer
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
| | - Vikash Gupta
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
| | - Ronnie A. Sebro
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
| | - Frederick M. Kusumoto
- Mayo Clinic, Department of Cardiovascular Medicine, Jacksonville, Florida, United States
| | - Barbaros Selnur Erdal
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
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Neural Network Detection of Pacemakers for MRI Safety. J Digit Imaging 2022; 35:1673-1680. [PMID: 35768751 DOI: 10.1007/s10278-022-00663-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 04/23/2022] [Accepted: 05/30/2022] [Indexed: 10/17/2022] Open
Abstract
Flagging the presence of cardiac devices such as pacemakers before an MRI scan is essential to allow appropriate safety checks. We assess the accuracy with which a machine learning model can classify the presence or absence of a pacemaker on pre-existing chest radiographs. A total of 7973 chest radiographs were collected, 3996 with pacemakers visible and 3977 without. Images were identified from information available on the radiology information system (RIS) and correlated with report text. Manual review of images by two board certified radiologists was performed to ensure correct labeling. The data set was divided into training, validation, and a hold-back test set. The data were used to retrain a pre-trained image classification neural network. Final model performance was assessed on the test set. Accuracy of 99.67% on the test set was achieved. Re-testing the final model on the full training and validation data revealed a few additional misclassified examples which are further analyzed. Neural network image classification could be used to screen for the presence of cardiac devices, in addition to current safety processes, providing notification of device presence in advance of safety questionnaires. Computational power to run the model is low. Further work on misclassified examples could improve accuracy on edge cases. The focus of many healthcare applications of computer vision techniques has been for diagnosis and guiding management. This work illustrates an application of computer vision image classification to enhance current processes and improve patient safety.
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Application of deep learning algorithm in automated identification of knee arthroplasty implants from plain radiographs using transfer learning models: Are algorithms better than humans? J Orthop 2022; 32:139-145. [DOI: 10.1016/j.jor.2022.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 01/16/2023] Open
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Tsai LW, Yuan KC, Hou SK, Wu WL, Hsu CH, Liu TL, Lee KM, Li CH, Chen HC, Tu E, Dubey R, Yeh CF, Chen RJ. Determining Carina and Clavicular Distance-Dependent Positioning of Endotracheal Tube in Critically Ill Patients: An Artificial Intelligence-Based Approach. BIOLOGY 2022; 11:490. [PMID: 35453690 PMCID: PMC9027916 DOI: 10.3390/biology11040490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/24/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
Early and accurate prediction of endotracheal tube (ETT) location is pivotal for critically ill patients. Automatic and timely detection of faulty ETT locations from chest X-ray images may avert patients' morbidity and mortality. Therefore, we designed convolutional neural network (CNN)-based algorithms to evaluate ETT position appropriateness relative to four detected key points, including tracheal tube end, carina, and left/right clavicular heads on chest radiographs. We estimated distances from the tube end to tracheal carina and the midpoint of clavicular heads. A DenseNet121 encoder transformed images into embedding features, and a CNN-based decoder generated the probability distributions. Based on four sets of tube-to-carina distance-dependent parameters (i.e., (i) 30-70 mm, (ii) 30-60 mm, (iii) 20-60 mm, and (iv) 20-55 mm), corresponding models were generated, and their accuracy was evaluated through the predicted L1 distance to ground-truth coordinates. Based on tube-to-carina and tube-to-clavicle distances, the highest sensitivity, and specificity of 92.85% and 84.62% respectively, were revealed for 20-55 mm. This implies that tube-to-carina distance between 20 and 55 mm is optimal for an AI-based key point appropriateness detection system and is empirically comparable to physicians' consensus.
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Affiliation(s)
- Lung-Wen Tsai
- Department of Medicine Research, Taipei Medical University Hospital, Taipei 11031, Taiwan;
- Department of Information Technology Office, Taipei Medical University Hospital, Taipei 11031, Taiwan
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 11031, Taiwan
| | - Kuo-Ching Yuan
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- Department of Surgery, Da Chien General Hospital, Miaoli 36052, Taiwan
| | - Sen-Kuang Hou
- Department of Emergency Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan;
- Department of Emergency Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Wei-Lin Wu
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Chen-Hao Hsu
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Tyng-Luh Liu
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Kuang-Min Lee
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Chiao-Hsuan Li
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Hann-Chyun Chen
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Ethan Tu
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Rajni Dubey
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Chun-Fu Yeh
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Ray-Jade Chen
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- Division of Infection Diseases, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan
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12
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Nedios S, Iliodromitis K, Kowalewski C, Bollmann A, Hindricks G, Dagres N, Bogossian H. Big Data in electrophysiology. Herzschrittmacherther Elektrophysiol 2022; 33:26-33. [PMID: 35137276 DOI: 10.1007/s00399-022-00837-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
The quantity of data produced and captured in medicine today is unprecedented. Technological improvements and automation have expanded the traditional statistical methods and enabled the analysis of Big Data. This has permitted the discovery of new associations with a granularity that was previously hidden to human eyes. In the first part of this review, the authors would like to provide an overview of basic Machine Learning (ML) principles and techniques in order to better understand their application in recent publications about cardiac arrhythmias. In the second part, ML-enabled advances in disease detection and diagnosis, outcome prediction, and novel disease characterization in topics like electrocardiography, atrial fibrillation, ventricular arrhythmias, and cardiac devices are presented. Finally, the limitations and challenges of applying ML in clinical practice, such as validation, replication, generalizability, and regulatory issues, are discussed. More carefully designed studies and collaborations are needed for ML to become feasible, trustworthy, accurate, and reproducible and to reach its full potential for patient-oriented precision medicine.
