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Chadalavada S, Fung K, Rauseo E, Lee AM, Khanji MY, Amir-Khalili A, Paiva J, Naderi H, Banik S, Chirvasa M, Jensen MT, Aung N, Petersen SE. Myocardial Strain Measured by Cardiac Magnetic Resonance Predicts Cardiovascular Morbidity and Death. J Am Coll Cardiol 2024; 84:648-659. [PMID: 39111972 PMCID: PMC11320766 DOI: 10.1016/j.jacc.2024.05.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/11/2024] [Accepted: 05/07/2024] [Indexed: 08/16/2024]
Abstract
BACKGROUND Myocardial strain using cardiac magnetic resonance (CMR) is a sensitive marker for predicting adverse outcomes in many cardiac disease states, but the prognostic value in the general population has not been studied conclusively. OBJECTIVES The goal of this study was to assess the independent prognostic value of CMR feature tracking (FT)-derived LV global longitudinal (GLS), circumferential (GCS), and radial strain (GRS) metrics in predicting adverse outcomes (heart failure, myocardial infarction, stroke, and death). METHODS Participants from the UK Biobank population imaging study were included. Univariable and multivariable Cox models were used for each outcome and each strain marker (GLS, GCS, GRS) separately. The multivariable models were tested with adjustment for prognostically important clinical features and conventional global LV imaging markers relevant for each outcome. RESULTS Overall, 45,700 participants were included in the study (average age 65 ± 8 years), with a median follow-up period of 3 years. All univariable and multivariable models demonstrated that lower absolute GLS, GCS, and GRS were associated with increased incidence of heart failure, myocardial infarction, stroke, and death. All strain markers were independent predictors (incrementally above some respective conventional LV imaging markers) for the morbidity outcomes, but only GLS predicted death independently: (HR: 1.18; 95% CI: 1.07-1.30). CONCLUSIONS In the general population, LV strain metrics derived using CMR-FT in radial, circumferential, and longitudinal directions are strongly and independently predictive of heart failure, myocardial infarction, and stroke, but only GLS is independently predictive of death in an adult population cohort.
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Affiliation(s)
- Sucharitha Chadalavada
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Kenneth Fung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Elisa Rauseo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Aaron M Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Mohammed Y Khanji
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | | | - Jose Paiva
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Hafiz Naderi
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Shantanu Banik
- Circle Cardiovascular Imaging Inc, Calgary, Alberta, Canada
| | | | | | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom; Health Data Research UK, London, United Kingdom; Alan Turing Institute, The British Library, John Dodson House, London, United Kingdom.
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2
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Cheung HC, Vimalesvaran K, Zaman S, Michaelides M, Shun-Shin MJ, Francis DP, Cole GD, Howard JP. Automating quality control in cardiac magnetic resonance: Artificial intelligence for discriminative assessment of planning and motion artifacts and real-time reacquisition guidance. J Cardiovasc Magn Reson 2024; 26:101067. [PMID: 39079601 PMCID: PMC11416635 DOI: 10.1016/j.jocmr.2024.101067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 07/10/2024] [Accepted: 07/24/2024] [Indexed: 09/14/2024] Open
Abstract
BACKGROUND Accurate measurements from cardiovascular magnetic resonance (CMR) images require precise positioning of scan planes and elimination of motion artifacts from arrhythmia or breathing. Unidentified or incorrectly managed artifacts degrade image quality, invalidate clinical measurements, and decrease diagnostic confidence. Currently, radiographers must manually inspect each acquired image to confirm diagnostic quality and decide whether reacquisition or a change in sequences is warranted. We aimed to develop artificial intelligence (AI) to provide continuous quality scores across different quality domains, and from these, determine whether cines are clinically adequate, require replanning, or warrant a change in protocol. METHODS A three-dimensional convolutional neural network was trained to predict cine quality graded on a continuous scale by a level 3 CMR expert, focusing separately on planning and motion artifacts. It incorporated four distinct output heads for the assessment of image quality in terms of (a, b, c) 2-, 3- and 4-chamber misplanning, and (d) long- and short-axis arrhythmia/breathing artifact. Backpropagation was selectively performed across these heads based on the labels present for each cine. Each image in the testing set was reported by four level 3 CMR experts, providing a consensus on clinical adequacy. The AI's assessment of image quality and ability to identify images requiring replanning or sequence changes were evaluated with Spearman's rho and the area under receiver operating characteristic curve (AUROC), respectively. RESULTS A total of 1940 cines across 1387 studies were included. On the test set of 383 cines, AI-judged image quality correlated strongly with expert judgment, with Spearman's rho of 0.84, 0.84, 0.81, and 0.81 for 2-, 3- and 4-chamber planning quality and the extent of arrhythmia or breathing artifacts, respectively. The AI also showed high efficacy in flagging clinically inadequate cines (AUROC 0.88, 0.93, and 0.93 for identifying misplanning of 2-, 3- and 4-chamber cines, and 0.90 for identifying movement artifacts). CONCLUSION AI can assess distinct domains of CMR cine quality and provide continuous quality scores that correlate closely with a consensus of experts. These ratings could be used to identify cases where reacquisition is warranted and guide corrective actions to optimize image quality, including replanning, prospective gating, or real-time imaging.
