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Kuwahara A, Iwasaki Y, Kobayashi M, Takagi R, Yamada S, Kubo T, Satomi K, Tanaka N. Artificial intelligence-derived left ventricular strain in echocardiography in patients treated with chemotherapy. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024:10.1007/s10554-024-03178-9. [PMID: 39042233 DOI: 10.1007/s10554-024-03178-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 06/28/2024] [Indexed: 07/24/2024]
Abstract
Global longitudinal strain (GLS) is an echocardiographic measure to detect chemotherapy-related cardiovascular dysfunction. However, its limited availability and the needed expertise may restrict its generalization. Artificial intelligence (AI)-based GLS might overcome these challenges. Our aims are to explore the agreements between AI-based GLS and conventional GLS, and to assess whether the agreements were influenced by expertise levels, cardiac remodeling and cardiovascular diseases/risks. Echocardiographic images in the apical four-chamber view of left ventricle were retrospectively analyzed based on AI-based GLS in patients treated with chemotherapy, and correlation between AI-based GLS (Caas Qardia, Pie Medical Imaging) and conventional GLS (Vivid E9/VividE95, GE Healthcare) were assessed. The agreement between unexperienced physicians ("GLS beginner") and experienced echocardiographer were also assessed. Among 94 patients (mean age 69 ± 12 years, 73% female), mean left ventricular ejection fraction was 64 ± 6%, 14% of patients had left ventricular hypertrophy, and 21% had left atrial enlargement. Mean GLS was - 15.9 ± 3.4% and - 19.0 ± 3.7% for the AI and conventional method, respectively. There was a moderate correlation between these methods (rho = 0.74; p < 0.01), and bias was - 3.1% (95% limits of agreement: -8.1 to 2.0). The reproducibility between GLS beginner and an experienced echocardiographer was numerically better in the AI method than the conventional method (inter-observer agreement = 0.82 vs. 0.68). The agreements were consistent across abnormal cardiac structure and function categories (p-for-interaction > 0.10). In patients treated with chemotherapy. AI-based GLS was moderately correlated with conventional GLS and provided a numerically better reproducibility compared with conventional GLS, regardless of different levels of expertise.
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Affiliation(s)
- Asuka Kuwahara
- Department of Cardiology, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan
| | - Yoichi Iwasaki
- Department of Cardiology, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan
| | - Masatake Kobayashi
- Department of Cardiology, Tokyo Medical University Hospital, 6-7-1, Nishi-shinjuku, Shinjuku, Tokyo, Japan.
| | - Ryu Takagi
- Department of Cardiology, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan
| | - Satoshi Yamada
- Department of Cardiology, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan
| | - Takashi Kubo
- Department of Cardiology, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan
| | - Kazuhiro Satomi
- Department of Cardiology, Tokyo Medical University Hospital, 6-7-1, Nishi-shinjuku, Shinjuku, Tokyo, Japan
| | - Nobuhiro Tanaka
- Department of Cardiology, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan
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Lovstakken L, Grenne B. The Road to Robust and Automated Strain Measurements in Echocardiography by Deep Learning. JACC Cardiovasc Imaging 2024; 17:726-728. [PMID: 38613555 DOI: 10.1016/j.jcmg.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 02/29/2024] [Indexed: 04/15/2024]
Affiliation(s)
- Lasse Lovstakken
- Norwegian University of Science and Technology, and St Olavs University Hospital, Trondheim, Norway.
| | - Bjørnar Grenne
- Norwegian University of Science and Technology, and St Olavs University Hospital, Trondheim, Norway
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Kwan AC, Chang EW, Jain I, Theurer J, Tang X, Francisco N, Haddad F, Liang D, Fábián A, Ferencz A, Yuan N, Merkely B, Siegel R, Cheng S, Kovács A, Tokodi M, Ouyang D. Deep Learning-Derived Myocardial Strain. JACC Cardiovasc Imaging 2024; 17:715-725. [PMID: 38551533 DOI: 10.1016/j.jcmg.2024.01.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 01/11/2024] [Accepted: 01/17/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Echocardiographic strain measurements require extensive operator experience and have significant intervendor variability. Creating an automated, open-source, vendor-agnostic method to retrospectively measure global longitudinal strain (GLS) from standard echocardiography B-mode images would greatly improve post hoc research applications and may streamline patient analyses. OBJECTIVES This study was seeking to develop an automated deep learning strain (DLS) analysis pipeline and validate its performance across multiple applications and populations. METHODS Interobserver/-vendor variation of traditional GLS, and simulated effects of variation in contour on speckle-tracking measurements were assessed. The DLS pipeline was designed to take semantic segmentation results from EchoNet-Dynamic and derive longitudinal strain by calculating change in the length of the left ventricular endocardial contour. DLS was evaluated for agreement with GLS on a large external dataset and applied across a range of conditions that result in cardiac hypertrophy. RESULTS In patients scanned by 2 sonographers using 2 vendors, GLS had an intraclass correlation of 0.29 (95% CI: -0.01 to 0.53, P = 0.03) between vendor measurements and 0.63 (95% CI: 0.48-0.74, P < 0.001) between sonographers. With minor changes in initial input contour, step-wise pixel shifts resulted in a mean absolute error of 3.48% and proportional strain difference of 13.52% by a 6-pixel shift. In external validation, DLS maintained moderate agreement with 2-dimensional GLS (intraclass correlation coefficient [ICC]: 0.56, P = 0.002) with a bias of -3.31% (limits of agreement: -11.65% to 5.02%). The DLS method showed differences (P < 0.0001) between populations with cardiac hypertrophy and had moderate agreement in a patient population of advanced cardiac amyloidosis: ICC was 0.64 (95% CI: 0.53-0.72), P < 0.001, with a bias of 0.57%, limits of agreement of -4.87% to 6.01% vs 2-dimensional GLS. CONCLUSIONS The open-source DLS provides lower variation than human measurements and similar quantitative results. The method is rapid, consistent, vendor-agnostic, publicly released, and applicable across a wide range of imaging qualities.
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Affiliation(s)
- Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
| | - Ernest W Chang
- Leon H. Charney Division of Cardiology, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA
| | - Ishan Jain
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - John Theurer
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Xiu Tang
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Nadia Francisco
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Francois Haddad
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - David Liang
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Alexandra Fábián
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Andrea Ferencz
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Neal Yuan
- Division of Cardiology, Department of Medicine, San Francisco VA, University of California-San Francisco, San Francisco, California, USA
| | - Béla Merkely
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Robert Siegel
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Attila Kovács
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary; Department of Surgical Research and Techniques, Semmelweis University, Budapest, Hungary
| | - Márton Tokodi
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary; Department of Surgical Research and Techniques, Semmelweis University, Budapest, Hungary
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
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Yu J, Taskén AA, Berg EAR, Tannvik TD, Slagsvold KH, Kirkeby-Garstad I, Grenne B, Kiss G, Aakhus S. Continuous monitoring of left ventricular function in postoperative intensive care patients using artificial intelligence and transesophageal echocardiography. Intensive Care Med Exp 2024; 12:54. [PMID: 38856861 PMCID: PMC11164841 DOI: 10.1186/s40635-024-00640-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 06/04/2024] [Indexed: 06/11/2024] Open
Abstract
BACKGROUND Continuous monitoring of mitral annular plane systolic excursion (MAPSE) using transesophageal echocardiography (TEE) may improve the evaluation of left ventricular (LV) function in postoperative intensive care patients. We aimed to assess the utility of continuous monitoring of LV function using TEE and artificial intelligence (autoMAPSE) in postoperative intensive care patients. METHODS In this prospective observational study, we monitored 50 postoperative intensive care patients for 120 min immediately after cardiac surgery. We recorded a set of two-chamber and four-chamber TEE images every five minutes. We defined monitoring feasibility as how often the same wall from the same patient could be reassessed, and categorized monitoring feasibility as excellent if the same LV wall could be reassessed in ≥ 90% of the total recordings. To compare autoMAPSE with manual measurements, we rapidly recorded three sets of repeated images to assess precision (least significant change), bias, and limits of agreement (LOA). To assess the ability to identify changes (trending ability), we compared changes in autoMAPSE with the changes in manual measurements in images obtained during the initiation of cardiopulmonary bypass as well as before and after surgery. RESULTS Monitoring feasibility was excellent in most patients (88%). Compared with manual measurements, autoMAPSE was more precise (least significant change 2.2 vs 3.1 mm, P < 0.001), had low bias (0.4 mm), and acceptable agreement (LOA - 2.7 to 3.5 mm). AutoMAPSE had excellent trending ability, as its measurements changed in the same direction as manual measurements (concordance rate 96%). CONCLUSION Continuous monitoring of LV function was feasible using autoMAPSE. Compared with manual measurements, autoMAPSE had excellent trending ability, low bias, acceptable agreement, and was more precise.