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Affiliation(s)
- Sotirios Nedios
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany.
- Rhythmologie, Herzzentrum Leipzig, Universität Leipzig, Strümpellstr. 39, 04289, Leipzig, Germany.
| | - Konstantinos Iliodromitis
- Department of Cardiology and Rhythmology, Ev. Krankenhaus Hagen, Hagen, Germany
- Department of Cardiology, University Witten/Herdecke, Witten, Germany
| | - Christopher Kowalewski
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany
| | - Nikolaos Dagres
- Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany
| | - Harilaos Bogossian
- Department of Cardiology and Rhythmology, Ev. Krankenhaus Hagen, Hagen, Germany
- Department of Cardiology, University Witten/Herdecke, Witten, Germany
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13
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Yuan KC, Tsai LW, Lai KS, Teng ST, Lo YS, Peng SJ. Using Transfer Learning Method to Develop an Artificial Intelligence Assisted Triaging for Endotracheal Tube Position on Chest X-ray. Diagnostics (Basel) 2021; 11:diagnostics11101844. [PMID: 34679542 PMCID: PMC8534985 DOI: 10.3390/diagnostics11101844] [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: 08/21/2021] [Revised: 09/21/2021] [Accepted: 09/28/2021] [Indexed: 11/16/2022] Open
Abstract
Endotracheal tubes (ETTs) provide a vital connection between the ventilator and patient; however, improper placement can hinder ventilation efficiency or injure the patient. Chest X-ray (CXR) is the most common approach to confirming ETT placement; however, technicians require considerable expertise in the interpretation of CXRs, and formal reports are often delayed. In this study, we developed an artificial intelligence-based triage system to enable the automated assessment of ETT placement in CXRs. Three intensivists performed a review of 4293 CXRs obtained from 2568 ICU patients. The CXRs were labeled "CORRECT" or "INCORRECT" in accordance with ETT placement. A region of interest (ROI) was also cropped out, including the bilateral head of the clavicle, the carina, and the tip of the ETT. Transfer learning was used to train four pre-trained models (VGG16, INCEPTION_V3, RESNET, and DENSENET169) and two models developed in the current study (VGG16_Tensor Projection Layer and CNN_Tensor Projection Layer) with the aim of differentiating the placement of ETTs. Only VGG16 based on ROI images presented acceptable performance (AUROC = 92%, F1 score = 0.87). The results obtained in this study demonstrate the feasibility of using the transfer learning method in the development of AI models by which to assess the placement of ETTs in CXRs.
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Affiliation(s)
- Kuo-Ching Yuan
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 10675, Taiwan;
- Department of Surgery, DA CHIEN General Hospital, Miaoli 36052, Taiwan
| | - Lung-Wen Tsai
- Department of Medicine Education, Taipei Medical University Hospital, Taipei 110301, Taiwan;
| | - Kevin S. Lai
- Division of Critical Care Medicine, Department of Emergency and Critical Care Medicine, Taipei Medical University Hospital, Taipei 110301, Taiwan; (K.S.L.); (S.-T.T.)
| | - Sing-Teck Teng
- Division of Critical Care Medicine, Department of Emergency and Critical Care Medicine, Taipei Medical University Hospital, Taipei 110301, Taiwan; (K.S.L.); (S.-T.T.)
| | - Yu-Sheng Lo
- Institute of Biomedical Informatics, Taipei Medical University, Taipei 110301, Taiwan
- Correspondence: (Y.-S.L.); (S.-J.P.); Tel.: +886-2-66382736 (Y.-S.L. & S.-J.P.); Fax: +886-2-87320395 (Y.-S.L.); +886-2-27321956 (S.-J.P.)
| | - Syu-Jyun Peng
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 10675, Taiwan;
- Correspondence: (Y.-S.L.); (S.-J.P.); Tel.: +886-2-66382736 (Y.-S.L. & S.-J.P.); Fax: +886-2-87320395 (Y.-S.L.); +886-2-27321956 (S.-J.P.)
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14
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Sharma S, Batta V, Chidambaranathan M, Mathialagan P, Mani G, Kiruthika M, Datta B, Kamineni S, Reddy G, Masilamani S, Vijayan S, Amanatullah DF. Knee Implant Identification by Fine-Tuning Deep Learning Models. Indian J Orthop 2021; 55:1295-1305. [PMID: 34824729 PMCID: PMC8586384 DOI: 10.1007/s43465-021-00529-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/12/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee arthroplasty prosthesis using automated deep learning models. METHODS Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior-posterior (AP) and lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with saliency maps for visualization. RESULTS After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%, the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing model correctly and uniquely identified the implants which could be visualized using saliency maps. CONCLUSION Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps give us a better understanding of which regions the model is focusing on while predicting the results.