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Affiliation(s)
- Hoi C Cheung
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Kavitha Vimalesvaran
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Sameer Zaman
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Michalis Michaelides
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Matthew J Shun-Shin
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Darrel P Francis
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Graham D Cole
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - James P Howard
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.
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Petmezas G, Papageorgiou VE, Vassilikos V, Pagourelias E, Tsaklidis G, Katsaggelos AK, Maglaveras N. Recent advancements and applications of deep learning in heart failure: Α systematic review. Comput Biol Med 2024; 176:108557. [PMID: 38728995 DOI: 10.1016/j.compbiomed.2024.108557] [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: 03/15/2024] [Revised: 04/12/2024] [Accepted: 05/05/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements. OBJECTIVE This review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities. METHODS A thorough literature search was conducted across four major electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore, yielding 137 articles that were subsequently categorized into five primary application areas: cardiovascular disease (CVD) classification, HF detection, image analysis, risk assessment, and other clinical analyses. The selection criteria focused on studies utilizing DL algorithms for HF assessment, not limited to HF detection but extending to any attempt in analyzing and interpreting HF-related data. RESULTS The analysis revealed a notable emphasis on CVD classification and HF detection, with DL algorithms showing significant promise in distinguishing between affected individuals and healthy subjects. Furthermore, the review highlights DL's capacity to identify underlying cardiomyopathies and other comorbidities, underscoring its utility in refining diagnostic processes and tailoring treatment plans to individual patient needs. CONCLUSIONS This review establishes DL as a key innovation in HF management, highlighting its role in advancing diagnostic accuracy and personalized care. The insights provided advocate for the integration of DL in clinical settings and suggest directions for future research to enhance patient outcomes in HF care.
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Affiliation(s)
- Georgios Petmezas
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | | | - Vasileios Vassilikos
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efstathios Pagourelias
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Tsaklidis
- Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Nicos Maglaveras
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Taskén AA, Yu J, Berg EAR, Grenne B, Holte E, Dalen H, Stølen S, Lindseth F, Aakhus S, Kiss G. Automatic Detection and Tracking of Anatomical Landmarks in Transesophageal Echocardiography for Quantification of Left Ventricular Function. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:797-804. [PMID: 38485534 DOI: 10.1016/j.ultrasmedbio.2024.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 05/01/2024]
Abstract
OBJECTIVE Evaluation of left ventricular (LV) function in critical care patients is useful for guidance of therapy and early detection of LV dysfunction, but the tools currently available are too time-consuming. To resolve this issue, we previously proposed a method for the continuous and automatic quantification of global LV function in critical care patients based on the detection and tracking of anatomical landmarks on transesophageal heart ultrasound. In the present study, our aim was to improve the performance of mitral annulus detection in transesophageal echocardiography (TEE). METHODS We investigated several state-of-the-art networks for both the detection and tracking of the mitral annulus in TEE. We integrated the networks into a pipeline for automatic assessment of LV function through estimation of the mitral annular plane systolic excursion (MAPSE), called autoMAPSE. TEE recordings from a total of 245 patients were collected from St. Olav's University Hospital and used to train and test the respective networks. We evaluated the agreement between autoMAPSE estimates and manual references annotated by expert echocardiographers in 30 Echolab patients and 50 critical care patients. Furthermore, we proposed a prototype of autoMAPSE for clinical integration and tested it in critical care patients in the intensive care unit. RESULTS Compared with manual references, we achieved a mean difference of 0.8 (95% limits of agreement: -2.9 to 4.7) mm in Echolab patients, with a feasibility of 85.7%. In critical care patients, we reached a mean difference of 0.6 (95% limits of agreement: -2.3 to 3.5) mm and a feasibility of 88.1%. The clinical prototype of autoMAPSE achieved real-time performance. CONCLUSION Automatic quantification of LV function had high feasibility in clinical settings. The agreement with manual references was comparable to inter-observer variability of clinical experts.