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Affiliation(s)
- Jinyang Yu
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Postboks 8905, 7491, Trondheim, Norway.
- Clinic of Cardiology St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
| | - Anders Austlid Taskén
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Erik Andreas Rye Berg
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Postboks 8905, 7491, Trondheim, Norway
- Clinic of Cardiology St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Tomas Dybos Tannvik
- Department of Anesthesia and Intensive Care, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Katrine Hordnes Slagsvold
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Postboks 8905, 7491, Trondheim, Norway
- Clinic of Cardiothoracic Surgery, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Idar Kirkeby-Garstad
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Postboks 8905, 7491, Trondheim, Norway
| | - Bjørnar Grenne
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Postboks 8905, 7491, Trondheim, Norway
- Clinic of Cardiology St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
- Centre of Innovative Ultrasound Solutions, Trondheim, Norway
| | - Gabriel Kiss
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Svend Aakhus
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Postboks 8905, 7491, Trondheim, Norway
- Clinic of Cardiology St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
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Grenne B, Østvik A. Beyond Years: Is Artificial Intelligence Ready to Predict Biological Age and Cardiovascular Risk Using Echocardiography? J Am Soc Echocardiogr 2024:S0894-7317(24)00263-3. [PMID: 38797330 DOI: 10.1016/j.echo.2024.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 05/18/2024] [Accepted: 05/20/2024] [Indexed: 05/29/2024]
Affiliation(s)
- Bjørnar Grenne
- Clinic of Cardiology, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Andreas Østvik
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway
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Chen L, Huang SH, Wang TH, Tseng VS, Tsao HM, Tang GJ. Automatic 3D left atrial strain extraction framework on cardiac computed tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 252:108236. [PMID: 38776829 DOI: 10.1016/j.cmpb.2024.108236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND AND OBJECTIVE Strain analysis provides insights into myocardial function and cardiac condition evaluation. However, the anatomical characteristics of left atrium (LA) inherently limit LA strain analysis when using echocardiography. Cardiac computed tomography (CT) with its superior spatial resolution, has become critical for in-depth evaluation of LA function. Recent studies have explored the feasibility of CT-derived strain; however, they relied on manually selected regions of interest (ROIs) and mainly focused on left ventricle (LV). This study aimed to propose a first-of-its-kind fully automatic deep learning (DL)-based framework for three-dimensional (3D) LA strain extraction on cardiac CT. METHODS A total of 111 patients undergoing ECG-gated contrast-enhanced CT for evaluating subclinical atrial fibrillation (AF) were enrolled in this study. We developed a 3D strain extraction framework on cardiac CT images, containing a 2.5D GN-U-Net network for LA segmentation, axis-oriented 3D view extraction, and LA strain measure. The segmentation accuracy was evaluated using Dice similarity coefficient (DSC). The model-extracted LA volumes and emptying fraction (EF) were compared with ground-truth measurements using intraclass correlation coefficient (ICC), correlation coefficient (r), and Bland-Altman plot (B-A). The automatically extracted LA strains were evaluated against the LA strains measured from 2D echocardiograms. We utilized this framework to gauge the effect of AF burden on LA strain, employing the atrial high rate episode (AHRE) burden as the measurement parameter. RESULTS The GN-U-Net LA segmentation network achieved a DSC score of 0.9603 on the test set. The framework-extracted LA estimates demonstrated excellent ICCs of 0.949 (95 % CI: 0.93-0.97) for minimal LA volume, 0.904 (95 % CI: 0.86-0.93) for maximal LA volume, and 0.902 (95 % CI: 0.86-0.93) for EF, compared with expert measurements. The framework-extracted LA strains demonstrated moderate agreement with the LA strains based on 2D echocardiography (ICCs >0.703). Patients with AHRE > 6 min had significantly lower global strain and LAEF, as extracted by the framework than those with AHRE ≤ 6 min. CONCLUSION The promising results highlighted the feasibility and clinical usefulness of automatically extracting 3D LA strain from CT images using a DL-based framework. This tool could provide a 3D-based alternative to echocardiography for assessing LA function.
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Affiliation(s)
- Ling Chen
- Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sung-Hao Huang
- Division of Cardiology, Department of Internal Medicine, National Yang Ming Chiao Tung University Hospital, No. 169, Xiao-She Road, Yilan, Taiwan.
| | - Tzu-Hsiang Wang
- Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Vincent S Tseng
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Hsuan-Ming Tsao
- Division of Cardiology, Department of Internal Medicine, National Yang Ming Chiao Tung University Hospital, No. 169, Xiao-She Road, Yilan, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Gau-Jun Tang
- Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan
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Makimoto H. Bridging the gap: AI in echocardiography for early detection of LVH in underserved areas. Hypertens Res 2024; 47:1434-1435. [PMID: 38438729 DOI: 10.1038/s41440-024-01608-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 01/20/2024] [Indexed: 03/06/2024]
Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
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Serrano RA, Smeltz AM. The Promise of Artificial Intelligence-Assisted Point-of-Care Ultrasonography in Perioperative Care. J Cardiothorac Vasc Anesth 2024; 38:1244-1250. [PMID: 38402063 DOI: 10.1053/j.jvca.2024.01.034] [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: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 02/26/2024]
Abstract
The role of point-of-care ultrasonography in the perioperative setting has expanded rapidly over recent years. Revolutionizing this technology further is integrating artificial intelligence to assist clinicians in optimizing images, identifying anomalies, performing automated measurements and calculations, and facilitating diagnoses. Artificial intelligence can increase point-of-care ultrasonography efficiency and accuracy, making it an even more valuable point-of-care tool. Given this topic's importance and ever-changing landscape, this review discusses the latest trends to serve as an introduction and update in this area.
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Affiliation(s)
| | - Alan M Smeltz
- University of North Carolina School of Medicine, Chapel Hill, NC
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9
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Naser JA, Lee E, Pislaru SV, Tsaban G, Malins JG, Jackson JI, Anisuzzaman DM, Rostami B, Lopez-Jimenez F, Friedman PA, Kane GC, Pellikka PA, Attia ZI. Artificial intelligence-based classification of echocardiographic views. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:260-269. [PMID: 38774376 PMCID: PMC11104471 DOI: 10.1093/ehjdh/ztae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 05/24/2024]
Abstract
Aims Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.