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Affiliation(s)
- Sukkrit Sharma
- Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur, Chengalpattu District, Tamil Nadu 603203 India
| | - Vineet Batta
- Department of Orthopaedic, Luton and Dunstable University College London Hospitals NHS Foundation Trust, Luton, UK
| | - Malathy Chidambaranathan
- Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur, Chengalpattu District, Tamil Nadu 603203 India
| | - Prabhakaran Mathialagan
- Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur, Chengalpattu District, Tamil Nadu 603203 India
| | - Gayathri Mani
- Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur, Chengalpattu District, Tamil Nadu 603203 India
| | - M. Kiruthika
- Department of Orthopaedic, Luton and Dunstable University College London Hospitals NHS Foundation Trust, Luton, UK
| | - Barun Datta
- Army Research and Referral, New Delhi, India
| | | | | | | | - Sandeep Vijayan
- Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Udupi, Karnataka India
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15
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Comparing sensitivity and specificity of pacemaker ID application and cardiac rhythm management device-finder application in identifying cardiac implantable electronic device manufacturer using chest radiograph - An observational study. Ann Med Surg (Lond) 2021; 69:102741. [PMID: 34484722 PMCID: PMC8408627 DOI: 10.1016/j.amsu.2021.102741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/12/2021] [Accepted: 08/15/2021] [Indexed: 11/21/2022] Open
Abstract
Background Smartphone-based applications to identify cardiac implantable electronic devices (CIED) are extremely useful in circumstances, where urgent device interrogation is needed, and a device identification card is not available. Few studies have provided insights regarding the utility of these applications. We have studied two widely available applications i.e., Pacemaker ID app (PMIDa) or Cardiac Rhythm Management Device-Finder (CRMD-f) to identify device manufacturers in CIEDs. Methods 547 patients who underwent CIED implantation from the year 2016-2020 in our institute were enrolled. There were 438 Medtronic and 109 St. Jude's devices. All chest radiographs were de-identified and resized into 225*225 pixels focusing on the CIED. PMIDa and CRMD-f applications were used to identify the CIED. Accuracy, sensitivity, specificity, negative predictive value, and positive predictive value for both applications were calculated and compared. Results Overall, CRMD-f application has higher specificity (93.58 vs. 82.5%) but lower sensitivity (53.6 vs. 55%) than PMIDa. The accuracy of both applications was comparable (61.6% vs. 60.5%). Accuracy varied with CIED model and type tested, and radiograph projection used. Accuracy is greatest with Cardiac-Resynchronization-Therapy (CRT) devices for both applications, followed by a single lead pacemaker. Conclusion CRMD-f has higher accuracy and specificity for CIED manufacturer identification. Both PMIDa and CRMD-f are specific tools to identify CIED but have low sensitivity.
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16
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Kim UH, Kim MY, Park EA, Lee W, Lim WH, Kim HL, Oh S, Jin KN. Deep Learning-Based Algorithm for the Detection and Characterization of MRI Safety of Cardiac Implantable Electronic Devices on Chest Radiographs. Korean J Radiol 2021; 22:1918-1928. [PMID: 34431249 PMCID: PMC8546129 DOI: 10.3348/kjr.2021.0201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 06/07/2021] [Accepted: 06/07/2021] [Indexed: 02/02/2023] Open
Abstract
Objective With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed. Materials and Methods We developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn). Results The overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001). Conclusion The remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.
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Affiliation(s)
- Ue-Hwan Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Moon Young Kim
- Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea
| | - Eun-Ah Park
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Whal Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Woo-Hyun Lim
- Division of Cardiology, Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea
| | - Hack-Lyoung Kim
- Division of Cardiology, Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea
| | - Sohee Oh
- Medical Research Collaborating Center, Seoul National University Boramae Medical Center, Seoul, Korea
| | - Kwang Nam Jin
- Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea.
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Nagarajan VD, Lee SL, Robertus JL, Nienaber CA, Trayanova NA, Ernst S. Artificial intelligence in the diagnosis and management of arrhythmias. Eur Heart J 2021; 42:3904-3916. [PMID: 34392353 PMCID: PMC8497074 DOI: 10.1093/eurheartj/ehab544] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 01/06/2021] [Accepted: 07/27/2021] [Indexed: 01/05/2023] Open
Abstract
The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.