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Affiliation(s)
- Anders Austlid Taskén
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Jinyang Yu
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Anesthesia and Intensive Care, St. Olav's University Hospital, Trondheim, Norway
| | - Erik Andreas Rye Berg
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olav's University Hospital, Trondheim, Norway
| | - Bjørnar Grenne
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olav's University Hospital, Trondheim, Norway
| | - Espen Holte
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olav's University Hospital, Trondheim, Norway
| | - Håvard Dalen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olav's University Hospital, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Stian Stølen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olav's University Hospital, Trondheim, Norway
| | - Frank Lindseth
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway
| | - Svend Aakhus
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olav's University Hospital, Trondheim, Norway
| | - Gabriel Kiss
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway
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Yu J, Taskén AA, Flade HM, Skogvoll E, Berg EAR, Grenne B, Rimehaug A, Kirkeby-Garstad I, Kiss G, Aakhus S. Automatic assessment of left ventricular function for hemodynamic monitoring using artificial intelligence and transesophageal echocardiography. J Clin Monit Comput 2024; 38:281-291. [PMID: 38280975 DOI: 10.1007/s10877-023-01118-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/03/2023] [Indexed: 01/29/2024]
Abstract
We have developed a method to automatically assess LV function by measuring mitral annular plane systolic excursion (MAPSE) using artificial intelligence and transesophageal echocardiography (autoMAPSE). Our aim was to evaluate autoMAPSE as an automatic tool for rapid and quantitative assessment of LV function in critical care patients. In this retrospective study, we studied 40 critical care patients immediately after cardiac surgery. First, we recorded a set of echocardiographic data, consisting of three consecutive beats of midesophageal two- and four-chamber views. We then altered the patient's hemodynamics by positioning them in anti-Trendelenburg and repeated the recordings. We measured MAPSE manually and used autoMAPSE in all available heartbeats and in four LV walls. To assess the agreement with manual measurements, we used a modified Bland-Altman analysis. To assess the precision of each method, we calculated the least significant change (LSC). Finally, to assess trending ability, we calculated the concordance rates using a four-quadrant plot. We found that autoMAPSE measured MAPSE in almost every set of two- and four-chamber views (feasibility 95%). It took less than a second to measure and average MAPSE over three heartbeats. AutoMAPSE had a low bias (0.4 mm) and acceptable limits of agreement (- 3.7 to 4.5 mm). AutoMAPSE was more precise than manual measurements if it averaged more heartbeats. AutoMAPSE had acceptable trending ability (concordance rate 81%) during hemodynamic alterations. In conclusion, autoMAPSE is feasible as an automatic tool for rapid and quantitative assessment of LV function, indicating its potential for hemodynamic monitoring.
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Affiliation(s)
- Jinyang Yu
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
- Clinic of Cardiology St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
| | - Anders Austlid Taskén
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Hans Martin Flade
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Anesthesia and Intensive Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Eirik Skogvoll
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Anesthesia and Intensive Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Erik Andreas Rye Berg
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Cardiology St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Bjørnar Grenne
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Cardiology St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Audun Rimehaug
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Anesthesia and Intensive Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Idar Kirkeby-Garstad
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Gabriel Kiss
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Svend Aakhus
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Cardiology St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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Christodoulou AG, Cruz G, Arami A, Weingärtner S, Artico J, Peters D, Seiberlich N. The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1). J Cardiovasc Magn Reson 2024; 26:100997. [PMID: 38237900 PMCID: PMC11211239 DOI: 10.1016/j.jocmr.2024.100997] [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: 12/21/2023] [Accepted: 01/10/2024] [Indexed: 02/26/2024] Open
Abstract
Cardiovascular magnetic resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR. To this end, the vision of each is described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 will tackle the "all-in-one" approaches, and Part 2 the "real-time" approaches along with an overall summary of these emerging methods.