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Affiliation(s)
- Jwan A Naser
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Eunjung Lee
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Sorin V Pislaru
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Gal Tsaban
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Jeffrey G Malins
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - John I Jackson
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - D M Anisuzzaman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Behrouz Rostami
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Garvan C Kane
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Patricia A Pellikka
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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Huang KC, Lin DSH, Jeng GS, Lin TT, Lin LY, Lee CK, Lin LC. Left Ventricular Segmentation, Warping, and Myocardial Registration for Automated Strain Measurement. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01119-5. [PMID: 38639806 DOI: 10.1007/s10278-024-01119-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 04/20/2024]
Abstract
The left ventricular global longitudinal strain (LVGLS) is a crucial prognostic indicator. However, inconsistencies in measurements due to the speckle tracking algorithm and manual adjustments have hindered its standardization and democratization. To solve this issue, we proposed a fully automated strain measurement by artificial intelligence-assisted LV segmentation contours. The LV segmentation model was trained from echocardiograms of 368 adults (11,125 frames). We compared the registration-like effects of dynamic time warping (DTW) with speckle tracking on a synthetic echocardiographic dataset in experiment-1. In experiment-2, we enrolled 80 patients to compare the DTW method with commercially available software. In experiment-3, we combined the segmentation model and DTW method to create the artificial intelligence (AI)-DTW method, which was then tested on 40 patients with general LV morphology, 20 with dilated cardiomyopathy (DCMP), and 20 with transthyretin-associated cardiac amyloidosis (ATTR-CA), 20 with severe aortic stenosis (AS), and 20 with severe mitral regurgitation (MR). Experiments-1 and -2 revealed that the DTW method is consistent with dedicated software. In experiment-3, the AI-DTW strain method showed comparable results for general LV morphology (bias - 0.137 ± 0.398%), DCMP (- 0.397 ± 0.607%), ATTR-CA (0.095 ± 0.581%), AS (0.334 ± 0.358%), and MR (0.237 ± 0.490%). Moreover, the strain curves showed a high correlation in their characteristics, with R-squared values of 0.8879-0.9452 for those LV morphology in experiment-3. Measuring LVGLS through dynamic warping of segmentation contour is a feasible method compared to traditional tracking techniques. This approach has the potential to decrease the need for manual demarcation and make LVGLS measurements more efficient and user-friendly for daily practice.
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Affiliation(s)
- Kuan-Chih Huang
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- National Taiwan University Hospital, Hsin-Chu branch, Hsinchu, Taiwan
| | - Donna Shu-Han Lin
- Division of Cardiology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
| | - Geng-Shi Jeng
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Ting-Tse Lin
- Section of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Lian-Yu Lin
- Section of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chih-Kuo Lee
- National Taiwan University Hospital, Hsin-Chu branch, Hsinchu, Taiwan
| | - Lung-Chun Lin
- Section of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
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11
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Cheng M, Wang J, Liu X, Wang Y, Wu Q, Wang F, Li P, Wang B, Zhang X, Xie W. Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms. RESEARCH (WASHINGTON, D.C.) 2024; 7:0319. [PMID: 38455153 PMCID: PMC10919123 DOI: 10.34133/research.0319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/26/2024] [Indexed: 03/09/2024]
Abstract
Early detection and treatment of congenital heart disease (CHD) can significantly improve the prognosis of children. However, inexperienced sonographers often face difficulties in recognizing CHD through transthoracic echocardiogram (TTE) images. In this study, 2-dimensional (2D) and Doppler TTEs of children collected from 2 clinical groups from Beijing Children's Hospital between 2018 and 2022 were analyzed, including views of apical 4 chamber, subxiphoid long-axis view of 2 atria, parasternal long-axis view of the left ventricle, parasternal short-axis view of aorta, and suprasternal long-axis view. A deep learning (DL) framework was developed to identify cardiac views, integrate information from various views and modalities, visualize the high-risk region, and predict the probability of the subject being normal or having an atrial septal defect (ASD) or a ventricular septaldefect (VSD). A total of 1,932 children (1,255 healthy controls, 292 ASDs, and 385 VSDs) were collected from 2 clinical groups. For view classification, the DL model reached a mean [SD] accuracy of 0.989 [0.001]. For CHD screening, the model using both 2D and Doppler TTEs with 5 views achieved a mean [SD] area under the receiver operating characteristic curve (AUC) of 0.996 [0.000] and an accuracy of 0.994 [0.002] for within-center evaluation while reaching a mean [SD] AUC of 0.990 [0.003] and an accuracy of 0.993 [0.001] for cross-center test set. For the classification of healthy, ASD, and VSD, the model reached the mean [SD] accuracy of 0.991 [0.002] and 0.986 [0.001] for within- and cross-center evaluation, respectively. The DL models aggregating TTEs with more modalities and scanning views attained superior performance to approximate that of experienced sonographers. The incorporation of multiple views and modalities of TTEs in the model enables accurate identification of children with CHD in a noninvasive manner, suggesting the potential to enhance CHD detection performance and simplify the screening process.
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Affiliation(s)
- Mingmei Cheng
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Department of Psychology, School of Mental Health and Psychological Sciences,
Anhui Medical University, Hefei 230011, China
| | - Jing Wang
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
- School of Basic Medical Sciences, Capital Medical University, Beijing 10069, China
| | - Xiaofeng Liu
- Gordon Center for Medical Imaging, Harvard Medical School, and Massachusetts General Hospital, Boston, MA 02114, USA
| | - Yanzhong Wang
- School of Life Course and Population Sciences, Faculty of Life Science and Medicine, King’s College London, London, UK
| | - Qun Wu
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
| | - Fangyun Wang
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
| | - Pei Li
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
| | - Binbin Wang
- Center for Genetics,
National Research Institute for Family Planning, Beijing 100730, China
- Graduated School,
Peking Union Medical College, Beijing 100730, China
| | - Xin Zhang
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
| | - Wanqing Xie
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Department of Psychology, School of Mental Health and Psychological Sciences,
Anhui Medical University, Hefei 230011, China
- Beth Israel Deaconess Medical Center, Harvard Medical School,
Harvard University, Boston, MA 02215, USA
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12
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Lopez L, Saurers DL, Barker PCA, Cohen MS, Colan SD, Dwyer J, Forsha D, Friedberg MK, Lai WW, Printz BF, Sachdeva R, Soni-Patel NR, Truong DT, Young LT, Altman CA. Guidelines for Performing a Comprehensive Pediatric Transthoracic Echocardiogram: Recommendations From the American Society of Echocardiography. J Am Soc Echocardiogr 2024; 37:119-170. [PMID: 38309834 DOI: 10.1016/j.echo.2023.11.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2024]
Abstract
Echocardiography is a fundamental component of pediatric cardiology, and appropriate indications have been established for its use in the setting of suspected, congenital, or acquired heart disease in children. Since the publication of guidelines for pediatric transthoracic echocardiography in 2006 and 2010, advances in knowledge and technology have expanded the scope of practice beyond the use of traditional modalities such as two-dimensional, M-mode, and Doppler echocardiography to evaluate the cardiac segmental structures and their function. Adjunct modalities such as contrast, three-dimensional, and speckle-tracking echocardiography are now used routinely at many pediatric centers. Guidelines and recommendations for the use of traditional and newer adjunct modalities in children are described in detail in this document. In addition, suggested protocols related to standard operations, infection control, sedation, and quality assurance and improvement are included to provide an organizational structure for centers performing pediatric transthoracic echocardiograms.
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Affiliation(s)
- Leo Lopez
- Department of Pediatrics Cardiology, Stanford University School of Medicine and Lucile Packard Children's Hospital Stanford, Palo Alto, California.