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Affiliation(s)
- Venkat D Nagarajan
- Department of Cardiology, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK.,Department of Cardiology, Doncaster and Bassetlaw Hospitals, NHS Foundation Trust, Thorne Road, Doncaster DN2 5LT, UK
| | - Su-Lin Lee
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), UCL, Foley Street, London W1W 7TS, UK
| | - Jan-Lukas Robertus
- Department of Pathology, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, London SW3 6LY, UK
| | - Christoph A Nienaber
- Department of Cardiology, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, London SW3 6LY, UK
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Charles Street, Baltimore, MD 21218, USA
| | - Sabine Ernst
- Department of Cardiology, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, London SW3 6LY, UK
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18
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Sabottke C, Breaux M, Lee R, Foreman A, Spieler B. Analysis of Potential for User Errors in Mobile Deployment of Radiology Deep Learning for Cardiac Rhythm Device Detection. J Digit Imaging 2021; 34:572-580. [PMID: 33742333 PMCID: PMC8329116 DOI: 10.1007/s10278-021-00443-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 11/28/2020] [Accepted: 03/01/2021] [Indexed: 11/28/2022] Open
Abstract
We examine how convolutional neural networks (CNNs) for cardiac rhythm device detection can exhibit failures in performance under suboptimal deployment scenarios and examine how medically adversarial image presentation can further impair neural network performance. We validated the publicly available Pacemaker-ID web server and mobile app on 43 local hospital emergency department (ED) cases of patients presenting with a cardiac rhythm device on anterior-posterior (AP) chest radiograph and assessed performance using Cohen's kappa coefficient for inter-rater reliability. To illustrate adversarial performance concerns, we then produced example CNN models using the 65,379 patient MIMIC-CXR chest radiograph retrospective database and evaluated performance with area under the receiver operating characteristic (AUROC). In retrospective review of 43 patients with cardiac rhythm devices on AP chest radiographs during our study period (January 1, 2020 to March 1, 2020), 74.4% (32/43) had device manufacturer information readily available within the electronic medical record. A total of 25.6% of patients (11/43) did not have this information documented in the patient chart and could ostensibly benefit from CNN-based identification of device manufacturer. For patients with known device manufacturer, the Pacemaker-ID prediction was accurate in 87.5% of cases (28/32). Mobile app accuracy varied from 62.5 to 93.75% depending on image capture settings and presentation. Cohen's kappa coefficient varied from 0.448 to 0.897 depending on mobile image capture conditions. For our additional analysis of medically adversarial performance failures with a DenseNet121 trained on MIMIC-CXR images, we showed that an AUROC of 0.9807 ± 0.0051 could be achieved on an example testing dataset while masking a 30% false positive rate in identification of cardiac rhythm devices versus clinically distinct entities such as vagal nerve stimulators. Despite the promise of CNN approaches for cardiac rhythm device analysis on chest radiographs, further study is warranted to assess potential for errors driven by user misuse when deploying these models to mobile devices as well as for cases when performance can be impaired by the presence of other support apparatuses.
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Affiliation(s)
- Carl Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, Tucson, AZ, USA.
| | - Marc Breaux
- School of Medicine, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Rebecca Lee
- Department of Internal Medicine, University Hospital and Clinics, Lafayette, LA, USA
| | - Adam Foreman
- Department of Neurology, Lafayette General Medical Center, Lafayette, LA, USA
| | - Bradley Spieler
- Department of Radiology, Louisiana State University Health Sciences Center, New Orleans, LA, USA
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Chudow JJ, Jones D, Weinreich M, Zaremski L, Lee S, Weinreich B, Krumerman A, Fisher JD, Ferrick KJ. A Head-to Head Comparison of Machine Learning Algorithms for Identification of Implanted Cardiac Devices. Am J Cardiol 2021; 144:77-82. [PMID: 33383004 DOI: 10.1016/j.amjcard.2020.12.067] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/18/2020] [Accepted: 12/22/2020] [Indexed: 01/02/2023]
Abstract
Application of artificial intelligence techniques in medicine has rapidly expanded in recent years. Two algorithms for identification of cardiac implantable electronic devices using chest radiography were recently developed: The PacemakerID algorithm, available as a mobile phone application (PIDa) and a web platform (PIDw) and The Pacemaker Identification with Neural Networks (PPMnn), available via web platform. In this study, we assessed the relative accuracy of these algorithms. The machine learning algorithms (PIDa, PIDw, PPMnn) were used to predict device manufacturer using chest X-rays for patients with implanted devices. Each prediction was considered correct if predicted certainty was >75%. For comparative purposes, accuracy of each prediction was compared to the result using the CARDIA-X algorithm. 500 X-rays were included from a convenience sample. Raw accuracy was PIDa 89%, PIDw 73%, PPMnn 71% and CARDIA-X 85%. In conclusion, machine learning algorithms for identification of cardiac devices are accurate at determining device manufacturer, have capacity for improved accuracy with additional training sets and can utilize simple user interfaces. These algorithms have clinical utility in limiting potential infectious exposures and facilitate rapid identification of devices as needed for device reprogramming.
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Affiliation(s)
- Jay J Chudow
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Bronx, New York
| | - Davis Jones
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Bronx, New York
| | - Michael Weinreich
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Bronx, New York
| | - Lynn Zaremski
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Bronx, New York
| | - Suegene Lee
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Bronx, New York
| | - Brian Weinreich
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Bronx, New York
| | - Andrew Krumerman
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Bronx, New York
| | - John D Fisher
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Bronx, New York
| | - Kevin J Ferrick
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Bronx, New York.