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Affiliation(s)
- Anthony G Christodoulou
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Gastao Cruz
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Ayda Arami
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | | | - Dana Peters
- Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Nicole Seiberlich
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
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Lindow T, Maanja M, Schelbert EB, Ribeiro AH, Ribeiro ALP, Schlegel TT, Ugander M. Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:384-392. [PMID: 37794867 PMCID: PMC10545529 DOI: 10.1093/ehjdh/ztad045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/05/2023] [Indexed: 10/06/2023]
Abstract
Aims Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age. Methods and results Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7-6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13-1.34) and adjusted HR 1.11 (1.01-1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01-1.21)], but not in unadjusted analyses [HR 1.00 (0.93-1.08)], making it less easily applicable in clinical practice. Conclusion A-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods.
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Affiliation(s)
- Thomas Lindow
- Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia
- Department of Clinical Physiology, Research and Development, Växjö Central Hospital, Region Kronoberg, Sweden
- Clinical Physiology, Clinical Sciences, Lund University, Sweden
| | - Maren Maanja
- Department of Clinical Physiology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | | | - Antônio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Antonio Luiz P Ribeiro
- Telehealth Center, Hospital das Clínicas, and Internal Medicine Department, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Todd T Schlegel
- Department of Clinical Physiology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
- Nicollier-Schlegel SARL, Trélex, Switzerland
| | - Martin Ugander
- Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia
- Department of Clinical Physiology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
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8
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Yoon SS, Fischer C, Amsel D, Monzon M, Toupin S, Pezel T, Garot J, Wetzl J, Maier A, Giese D. Fully automated AI-based cardiac motion parameter extraction - application to mitral and tricuspid valves on long-axis cine MR images. Eur J Radiol 2023; 166:110978. [PMID: 37517314 DOI: 10.1016/j.ejrad.2023.110978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 05/07/2023] [Accepted: 07/12/2023] [Indexed: 08/01/2023]
Abstract
PURPOSE In cardiac MRI, valve motion parameters can be useful for the diagnosis of cardiac dysfunction. In this study, a fully automated AI-based valve tracking system was developed and evaluated on 2- or 4-chamber view cine series on a large cardiac MR dataset. Automatically derived motion parameters include atrioventricular plane displacement (AVPD), velocities (AVPV), mitral or tricuspid annular plane systolic excursion (MAPSE, TAPSE), or longitudinal shortening (LS). METHOD Two sequential neural networks with an intermediate processing step are applied to localize the target and track the landmarks throughout the cardiac cycle. Initially, a localisation network is used to perform heatmap regression of the target landmarks, such as mitral, tricuspid valve annulus as well as apex points. Then, a registration network is applied to track these landmarks using deformation fields. Based on these outputs, motion parameters were derived. RESULTS The accuracy of the system resulted in deviations of 1.44 ± 1.32 mm, 1.51 ± 1.46 cm/s, 2.21 ± 1.81 mm, 2.40 ± 1.97 mm, 2.50 ± 2.06 mm for AVPD, AVPV, MAPSE, TAPSE and LS, respectively. Application on a large patient database (N = 5289) revealed a mean MAPSE and LS of 9.5 ± 3.0 mm and 15.9 ± 3.9 % on 2-chamber and 4-chamber views, respectively. A mean TAPSE and LS of 13.4 ± 4.7 mm and 21.4 ± 6.9 % was measured. CONCLUSION The results demonstrate the versatility of the proposed system for automatic extraction of various valve-related motion parameters.