| | - Daniel L Saurers
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Piers C A Barker
- Duke Children's Hospital & Health Center, Duke University, Durham, North Carolina
| | - Meryl S Cohen
- Cardiac Center and Division of Cardiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Steven D Colan
- Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts
| | - Jeanine Dwyer
- Pediatric Heart Institute, Children's Hospital Colorado, Aurora, Colorado
| | - Daniel Forsha
- Ward Family Heart Center, Children's Mercy Kansas City Hospital, Kansas City, Missouri
| | - Mark K Friedberg
- Labatt Family Heart Centre, Division of Cardiology, The Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada
| | - Wyman W Lai
- Division of Pediatric Cardiology, University of California School of Medicine, Irvine, California; Department of Pediatrics, Children's Hospital of Orange County, Orange, California
| | - Beth F Printz
- Rady Children's Hospital San Diego and University of California, San Diego, San Diego, California
| | - Ritu Sachdeva
- Department of Pediatrics, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, Georgia
| | - Neha R Soni-Patel
- Pediatric & Adult Congenital Heart Center, Cleveland Clinic Children's Hospital, Cleveland, Ohio
| | - Dongngan T Truong
- University of Utah and Division of Pediatric Cardiology, Primary Children's Hospital, Salt Lake City, Utah
| | - Luciana T Young
- Seattle Children's Hospital and Pediatric Cardiology, University of Washington School of Medicine, Seattle, Washington
| | - Carolyn A Altman
- Baylor College of Medicine and Texas Children's Heart Center, Texas Children's Hospital, Houston, Texas
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13
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Sveric KM, Ulbrich S, Dindane Z, Winkler A, Botan R, Mierke J, Trausch A, Heidrich F, Linke A. Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging. Int J Cardiol 2024; 394:131383. [PMID: 37757986 DOI: 10.1016/j.ijcard.2023.131383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/10/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Left ventricular ejection fraction (LVEF) measurement in echocardiography (Echo) using the recommended modified biplane Simpson (MBS) method is operator-dependent and exhibits variability. We aimed to assess the accuracy of a novel fully automated (Auto) artificial intelligence (AI) in view selection and biplane LVEF calculation compared to MBS-Echo, with cardiac magnetic resonance imaging (CMR) as reference. METHODS Each of the 301 consecutive patients underwent CMR and Echo on the same day. LVEF was measured independently by Auto-Echo, MBS-Echo and CMR. Interobserver (n = 40) and test-retest (n = 14) analysis followed. RESULTS A total of 229 patients (76%) underwent complete analysis. Auto-Echo and MBS-Echo showed high correlations with CMR (R = 0.89 and 0.89) and with each other (R = 0.93). Auto underestimated LVEF (bias: 2.2%; limits of agreement [LOA]: -13.5 to 17.9%), while MBS overestimated it (bias: -2.2%; LOA: 18.6 to 14.1%). Despite comparable areas under the curves of Auto- and MBS-Echo (0.93 and 0.92), 46% (n = 70) of MBS-Echo misclassified LVEF by ≥5% units in patients with a reduced CMR-LVEF <51%. Although LVEF bias variability across different LV function ranges was significant (p < 0.001), Auto-Echo was closer to CMR for patients with reduced LVEF, wall motion abnormalities, and poor image quality than MBS-Echo. The interobserver correlation coefficient of Auto-Echo was excellent compared to MBS-Echo (1.00 vs. <0.91) for different readers. True test-retest variability was higher for MBS-Echo than for Auto-Echo (7.9% vs. 2.5%). CONCLUSION The tested AI has the potential to improve the clinical utility of Echo by reducing user-related variability, providing more accurate and reliable results than MBS.
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Affiliation(s)
- Krunoslav Michael Sveric
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany.
| | - Stefan Ulbrich
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Zouhir Dindane
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Anna Winkler
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Roxana Botan
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Johannes Mierke
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Anne Trausch
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Felix Heidrich
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Axel Linke
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
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14
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Myhre PL, Hung CL, Frost MJ, Jiang Z, Ouwerkerk W, Teramoto K, Svedlund S, Saraste A, Hage C, Tan RS, Beussink-Nelson L, Fermer ML, Gan LM, Hummel YM, Lund LH, Shah SJ, Lam CSP, Tromp J. External validation of a deep learning algorithm for automated echocardiographic strain measurements. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:60-68. [PMID: 38264705 PMCID: PMC10802824 DOI: 10.1093/ehjdh/ztad072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/08/2023] [Accepted: 10/12/2023] [Indexed: 01/25/2024]
Abstract
Aims Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging. Methods and results We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myocardial infarction. Outcomes included measures of agreement [bias, mean absolute difference (MAD), root-mean-squared-error (RMSE), and Pearson's correlation (R)] and area under the curve (AUC) to identify HF and regional wall-motion abnormalities. The DL workflow successfully analysed 3741 (89%) studies in the Taiwanese cohort, 176 (96%) in PROMIS-HFpEF, and 158 (98%) in HMC-QU-MI. Automated GLS showed good agreement with manual measurements (mean ± SD): -18.9 ± 4.5% vs. -18.2 ± 4.4%, respectively, bias 0.68 ± 2.52%, MAD 2.0 ± 1.67, RMSE = 2.61, R = 0.84 in the Taiwanese cohort; and -15.4 ± 4.1% vs. -15.9 ± 3.6%, respectively, bias -0.65 ± 2.71%, MAD 2.19 ± 1.71, RMSE = 2.78, R = 0.76 in PROMIS-HFpEF. In the Taiwanese cohort, automated GLS accurately identified patients with HF (AUC = 0.89 for total HF and AUC = 0.98 for HF with reduced ejection fraction). In HMC-QU-MI, automated regional strain identified regional wall-motion abnormalities with an average AUC = 0.80. Conclusion DL algorithms can interpret echocardiographic strain images with similar accuracy as conventional measurements. These results highlight the potential of DL algorithms to democratize the use of cardiac strain measurements and reduce time-spent and costs for echo labs globally.
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Affiliation(s)
- Peder L Myhre
- Division of Medicine, Akershus University Hospital, Lørenskog, Norway
- K.G. Jebsen Center of Cardiac Biomarkers, University of Oslo, Oslo, Norway
| | - Chung-Lieh Hung
- Division of Cardiology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
- Institute of Biomedical Sciences, MacKay Medical College, New Taipei, Taiwan
| | | | | | - Wouter Ouwerkerk
- National Heart Centre Singapore, Singapore, Singapore
- Department of Dermatology, Amsterdam Institute for Infection and Immunity, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Kanako Teramoto
- Department of Biostatistics, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Sara Svedlund
- Department of Clinical Physiology, Institute of Medicine, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
- Ribocure Pharmaceuticals AB/Ribo Life Science Co Ltd, Gothenburg, Sweden
| | - Antti Saraste
- Heart Center, Turku University Hospital, University of Turku, Turku, Finland
| | - Camilla Hage
- Department of Cardiology, Heart, Vascular and Neuro Theme, Karolinska University Hospital, Stockholm, Sweden
- Department of Medicine, Cardiology Unit, Karolinska Institutet, Stockholm, Sweden
| | - Ru-San Tan
- National Heart Centre Singapore, Singapore, Singapore
| | - Lauren Beussink-Nelson
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Maria L Fermer
- Early Clinical Development, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Li-Ming Gan
- Ribocure Pharmaceuticals AB/Ribo Life Science Co Ltd, Gothenburg, Sweden
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | | | - Lars H Lund
- Department of Cardiology, Heart, Vascular and Neuro Theme, Karolinska University Hospital, Stockholm, Sweden
| | - Sanjiv J Shah
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Carolyn S P Lam
- National Heart Centre Singapore, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Jasper Tromp
- National Heart Centre Singapore, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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15
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Johnson MD, Zimmerman KG, Nakashima T, Urrea KA, Rojas-Pena A, Bartlett RH, Drake DH. Artificial Intelligence-Assisted Strain Echocardiography in an Ex Vivo Heart. ASAIO J 2023; 69:e523-e525. [PMID: 37524082 DOI: 10.1097/mat.0000000000001994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2023] Open
Affiliation(s)
- Matthew D Johnson
- From the Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan
| | - Karen G Zimmerman
- Department of Cardiology, Henry Ford Health System, Detroit, Michigan
| | - Takahiro Nakashima
- From the Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan
| | - Kristopher A Urrea
- From the Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan
| | - Alvaro Rojas-Pena
- From the Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan
| | - Robert H Bartlett
- From the Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan
| | - Daniel H Drake
- Department of Cardiac Surgery, University of Michigan Medical School, Ann Arbor, Michigan
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16
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Tokodi M, Kovács A. A New Hope for Deep Learning-Based Echocardiogram Interpretation: The DROIDs You Were Looking For. J Am Coll Cardiol 2023; 82:1949-1952. [PMID: 37940232 DOI: 10.1016/j.jacc.2023.09.799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 09/13/2023] [Indexed: 11/10/2023]
Affiliation(s)
- Márton Tokodi
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
| | - Attila Kovács
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary. https://twitter.com/kovatti87
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17
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Jiang J, Liu B, Li YW, Hothi SS. Clinical service evaluation of the feasibility and reproducibility of novel artificial intelligence based-echocardiographic quantification of global longitudinal strain and left ventricular ejection fraction in trastuzumab-treated patients. Front Cardiovasc Med 2023; 10:1250311. [PMID: 38045908 PMCID: PMC10693341 DOI: 10.3389/fcvm.2023.1250311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/16/2023] [Indexed: 12/05/2023] Open
Abstract
Introduction Cardiotoxicity is a potential prognostically important complication of certain chemotherapeutic agents that may result in preclinical or overt clinical heart failure. In some cases, chemotherapy must be withheld when left ventricular (LV) systolic function becomes significantly impaired, to protect cardiac function at the expense of a change in the oncological treatment plan, leading to associated changes in oncological prognosis. Accordingly, patients receiving potentially cardiotoxic chemotherapy undergo routine surveillance before, during and following completion of therapy, usually with transthoracic echocardiography (TTE). Recent advancements in AI-based cardiac imaging reveal areas of promise but key challenges remain. There are ongoing questions as to whether the ability of AI to detect subtle changes in individual patients is at a level equivalent to manual analysis. This raises the question as to whether AI-based left ventricular strain analysis could provide a potential solution to left ventricular systolic function analysis in a manner equivocal to or superior to conventional assessment, in a real-world clinical service. AI based automated analyses may represent a potential solution for addressing the pressure of increasing echocardiographic demands within limited service-capacity healthcare systems, in addition to facilitating more accurate diagnoses. Methods This clinical service evaluation aims to establish whether AI-automated analysis compared to conventional methods (1) is a feasible method for assessing LV-GLS and LVEF, (2) yields moderate to good correlation between the two approaches, and (3) would lead to different clinical recommendations with serial surveillance in a real-world clinical population. Results and Discussion We observed a moderate correlation (r = 0.541) in GLS between AI automated assessment compared to conventional methods. The LVEF quantification between methods demonstrated a strong correlation (r = 0.895). AI-generated GLS and LVEF values compared reasonably well with conventional methods, demonstrating a similar temporal pattern throughout echocardiographic surveillance. The apical-three chamber view demonstrated the lowest correlation (r = 0.423) and revealed to be least successful for acquisition of GLS and LVEF. Compared to conventional methodology, AI-automated analysis has a significantly lower feasibility rate, demonstrating a success rate of 14% (GLS) and 51% (LVEF).