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Patel R, Thong EHE, Batta V, Bharath AA, Francis D, Howard J. Automated Identification of Orthopedic Implants on Radiographs Using Deep Learning. Radiol Artif Intell 2021; 3:e200183. [PMID: 34350407 DOI: 10.1148/ryai.2021200183] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 02/21/2020] [Accepted: 03/01/2020] [Indexed: 12/15/2022]
Abstract
Accurate identification of metallic orthopedic implant design is important for preoperative planning of revision arthroplasty. Surgical records of implant models are frequently unavailable. The aim of this study was to develop and evaluate a convolutional neural network for identifying orthopedic implant models using radiographs. In this retrospective study, 427 knee and 922 hip unilateral anteroposterior radiographs, including 12 implant models from 650 patients, were collated from an orthopedic center between March 2015 and November 2019 to develop classification networks. A total of 198 images paired with autogenerated image masks were used to develop a U-Net segmentation network to automatically zero-mask around the implants on the radiographs. Classification networks processing original radiographs, and two-channel conjoined original and zero-masked radiographs, were ensembled to provide a consensus prediction. Accuracies of five senior orthopedic specialists assisted by a reference radiographic gallery were compared with network accuracy using McNemar exact test. When evaluated on a balanced unseen dataset of 180 radiographs, the final network achieved a 98.9% accuracy (178 of 180) and 100% top-three accuracy (180 of 180). The network performed superiorly to all five specialists (76.1% [137 of 180] median accuracy and 85.6% [154 of 180] best accuracy; both P < .001), with robustness to scan quality variation and difficult to distinguish implants. A neural network model was developed that outperformed senior orthopedic specialists at identifying implant models on radiographs; real-world application can now be readily realized through training on a broader range of implants and joints, supported by all code and radiographs being made freely available. Supplemental material is available for this article. Keywords: Neural Networks, Skeletal-Appendicular, Knee, Hip, Computer Applications-General (Informatics), Prostheses, Technology Assess-ment, Observer Performance © RSNA, 2021.
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Affiliation(s)
- Ravi Patel
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - Elizabeth H E Thong
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - Vineet Batta
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - Anil Anthony Bharath
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - Darrel Francis
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
| | - James Howard
- Faculty of Medicine, Imperial College Healthcare NHS Trust, London, England (R.P., E.H.E.T., D.F., J.H.); Department of Bioengineering, Imperial College London, Level 2, Faculty Building, South Kensington Campus, London SW7 2AZ, England (R.P., A.A.B.); and Department of Orthopaedic Surgery, Luton and Dunstable University Hospital, Luton, England (V.B.)
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21
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Higaki A, Kurokawa T, Kazatani T, Kido S, Aono T, Matsuda K, Tanaka Y, Kosaki T, Kawamura G, Shigematsu T, Kawada Y, Hiasa G, Yamada T, Okayama H. Image similarity-based cardiac rhythm device identification from X-rays using feature point matching. PACING AND CLINICAL ELECTROPHYSIOLOGY: PACE 2021; 44:633-640. [PMID: 33687744 DOI: 10.1111/pace.14209] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/09/2021] [Accepted: 02/28/2021] [Indexed: 11/30/2022]
Abstract
AIMS Identifying the manufacturer and the type of cardiac implantable electronic devices (CIEDs) is important in emergent clinical settings. Recent studies have illustrated that artificial neural network models can successfully recognize CIEDs from chest X-ray images. However, all existing methods require a vast amount of medical data to train the classification model. Here, we have proposed a novel method to retrieve an identical CIED image from an image database by employing the feature point matching algorithm. METHODS AND RESULTS A total of 653 unique X-ray images from 456 patients who visited our pacemaker clinic between April 2012 and August 2020 were collected. The device images were manually square-shaped, and was thereafter resized to 224 × 224 pixels. A scale-invariant feature transform (SIFT) algorithm was used to extract the keypoints from the query image and reference images. Paired feature points were selected via brute-force matching, and the average Euclidean distance was calculated. The image with the shortest average distance was defined as the most similar image. The classification performance was indicated by accuracy, precision, recall, and F1-score for detecting the manufacturers and model groups, respectively. The average accuracy, precision, recall, and F-1 score for the manufacturer classification were 97.0%, 0.97, 0.96, and 0.96, respectively. For the model classification task, the average accuracy, precision, recall, and F-1 score were 93.2%, 0.94, 0.92, and 0.93, respectively, all of which were higher than those of the previously reported machine learning models. CONCLUSION Feature point matching is useful for identifying CIEDs from X-ray images.