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Affiliation(s)
- Seung Su Yoon
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany.
| | - Carola Fischer
- Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany; Technische Universität Berlin, Germany
| | - Daniel Amsel
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Maria Monzon
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | - Théo Pezel
- Institut Cardiovasculaire Paris Sud, Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques CARTIER, Ramsay Santé, Massy, France; Université de Paris Cité, Service de Cardiologie, Hôpital Lariboisière - APHP, Inserm UMRS 942, 75010 Paris, France
| | - Jérôme Garot
- Institut Cardiovasculaire Paris Sud, Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques CARTIER, Ramsay Santé, Massy, France
| | - Jens Wetzl
- Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
| | - Andreas Maier
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Daniel Giese
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
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9
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Berg J, Åkesson J, Jablonowski R, Solem K, Heiberg E, Borgquist R, Arheden H, Carlsson M. Ventricular longitudinal function by cardiovascular magnetic resonance predicts cardiovascular morbidity in HFrEF patients. ESC Heart Fail 2022; 9:2313-2324. [PMID: 35411699 PMCID: PMC9288769 DOI: 10.1002/ehf2.13916] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/16/2022] [Accepted: 03/14/2022] [Indexed: 11/23/2022] Open
Abstract
Aims Ventricular longitudinal function measured as basal‐apical atrioventricular plane displacement (AVPD) or global longitudinal strain (GLS) is a potent predictor of mortality and could potentially be a predictor of heart failure‐associated morbidity. We hypothesized that low AVPD and GLS are associated with the combined endpoint of cardiovascular mortality and heart failure‐associated morbidity. Methods and results Two hundred eighty‐seven patients (age 62 ± 12 years, 78% male) with heart failure with reduced (≤40%) ejection fraction (HFrEF) referred to a cardiovascular magnetic resonance exam were included. Ventricular longitudinal function, ventricular volume, and myocardial fibrosis or infarction were analysed from cine and late gadolinium enhancement images. National registries provided data on causes of cardiovascular hospitalizations and cardiovascular mortality for the combined endpoint. Time‐to‐event analysis capable of including reoccurring events was employed with a 5‐year follow‐up. HFrEF patients had EF 26.5 ± 8.0%, AVPD 7.8 ± 2.4 mm, and GLS −7.5 ± 3.0%. In contrast, ventricular longitudinal function was approximately twice as large in an age‐matched control group (AVPD 15.3 ± 1.6 mm; GLS −20.6 ± 2.0%; P < 0.001 for both). There were 578 events in total, and the majority were HF hospitalizations (n = 418). Other major events were revascularizations (n = 64), cardiovascular deaths (n = 40), and myocardial infarctions (n = 21). One hundred fifty‐five (54%) patients experienced at least one event (mean 2.0, range 0–64). Of these patients, 119 (71%) had three events or fewer, and the first three events comprised 51% of all events (295 events). Patients in the bottom AVPD or GLS tertile (<6.8 mm or >−6.1%) overall experienced more than 3 times as many events as the top tertile (>8.8 mm or <−8.4%; P < 0.001). Patients in this tertile also faced more cardiovascular deaths (P < 0.05), HF hospitalizations (P = 0.001), myocardial infarctions (only GLS: P = 0.032), and accumulated longer in‐hospital length‐of‐stay overall (AVPD 20.9 vs. 9.1 days; GLS 22.4 vs. 6.5 days; P = 0.001 for both), and from HF hospitalizations (AVPD 19.3 vs. 8.3 days; GLS 19.3 vs. 5.4 days; P = 0.001 for both). In multivariate analysis adjusted for significant covariates, AVPD and GLS remained independent predictors of events (hazard ratio 1.12 per‐mm‐decrease and 1.13 per‐%‐increase) alongside hyponatremia (<135 mmol/L), aetiology of HF, and LV end‐diastolic volume index. Conclusions Low ventricular longitudinal function is associated with an increase in number of events as well as longer in‐hospital stay from cardiovascular causes. In addition, AVPD and GLS have independent prognostic value for cardiovascular mortality and morbidity in HFrEF patients.
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Affiliation(s)
- Jonathan Berg
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden.,Syntach AB, Lund, Sweden
| | - Julius Åkesson
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Robert Jablonowski
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | | | - Einar Heiberg
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Rasmus Borgquist
- Cardiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Håkan Arheden
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Marcus Carlsson
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden.,Laboratory of Clinical Physiology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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