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Affiliation(s)
- J. Jiang
- Heart and Lung Centre, New Cross Hospital, Royal Wolverhampton NHS Trust, Wolverhampton, United Kingdom
| | - B. Liu
- Department of Cardiology, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Y. W. Li
- Department of Anaesthesia, New Cross Hospital, Royal Wolverhampton NHS Trust, Wolverhampton, United Kingdom
| | - S. S. Hothi
- Heart and Lung Centre, New Cross Hospital, Royal Wolverhampton NHS Trust, Wolverhampton, United Kingdom
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, United Kingdom
- Research Centre for Health and Life Sciences, Coventry University, Coventry, United Kingdom
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18
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Vasile CM, Iriart X. Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve. Diagnostics (Basel) 2023; 13:3137. [PMID: 37835880 PMCID: PMC10572870 DOI: 10.3390/diagnostics13193137] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/26/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs' diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.
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Affiliation(s)
- Corina Maria Vasile
- Department of Pediatric and Adult Congenital Cardiology, Bordeaux University Hospital, 33600 Pessac, France
| | - Xavier Iriart
- Department of Pediatric and Adult Congenital Cardiology, Bordeaux University Hospital, 33600 Pessac, France
- IHU Liryc—Electrophysiology and Heart Modelling Institute, Bordeaux University Foundation, 33600 Pessac, France
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19
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Ositelu K, Trevino A, Tong A, Chen MH, Akhter N. Challenges in Cardiovascular Imaging in Women with Breast Cancer. Curr Cardiol Rep 2023; 25:1247-1255. [PMID: 37642930 DOI: 10.1007/s11886-023-01941-3] [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] [Accepted: 08/11/2023] [Indexed: 08/31/2023]
Abstract
Cardiovascular imaging in breast cancer patients is paramount for the surveillance of cancer therapy-related cardiac dysfunction (CTRCD); however, it comes with specific limitations. PURPOSE OF REVIEW: This review aims to describe the unique challenges faced in cardiovascular imaging of breast cancer patients, discuss evidence to support the utility of various imaging modalities, and provide solutions for improvement in imaging this unique population. RECENT FINDINGS: Updated clinical society guidelines have introduced more unifying surveillance of CTRCD, although there remains a lack of a universally accepted definition. Traditional and novel multi-modality imaging can be used to detect CTRCD and myocarditis in breast cancer patients. Cardiovascular imaging in breast cancer patients is difficult due to reconstructive surgery. Although echocardiography with myocardial strain is the cornerstone, multi-modality imaging can be used to evaluate for CTRCD and myocarditis. Novel imaging techniques to improve the diagnosis of cardiotoxicities in breast cancer patients are needed.
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Affiliation(s)
- Kamari Ositelu
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 N. St. Clair Street, Suite 600, Chicago, IL, USA
| | - Alexandra Trevino
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ann Tong
- The Cardiac & Vascular Institute, Gainesville, FL, USA
| | - Ming Hui Chen
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Nausheen Akhter
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 N. St. Clair Street, Suite 600, Chicago, IL, USA.
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20
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Sengupta PP, Chandrashekhar Y. From Conventional Deep Learning to GPT: AI's Emergent Power for Cardiac Imaging. JACC Cardiovasc Imaging 2023; 16:1129-1131. [PMID: 37558359 DOI: 10.1016/j.jcmg.2023.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
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21
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Daniels LJ, Macindoe C, Koutsifeli P, Annandale M, James SL, Watson LE, Coffey S, Raaijmakers AJA, Weeks KL, Bell JR, Janssens JV, Curl CL, Delbridge LMD, Mellor KM. Myocardial deformation imaging by 2D speckle tracking echocardiography for assessment of diastolic dysfunction in murine cardiopathology. Sci Rep 2023; 13:12344. [PMID: 37524893 PMCID: PMC10390581 DOI: 10.1038/s41598-023-39499-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
Diastolic dysfunction is increasingly identified as a key, early onset subclinical condition characterizing cardiopathologies of rising prevalence, including diabetic heart disease and heart failure with preserved ejection fraction (HFpEF). Diastolic dysfunction characterization has important prognostic value in management of disease outcomes. Validated tools for in vivo monitoring of diastolic function in rodent models of diabetes are required for progress in pre-clinical cardiology studies. 2D speckle tracking echocardiography has emerged as a powerful tool for evaluating cardiac wall deformation throughout the cardiac cycle. The aim of this study was to examine the applicability of 2D speckle tracking echocardiography for comprehensive global and regional assessment of diastolic function in a pre-clinical murine model of cardio-metabolic disease. Type 2 diabetes (T2D) was induced in C57Bl/6 male mice using a high fat high sugar dietary intervention for 20 weeks. Significant impairment in left ventricle peak diastolic strain rate was evident in longitudinal, radial and circumferential planes in T2D mice. Peak diastolic velocity was similarly impaired in the longitudinal and radial planes. Regional analysis of longitudinal peak diastolic strain rate revealed that the anterior free left ventricular wall is particularly susceptible to T2D-induced diastolic dysfunction. These findings provide a significant advance on characterization of diastolic dysfunction in a pre-clinical mouse model of cardiopathology and offer a comprehensive suite of benchmark values for future pre-clinical cardiology studies.