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Affiliation(s)
- Akinori Higaki
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Tsukasa Kurokawa
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Takuro Kazatani
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Shinsuke Kido
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Tetsuya Aono
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Kensho Matsuda
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Yuta Tanaka
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Tetsuya Kosaki
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Go Kawamura
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Tatsuya Shigematsu
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Yoshitaka Kawada
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Go Hiasa
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Tadakatsu Yamada
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
| | - Hideki Okayama
- Department of Cardiology, Ehime Prefectural Central Hospital, Matsuyama, Ehime, Japan
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22
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Howard JP, Zaman S, Ragavan A, Hall K, Leonard G, Sutanto S, Ramadoss V, Razvi Y, Linton NF, Bharath A, Shun-Shin M, Rueckert D, Francis D, Cole G. Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition. Int J Cardiovasc Imaging 2021; 37:1033-1042. [PMID: 33123938 PMCID: PMC7969571 DOI: 10.1007/s10554-020-02050-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 09/29/2020] [Indexed: 11/25/2022]
Abstract
The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early on in the scan requires experience and vigilance. We investigated whether deep learning of the images acquired in the first few minutes of a scan could provide an automated early alert of abnormal features. Anatomy sequences from 375 CMR scans were used as a training set. From these, we annotated 1500 individual slices and used these to train a convolutional neural network to perform automatic segmentation of the cardiac chambers, great vessels and any pleural effusions. 200 scans were used as a testing set. The system then assembled a 3D model of the thorax from which it made clinical measurements to identify important abnormalities. The system was successful in segmenting the anatomy slices (Dice 0.910) and identified multiple features which may guide further image acquisition. Diagnostic accuracy was 90.5% and 85.5% for left and right ventricular dilatation, 85% for left ventricular hypertrophy and 94.4% for ascending aorta dilatation. The area under ROC curve for diagnosing pleural effusions was 0.91. We present proof-of-concept that a neural network can segment and derive accurate clinical measurements from a 3D model of the thorax made from transaxial anatomy images acquired in the first few minutes of a scan. This early information could lead to dynamic adaptive scanning protocols, and by focusing scanner time appropriately and prioritizing cases for supervision and early reporting, improve patient experience and efficiency.
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Affiliation(s)
- James P Howard
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK.
| | - Sameer Zaman
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Aaraby Ragavan
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Kerry Hall
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Greg Leonard
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Sharon Sutanto
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Vijay Ramadoss
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Yousuf Razvi
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Nick F Linton
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Anil Bharath
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Matthew Shun-Shin
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Daniel Rueckert
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Darrel Francis
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
| | - Graham Cole
- Department of Computing, National Heart and Lung Institute, Imperial College London, Imperial College Healthcare NHS Trust, London, UK
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Abstract
Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.
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Affiliation(s)
- Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Alliance for Cardiovascular Diagnosis and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, MD, USA 21205
| | - Dan M. Popescu
- Alliance for Cardiovascular Diagnosis and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
| | - Julie K. Shade
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Alliance for Cardiovascular Diagnosis and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
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24
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Deshpande S, Kella D, Padmanabhan D. MRI in patients with cardiac implantable electronic devices: A comprehensive review. PACING AND CLINICAL ELECTROPHYSIOLOGY: PACE 2021; 44:360-372. [DOI: 10.1111/pace.14141] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 11/20/2020] [Accepted: 11/29/2020] [Indexed: 12/19/2022]
Affiliation(s)
- Saurabh Deshpande
- Department of Cardiac Electrophysiology Sri Jayadeva Institute of Cardiovascular Sciences and Research Bangalore India
| | - Danesh Kella
- Department of Cardiology Piedmont Heart Institute Atlanta Georgia USA
| | - Deepak Padmanabhan
- Department of Cardiac Electrophysiology Sri Jayadeva Institute of Cardiovascular Sciences and Research Bangalore India
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25
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Belete SC, Batta V, Kunz H. Automated classification of total knee replacement prosthesis on plain film radiograph using a deep convolutional neural network. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100669] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
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26
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Weikert T, Francone M, Abbara S, Baessler B, Choi BW, Gutberlet M, Hecht EM, Loewe C, Mousseaux E, Natale L, Nikolaou K, Ordovas KG, Peebles C, Prieto C, Salgado R, Velthuis B, Vliegenthart R, Bremerich J, Leiner T. Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges. Eur Radiol 2020; 31:3909-3922. [PMID: 33211147 PMCID: PMC8128798 DOI: 10.1007/s00330-020-07417-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 09/01/2020] [Accepted: 10/13/2020] [Indexed: 12/31/2022]
Abstract
Abstract Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of machine learning (ML) in cardiovascular imaging. The purpose of this statement is to provide guidance on requirements for successful development and implementation of ML applications in cardiovascular imaging. In particular, recommendations on how to adequately design ML studies and how to report and interpret their results are provided. Finally, we identify opportunities and challenges ahead. While the focus of this position statement is ML development in cardiovascular imaging, most considerations are relevant to ML in radiology in general. Key Points • Development and clinical implementation of machine learning in cardiovascular imaging is a multidisciplinary pursuit. • Based on existing study quality standard frameworks such as SPIRIT and STARD, we propose a list of quality criteria for ML studies in radiology. • The cardiovascular imaging research community should strive for the compilation of multicenter datasets for the development, evaluation, and benchmarking of ML algorithms. Supplementary Information The online version contains supplementary material available at (10.1007/s00330-020-07417-0).