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Affiliation(s)
- L J Daniels
- Cellular and Molecular Cardiology Laboratory, Department of Physiology, University of Auckland, Auckland, New Zealand
- Radcliffe Department of Medicine, OCDEM, University of Oxford, Oxford, UK
| | - C Macindoe
- Cellular and Molecular Cardiology Laboratory, Department of Physiology, University of Auckland, Auckland, New Zealand
| | - P Koutsifeli
- Cellular and Molecular Cardiology Laboratory, Department of Physiology, University of Auckland, Auckland, New Zealand
| | - M Annandale
- Cellular and Molecular Cardiology Laboratory, Department of Physiology, University of Auckland, Auckland, New Zealand
| | - S L James
- Cellular and Molecular Cardiology Laboratory, Department of Physiology, University of Auckland, Auckland, New Zealand
| | - L E Watson
- Cellular and Molecular Cardiology Laboratory, Department of Physiology, University of Auckland, Auckland, New Zealand
| | - S Coffey
- Department of Medicine, University of Otago, Dunedin, New Zealand
| | - A J A Raaijmakers
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
| | - K L Weeks
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - J R Bell
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
- Department of Microbiology, Anatomy, Physiology and Pharmacology, La Trobe University, Melbourne, Australia
| | - J V Janssens
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
| | - C L Curl
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
| | - L M D Delbridge
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
| | - Kimberley M Mellor
- Cellular and Molecular Cardiology Laboratory, Department of Physiology, University of Auckland, Auckland, New Zealand.
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia.
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
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22
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Peng GJ, Luo SY, Zhong XF, Lin XX, Zheng YQ, Xu JF, Liu YY, Chen LX. Feasibility and reproducibility of semi-automated longitudinal strain analysis: a comparative study with conventional manual strain analysis. Cardiovasc Ultrasound 2023; 21:12. [PMID: 37464361 DOI: 10.1186/s12947-023-00309-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 06/11/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Conventional approach to myocardial strain analysis relies on a software designed for the left ventricle (LV) which is complex and time-consuming and is not specific for right ventricular (RV) and left atrial (LA) assessment. This study compared this conventional manual approach to strain evaluation with a novel semi-automatic analysis of myocardial strain, which is also chamber-specific. METHODS Two experienced observers used the AutoStrain software and manual QLab analysis to measure the LV, RV and LA strains in 152 healthy volunteers. Fifty cases were randomly selected for timing evaluation. RESULTS No significant differences in LV global longitudinal strain (LVGLS) were observed between the two methods (-21.0% ± 2.5% vs. -20.8% ± 2.4%, p = 0.230). Conversely, RV longitudinal free wall strain (RVFWS) and LA longitudinal strain during the reservoir phase (LASr) measured by the semi-automatic software differed from the manual analysis (RVFWS: -26.4% ± 4.8% vs. -31.3% ± 5.8%, p < 0.001; LAS: 48.0% ± 10.0% vs. 37.6% ± 9.9%, p < 0.001). Bland-Altman analysis showed a mean error of 0.1%, 4.9%, and 10.5% for LVGLS, RVFWS, and LASr, respectively, with limits of agreement of -2.9,2.6%, -8.1,17.9%, and -12.3,33.3%, respectively. The semi-automatic method had a significantly shorter strain analysis time compared with the manual method. CONCLUSIONS The novel semi-automatic strain analysis has the potential to improve efficiency in measurement of longitudinal myocardial strain. It shows good agreement with manual analysis for LV strain measurement.
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Affiliation(s)
- Gui-Juan Peng
- Shenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Shu-Yu Luo
- Shenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Xiao-Fang Zhong
- Shenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Xiao-Xuan Lin
- Shenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Ying-Qi Zheng
- Shenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Jin-Feng Xu
- Shenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China.
| | - Ying-Ying Liu
- Shenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China.
| | - Li-Xin Chen
- Shenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China.
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23
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Krishna H, Desai K, Slostad B, Bhayani S, Arnold JH, Ouwerkerk W, Hummel Y, Lam CSP, Ezekowitz J, Frost M, Jiang Z, Equilbec C, Twing A, Pellikka PA, Frazin L, Kansal M. Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography. J Am Soc Echocardiogr 2023; 36:769-777. [PMID: 36958708 DOI: 10.1016/j.echo.2023.03.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/07/2023] [Accepted: 03/10/2023] [Indexed: 03/25/2023]
Abstract
BACKGROUND Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75 years and above. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is time-consuming and requires expert sonographic and interpretation capabilities to yield accurate results. Artificial intelligence (AI) technology has emerged as a useful tool to address these limitations but has not yet been applied in a fully hands-off manner to evaluate AS. Here, we correlate artificial neural network measurements of key hemodynamic AS parameters to experienced human reader assessment. METHODS Two-dimensional and Doppler echocardiographic images from patients with normal aortic valves and all degrees of AS were analyzed by an artificial neural network (Us2.ai) with no human input to measure key variables in AS assessment. Trained echocardiographers blinded to AI data performed manual measurements of these variables, and correlation analyses were performed. RESULTS Our cohort included 256 patients with an average age of 67.6 ± 9.5 years. Across all AS severities, AI closely matched human measurement of aortic valve peak velocity (r = 0.97, P < .001), mean pressure gradient (r = 0.94, P < .001), aortic valve area by continuity equation (r = 0.88, P < .001), stroke volume index (r = 0.79, P < .001), left ventricular outflow tract velocity-time integral (r = 0.89, P < .001), aortic valve velocity-time integral (r = 0.96, P < .001), and left ventricular outflow tract diameter (r = 0.76, P < .001). CONCLUSIONS Artificial neural networks have the capacity to closely mimic human measurement of all relevant parameters in the adjudication of AS severity. Application of this AI technology may minimize interscan variability, improve interpretation and diagnosis of AS, and allow for precise and reproducible identification and management of patients with AS.
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Affiliation(s)
- Hema Krishna
- Division of Cardiology, University of Illinois at Chicago, Chicago, Illinois; Jesse Brown VA Medical Center, Chicago, Illinois
| | - Kevin Desai
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois
| | - Brody Slostad
- Bluhm Cardiovascular Institute, Northwestern University, Chicago, Illinois
| | - Siddharth Bhayani
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois
| | - Joshua H Arnold
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois
| | - Wouter Ouwerkerk
- National Heart Centre Singapore, Singapore; Department of Dermatology, Amsterdam UMC, Amsterdam, Netherlands
| | | | - Carolyn S P Lam
- National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Justin Ezekowitz
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada
| | | | | | | | - Aamir Twing
- Division of Cardiology, University of Illinois at Chicago, Chicago, Illinois
| | | | - Leon Frazin
- Division of Cardiology, University of Illinois at Chicago, Chicago, Illinois; Jesse Brown VA Medical Center, Chicago, Illinois
| | - Mayank Kansal
- Division of Cardiology, University of Illinois at Chicago, Chicago, Illinois; Jesse Brown VA Medical Center, Chicago, Illinois.
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24
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Salte IM, Østvik A, Olaisen SH, Karlsen S, Dahlslett T, Smistad E, Eriksen-Volnes TK, Brunvand H, Haugaa KH, Edvardsen T, Dalen H, Lovstakken L, Grenne B. Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study. J Am Soc Echocardiogr 2023; 36:788-799. [PMID: 36933849 DOI: 10.1016/j.echo.2023.02.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/20/2023]
Abstract
AIMS Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudinal strain (GLS) and may improve the clinical utility of echocardiography by reducing user-related variability. The aim of this study was to assess within-patient test-retest reproducibility of LV GLS measured by the novel AI method in repeated echocardiograms recorded by different echocardiographers and to compare the results to manual measurements. METHODS Two test-retest data sets (n = 40 and n = 32) were obtained at separate centers. Repeated recordings were acquired in immediate succession by 2 different echocardiographers at each center. For each data set, 4 readers measured GLS in both recordings using a semiautomatic method to construct test-retest interreader and intrareader scenarios. Agreement, mean absolute difference, and minimal detectable change (MDC) were compared to analyses by AI. In a subset of 10 patients, beat-to-beat variability in 3 cardiac cycles was assessed by 2 readers and AI. RESULTS Test-retest variability was lower with AI compared with interreader scenarios (data set I: MDC = 3.7 vs 5.5, mean absolute difference = 1.4 vs 2.1, respectively; data set II: MDC = 3.9 vs 5.2, mean absolute difference = 1.6 vs 1.9, respectively; all P < .05). There was bias in GLS measurements in 13 of 24 test-retest interreader scenarios (largest bias, 3.2 strain units). In contrast, there was no bias in measurements by AI. Beat-to-beat MDCs were 1,5, 2.1, and 2.3 for AI and the 2 readers, respectively. Processing time for analyses of GLS by the AI method was 7.9 ± 2.8 seconds. CONCLUSION A fast AI method for automated measurements of LV GLS reduced test-retest variability and removed bias between readers in both test-retest data sets. By improving the precision and reproducibility, AI may increase the clinical utility of echocardiography.
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Affiliation(s)
- Ivar M Salte
- Department of Medicine, Hospital of Southern Norway, Kristiansand, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Andreas Østvik
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway
| | - Sindre H Olaisen
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sigve Karlsen
- Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Medicine, Hospital of Southern Norway, Arendal, Norway
| | - Thomas Dahlslett
- Department of Medicine, Hospital of Southern Norway, Arendal, Norway
| | - Erik Smistad
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway
| | - Torfinn K Eriksen-Volnes
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway
| | - Harald Brunvand
- Department of Medicine, Hospital of Southern Norway, Arendal, Norway
| | - Kristina H Haugaa
- Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway; Faculty of Medicine, Karolinska Institutet and Cardiovascular Division, Karolinska University Hospital, Stockholm, Sweden
| | - Thor Edvardsen
- Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Håvard Dalen
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Lasse Lovstakken
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bjørnar Grenne
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway.
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25
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Daae AS, Wigen MS, Halvorsrød MI, Løvstakken L, Støylen A, Fadnes S. Retrospective Ultrasound Doppler Quantification Using a Single Acquisition in Healthy Adults. ULTRASOUND IN MEDICINE & BIOLOGY 2023:S0301-5629(23)00146-1. [PMID: 37301662 DOI: 10.1016/j.ultrasmedbio.2023.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 04/25/2023] [Accepted: 04/30/2023] [Indexed: 06/12/2023]
Abstract
OBJECTIVE Using an experimental tool for retrospective ultrasound Doppler quantification-with high temporal resolution and large spatial coverage-simultaneous flow and tissue measurements were obtained. We compared and validated these experimental values against conventional measurements to determine if the experimental acquisition produced trustworthy tissue and flow velocities. METHODS We included 21 healthy volunteers. The only exclusion criterion was the presence of an irregular heartbeat. Two ultrasound examinations were performed for each participant, one using conventional and one using experimental acquisition. The experimental acquisition used multiple plane wave emissions combined with electrocardiography stitching to obtain continuous data with over 3500 frames per second. With two recordings covering a biplane apical view of the left ventricle, we retrospectively extracted selected flow and tissue velocities. RESULTS Flow and tissue velocities were compared between the two acquisitions. Statistical testing showed a small but significant difference. We also exemplified the possibility of extracting spectral tissue Doppler from different sample volumes in the myocardium within the imaging sector, showing a decrease in the velocities from the base to the apex. CONCLUSION This study demonstrates the feasibility of simultaneous, retrospective spectral and color Doppler of both tissue and flow from an experimental acquisition covering a full sector width. The measurements were significantly different between the two acquisitions but were still comparable, as the biases were small compared to clinical practice, and the two acquisitions were not done simultaneously. The experimental acquisition also enabled the study of deformation by simultaneous spectral velocity traces from all regions of the image sector.
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Affiliation(s)
- Annichen Søyland Daae
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Department of Cardiology, St. Olav Hospital/Trondheim University Hospital, Trondheim, Norway.
| | - Morten Smedsrud Wigen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marlene Iversen Halvorsrød
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Department of Cardiology, St. Olav Hospital/Trondheim University Hospital, Trondheim, Norway
| | - Lasse Løvstakken
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Asbjørn Støylen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Department of Cardiology, St. Olav Hospital/Trondheim University Hospital, Trondheim, Norway
| | - Solveig Fadnes
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Møre og Romsdal Hospital Trust, Women's Health, Child and Adolescent Clinic, Ålesund Hospital, Ålesund, Norway
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26
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Ledziński Ł, Grześk G. Artificial Intelligence Technologies in Cardiology. J Cardiovasc Dev Dis 2023; 10:jcdd10050202. [PMID: 37233169 DOI: 10.3390/jcdd10050202] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/27/2023] Open
Abstract
As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.
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Affiliation(s)
- Łukasz Ledziński
- Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland
| | - Grzegorz Grześk
- Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland
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27
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Sabo S, Pettersen HN, Smistad E, Pasdeloup D, Stølen SB, Grenne BL, Lovstakken L, Holte E, Dalen H. Real-time guiding by deep learning during echocardiography to reduce left ventricular foreshortening and measurement variability. EUROPEAN HEART JOURNAL. IMAGING METHODS AND PRACTICE 2023; 1:qyad012. [PMID: 39044792 PMCID: PMC11195768 DOI: 10.1093/ehjimp/qyad012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/20/2023] [Indexed: 07/25/2024]
Abstract
Aims Apical foreshortening leads to an underestimation of left ventricular (LV) volumes and an overestimation of LV ejection fraction and global longitudinal strain. Real-time guiding using deep learning (DL) during echocardiography to reduce foreshortening could improve standardization and reduce variability. We aimed to study the effect of real-time DL guiding during echocardiography on measures of LV foreshortening and inter-observer variability. Methods and results Patients (n = 88) in sinus rhythm referred for echocardiography without indication for contrast were included. All participants underwent three echocardiograms. The first two examinations were performed by sonographers, and the third by cardiologists. In Period 1, the sonographers were instructed to provide high-quality echocardiograms. In Period 2, the DL guiding was used by the second sonographer. One blinded expert measured LV length in all recordings. Tri-plane recordings by cardiologists were used as reference. Apical foreshortening was calculated at the end-diastole. Both sonographer groups significantly foreshortened the LV in Period 1 (mean foreshortening: Sonographer 1: 4 mm; Sonographer 2: 3 mm, both P < 0.001 vs. reference) and reduced foreshortening in Period 2 (2 and 0 mm, respectively. Period 1 vs. Period 2, P < 0.05). Sonographers using DL guiding did not foreshorten more than cardiologists (P ≥ 0.409). Real-time guiding did not improve intra-class correlation (ICC) [LV end-diastolic volume ICC, (95% confidence interval): DL guiding 0.87 (0.77-0.93) vs. no guiding 0.92 (0.88-0.95)]. Conclusion Real-time guiding reduced foreshortening among experienced operators and has the potential to improve image standardization. Even though the effect on inter-operator variability was minimal among experienced users, real-time guiding may improve test-retest variability among less experienced users. Clinical trial registration ClinicalTrials.gov, Identifier: NCT04580095.
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Affiliation(s)
- Sigbjorn Sabo
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NTNU, Box 8905, 7491 Trondheim, Norway
| | - Hakon Neergaard Pettersen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NTNU, Box 8905, 7491 Trondheim, Norway
- Department of Internal Medicine, Kristiansund Hospital, More and Romsdal Hospital Trust, Herman Døhlens vei 1, 6508 Kristiansund, Norway
| | - Erik Smistad
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NTNU, Box 8905, 7491 Trondheim, Norway
- Sintef Digital, Box 4760 Torgarden, 7465 Trondheim, Norway
| | - David Pasdeloup
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NTNU, Box 8905, 7491 Trondheim, Norway
| | - Stian Bergseng Stølen
- Clinic of Cardiology, St Olavs University Hospital, Box 3250 Torgarden, 7006 Trondheim, Norway
| | - Bjørnar Leangen Grenne
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NTNU, Box 8905, 7491 Trondheim, Norway
- Clinic of Cardiology, St Olavs University Hospital, Box 3250 Torgarden, 7006 Trondheim, Norway
| | - Lasse Lovstakken
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NTNU, Box 8905, 7491 Trondheim, Norway
| | - Espen Holte
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NTNU, Box 8905, 7491 Trondheim, Norway
- Clinic of Cardiology, St Olavs University Hospital, Box 3250 Torgarden, 7006 Trondheim, Norway
| | - Havard Dalen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NTNU, Box 8905, 7491 Trondheim, Norway
- Clinic of Cardiology, St Olavs University Hospital, Box 3250 Torgarden, 7006 Trondheim, Norway
- Department of Internal Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Kirkegata 2, 7601 Levanger, Norway
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28
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Krittanawong C, Omar AMS, Narula S, Sengupta PP, Glicksberg BS, Narula J, Argulian E. Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review. Life (Basel) 2023; 13:life13041029. [PMID: 37109558 PMCID: PMC10145844 DOI: 10.3390/life13041029] [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: 02/17/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.