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Affiliation(s)
- Thomas Weikert
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
| | - Marco Francone
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, V.le Regina Elena 324, 00161, Rome, Italy
| | - Suhny Abbara
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9316, USA
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Byoung Wook Choi
- Radiology Department, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Matthias Gutberlet
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig - University Leipzig, Strümpellstrasse 39, 04289, Leipzig, Germany
| | - Elizabeth M Hecht
- Department of Radiology, Weill Cornell Medicine, 520 East 70th Street, New York, NY, 10021, USA
| | - Christian Loewe
- Division of Cardiovascular and Interventional Radiology, Department of Bioimaging and Image-Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Elie Mousseaux
- Department of Radiology, Hôpital Européen Georges Pompidou, APHP, University of Paris & INSERM, U970 29 rue Leblanc, 75015, Paris, France
| | - Luigi Natale
- Radiological and Haematological Sciences Department, Fondazione Policlinico Universitario A. Gemelli- IRCCS, Università Cattolica S. Cuore, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe-Seyler-Strasse 3, 72076, Tübingen, Germany
| | - Karen G Ordovas
- Department of Radiology and Biomedical Imaging, University of California- San Francisco, 505 Parnassus Ave, M396 Box 0628, San Francisco, CA, 94143-0628, USA
| | - Charles Peebles
- Department of Radiology, University Hospital Southampton, Tremona Road, Southampton, SO16 6YD, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, SE1 7EH, UK
| | - Rodrigo Salgado
- Department of Radiology, Antwerp University Hospital & Holy Heart Hospital Lier, Wilrijkstraat 10, 2650, Edegem, Belgium
| | - Birgitta Velthuis
- Department of Radiology, Utrecht University Medical Center, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Jens Bremerich
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Tim Leiner
- Department of Radiology, Utrecht University Medical Center, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
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27
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Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, Albus M, Sheele JM, Bellolio F, Friedman PA, Lopez-Jimenez F, Noseworthy PA. Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Circ Arrhythm Electrophysiol 2020; 13:e008437. [PMID: 32986471 DOI: 10.1161/circep.120.008437] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). METHODS We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. RESULTS A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83-0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76-0.84). CONCLUSIONS The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.
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Affiliation(s)
| | - Rickey E Carter
- Department of Health Sciences Research (R.E.C., P.J.), Mayo Clinic, Jacksonville, FL
| | - Zachi Attia
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
| | - Patrick Johnson
- Department of Health Sciences Research (R.E.C., P.J.), Mayo Clinic, Jacksonville, FL
| | | | - Jennifer L Dugan
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
| | - Michael Albus
- Department of Emergency Medicine (M.A., J.M.S.), Mayo Clinic, Jacksonville, FL
| | - Johnathan M Sheele
- Department of Emergency Medicine (M.A., J.M.S.), Mayo Clinic, Jacksonville, FL
| | | | - Paul A Friedman
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
| | - Francisco Lopez-Jimenez
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
| | - Peter A Noseworthy
- Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN
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28
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Feeny AK, Chung MK, Madabhushi A, Attia ZI, Cikes M, Firouznia M, Friedman PA, Kalscheur MM, Kapa S, Narayan SM, Noseworthy PA, Passman RS, Perez MV, Peters NS, Piccini JP, Tarakji KG, Thomas SA, Trayanova NA, Turakhia MP, Wang PJ. Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circ Arrhythm Electrophysiol 2020; 13:e007952. [PMID: 32628863 PMCID: PMC7808396 DOI: 10.1161/circep.119.007952] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.
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Affiliation(s)
- Albert K Feeny
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.K.C.), Case Western Reserve University, OH
| | - Mina K Chung
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.K.C.), Case Western Reserve University, OH
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Anant Madabhushi
- Department of Biomedical Engineering (A.M., M.F.), Case Western Reserve University, OH
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH (A.M.)
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Maja Cikes
- Department of Cardiovascular Diseases, University of Zagreb School of Medicine & University Hospital Center Zagreb, Croatia (M.C.)
| | - Marjan Firouznia
- Department of Biomedical Engineering (A.M., M.F.), Case Western Reserve University, OH
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Matthew M Kalscheur
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine & Public Health, University of Wisconsin (M.M.K.)
- William S. Middleton Veterans Hospital, Madison, WI (M.M.K.)
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Sanjiv M Narayan
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Rod S Passman
- Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, IL (R.S.P.)
| | - Marco V Perez
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| | - Nicholas S Peters
- National Heart Lung Institute & Centre for Cardiac Engineering, Imperial College London, United Kingdom (N.S.P.)
| | - Jonathan P Piccini
- Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.P.P.)
| | - Khaldoun G Tarakji
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Suma A Thomas
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD (N.A.T.)
| | - Mintu P Turakhia
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Center for Digital Health, Stanford University School of Medicine, CA (M.P.T.)
| | - Paul J Wang
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
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29
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Arnold AD, Howard JP, Gopi A, Chan CP, Ali N, Keene D, Shun-Shin MJ, Ahmad Y, Wright IJ, Ng FS, Linton NW, Kanagaratnam P, Peters NS, Rueckert D, Francis DP, Whinnett ZI. Discriminating electrocardiographic responses to His-bundle pacing using machine learning. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2020; 1:11-20. [PMID: 32954375 PMCID: PMC7484933 DOI: 10.1016/j.cvdhj.2020.07.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND His-bundle pacing (HBP) has emerged as an alternative to conventional ventricular pacing because of its ability to deliver physiological ventricular activation. Pacing at the His bundle produces different electrocardiographic (ECG) responses: selective His-bundle pacing (S-HBP), non-selective His bundle pacing (NS-HBP), and myocardium-only capture (MOC). These 3 capture types must be distinguished from each other, which can be challenging and time-consuming even for experts. OBJECTIVE The purpose of this study was to use artificial intelligence (AI) in the form of supervised machine learning using a convolutional neural network (CNN) to automate HBP ECG interpretation. METHODS We identified patients who had undergone HBP and extracted raw 12-lead ECG data during S-HBP, NS-HBP, and MOC. A CNN was trained, using 3-fold cross-validation, on 75% of the segmented QRS complexes labeled with their capture type. The remaining 25% was kept aside as a testing dataset. RESULTS The CNN was trained with 1297 QRS complexes from 59 patients. Cohen kappa for the neural network's performance on the 17-patient testing set was 0.59 (95% confidence interval 0.30 to 0.88; P <.0001), with an overall accuracy of 75%. The CNN's accuracy in the 17-patient testing set was 67% for S-HBP, 71% for NS-HBP, and 84% for MOC. CONCLUSION We demonstrated proof of concept that a neural network can be trained to automate discrimination between HBP ECG responses. When a larger dataset is trained to higher accuracy, automated AI ECG analysis could facilitate HBP implantation and follow-up and prevent complications resulting from incorrect HBP ECG analysis.
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Affiliation(s)
- Ahran D. Arnold
- Address reprint requests and correspondence: Dr Ahran D. Arnold, Hammersmith Hospital, London W12 0HS, United Kingdom.
| | | | - Aiswarya Gopi
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Cheng Pou Chan
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Nadine Ali
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Daniel Keene
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Matthew J. Shun-Shin
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Yousif Ahmad
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Ian J. Wright
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Nick W.F. Linton
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Prapa Kanagaratnam
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Nicholas S. Peters
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Daniel Rueckert
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | | | - Zachary I. Whinnett
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
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30
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Howard JP, Tan J, Shun-Shin MJ, Mahdi D, Nowbar AN, Arnold AD, Ahmad Y, McCartney P, Zolgharni M, Linton NWF, Sutaria N, Rana B, Mayet J, Rueckert D, Cole GD, Francis DP. Improving ultrasound video classification: an evaluation of novel deep learning methods in echocardiography. ACTA ACUST UNITED AC 2020; 3. [PMID: 32226937 DOI: 10.21037/jmai.2019.10.03] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Echocardiography is the commonest medical ultrasound examination, but automated interpretation is challenging and hinges on correct recognition of the 'view' (imaging plane and orientation). Current state-of-the-art methods for identifying the view computationally involve 2-dimensional convolutional neural networks (CNNs), but these merely classify individual frames of a video in isolation, and ignore information describing the movement of structures throughout the cardiac cycle. Here we explore the efficacy of novel CNN architectures, including time-distributed networks and two-stream networks, which are inspired by advances in human action recognition. We demonstrate that these new architectures more than halve the error rate of traditional CNNs from 8.1% to 3.9%. These advances in accuracy may be due to these networks' ability to track the movement of specific structures such as heart valves throughout the cardiac cycle. Finally, we show the accuracies of these new state-of-the-art networks are approaching expert agreement (3.6% discordance), with a similar pattern of discordance between views.
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Affiliation(s)
- James P Howard
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | - Jeremy Tan
- Department of Computing, Imperial College London, Hammersmith Hospital, London, UK
| | - Matthew J Shun-Shin
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | - Dina Mahdi
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | - Alexandra N Nowbar
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | - Ahran D Arnold
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | - Yousif Ahmad
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | | | - Massoud Zolgharni
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | - Nick W F Linton
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | | | - Bushra Rana
- Imperial College Healthcare NHS Trust, London, UK
| | - Jamil Mayet
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, Hammersmith Hospital, London, UK
| | - Graham D Cole
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
| | - Darrel P Francis
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK
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31
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de Marvao A, Dawes TJW, O'Regan DP. Artificial Intelligence for Cardiac Imaging-Genetics Research. Front Cardiovasc Med 2020; 6:195. [PMID: 32039240 PMCID: PMC6985036 DOI: 10.3389/fcvm.2019.00195] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/27/2019] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
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Affiliation(s)
| | | | - Declan P. O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
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