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Affiliation(s)
- Chayakrit Krittanawong
- Cardiology Division, NYU Langone Health, NYU School of Medicine, New York, NY 10016, USA
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
| | - Alaa Mabrouk Salem Omar
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Sukrit Narula
- Department of Medicine, Yale School of Medicine, New Haven, CT 06512, USA
| | - Partho P Sengupta
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Edgar Argulian
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
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29
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Sveric KM, Botan R, Dindane Z, Winkler A, Nowack T, Heitmann C, Schleußner L, Linke A. Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography. Diagnostics (Basel) 2023; 13:diagnostics13071298. [PMID: 37046515 PMCID: PMC10093353 DOI: 10.3390/diagnostics13071298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 03/31/2023] Open
Abstract
Left ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based, commercially available and ready-to use-artificial intelligence (AI) application based on convolutional neural network methods that integrate fully automatic view selection and measurement of LVEF from an entire Echo exam into a single workflow. We prospectively enrolled 1083 consecutive patients who had been referred to Echo for diagnostic or therapeutic purposes. LVEF was measured independently using MBS and AI. Test–retest variability was assessed in 40 patients. The reliability, repeatability, and time efficiency of LVEF measurements were compared between the two methods. Overall, 889 Echos were analyzed by cardiologists with the MBS method and by the AI. Over the study period of 10 weeks, the feasibility of both automatic view classification and seamlessly measured LVEF rose to 81% without user involvement. LVEF, LV end-diastolic and end-systolic volumes correlated strongly between MBS and AI (R = 0.87, 0.89 and 0.93, p < 0.001 for all) with a mean bias of +4.5% EF, −12 mL and −11 mL, respectively, due to impaired image quality and the extent of LV function. Repeatability and reliability of LVEF measurement (n = 40, test–retest) by AI was excellent compared to MBS (coefficient of variation: 3.2% vs. 5.9%), although the median analysis time of the AI was longer than that of the operator-dependent MBS method (258 s vs. 171 s). This AI has succeeded in identifying apical LV views and measuring EF in one workflow with comparable results to the MBS method and shows excellent reproducibility. It offers realistic perspectives for fully automated AI-based measurement of LVEF in routine clinical settings.
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30
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Yoshida Y, Jin Z, Russo C, Homma S, Nakanishi K, Ito K, Mannina C, Elkind MSV, Rundek T, Yoshita M, DeCarli C, Wright CB, Sacco RL, Di Tullio MR. Subclinical left ventricular systolic dysfunction and incident stroke in the elderly: long-term findings from Cardiovascular Abnormalities and Brain Lesions. Eur Heart J Cardiovasc Imaging 2023; 24:522-531. [PMID: 35900282 PMCID: PMC10226754 DOI: 10.1093/ehjci/jeac145] [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] [Received: 04/28/2022] [Revised: 07/06/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
AIMS Heart disease is associated with an increased risk for ischaemic stroke. However, the predictive value of reduced left ventricular ejection fraction (LVEF) for stroke is controversial and only observed in patients with severe reduction. LV global longitudinal strain (LV GLS) can detect subclinical LV systolic impairment when LVEF is normal. We investigated the prognostic role of LV GLS for incident stroke in a predominantly elderly cohort. METHODS AND RESULTS Two-dimensional echocardiography with speckle tracking was performed in the Cardiac Abnormalities and Brain Lesions (CABL) study. Among 708 stroke-free participants (mean age 71.4 ± 9.4 years, 60.9% women), abnormal LV GLS (>-14.7%: 95% percentile of the subgroup without risk factors) was detected in 133 (18.8%). During a mean follow-up of 10.8 ± 3.9 years, 47 participants (6.6%) experienced an ischaemic stroke (26 cardioembolic or cryptogenic, 21 other subtypes). The cumulative incidence of ischaemic stroke was significantly higher in participants with abnormal LV GLS than with normal LV GLS (P < 0.001). In multivariate stepwise logistic regression analysis, abnormal LV GLS was associated with ischaemic stroke independently of cardiovascular risk factors including LVEF, LV mass, left atrial volume, subclinical cerebrovascular disease at baseline, and incident atrial fibrillation [hazard ratio (HR): 2.69, 95% confidence interval (CI): 1.47-4.92; P = 0.001]. Abnormal LV GLS independently predicted cardioembolic or cryptogenic stroke (adjusted HR: 3.57, 95% CI: 1.51-8.43; P = 0.004) but not other subtypes. CONCLUSION LV GLS was a strong independent predictor of ischaemic stroke in a predominantly elderly stroke-free cohort. Our findings provide insights into the brain-heart interaction and may help improve stroke primary prevention strategies.
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Affiliation(s)
- Yuriko Yoshida
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Zhezhen Jin
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Cesare Russo
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Shunichi Homma
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Koki Nakanishi
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Kazato Ito
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Carlo Mannina
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Mitchell S V Elkind
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Tatjana Rundek
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Mitsuhiro Yoshita
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Charles DeCarli
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Clinton B Wright
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Ralph L Sacco
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
| | - Marco R Di Tullio
- Department of Medicine, Division of Cardiology, Columbia university Irving Medical Center, 630 W 168th St, New York, NY 10032, USA
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Hadjidimitriou S, Pagourelias E, Apostolidis G, Dimaridis I, Charisis V, Bakogiannis C, Hadjileontiadis L, Vassilikos V. Clinical Validation of an Artificial Intelligence-Based Tool for Automatic Estimation of Left Ventricular Ejection Fraction and Strain in Echocardiography: Protocol for a Two-Phase Prospective Cohort Study. JMIR Res Protoc 2023; 12:e44650. [PMID: 36912875 PMCID: PMC10131996 DOI: 10.2196/44650] [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: 11/30/2022] [Revised: 01/16/2023] [Accepted: 01/24/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Echocardiography (ECHO) is a type of ultrasonographic procedure for examining the cardiac function and morphology, with functional parameters of the left ventricle (LV), such as the ejection fraction (EF) and global longitudinal strain (GLS), being important indicators. Estimation of LV-EF and LV-GLS is performed either manually or semiautomatically by cardiologists and requires a nonnegligible amount of time, while estimation accuracy depends on scan quality and the clinician's experience in ECHO, leading to considerable measurement variability. OBJECTIVE The aim of this study is to externally validate the clinical performance of a trained artificial intelligence (AI)-based tool that automatically estimates LV-EF and LV-GLS from transthoracic ECHO scans and to produce preliminary evidence regarding its utility. METHODS This is a prospective cohort study conducted in 2 phases. ECHO scans will be collected from 120 participants referred for ECHO examination based on routine clinical practice in the Hippokration General Hospital, Thessaloniki, Greece. During the first phase, 60 scans will be processed by 15 cardiologists of different experience levels and the AI-based tool to determine whether the latter is noninferior in LV-EF and LV-GLS estimation accuracy (primary outcomes) compared to cardiologists. Secondary outcomes include the time required for estimation and Bland-Altman plots and intraclass correlation coefficients to assess measurement reliability for both the AI and cardiologists. In the second phase, the rest of the scans will be examined by the same cardiologists with and without the AI-based tool to primarily evaluate whether the combination of the cardiologist and the tool is superior in terms of correctness of LV function diagnosis (normal or abnormal) to the cardiologist's routine examination practice, accounting for the cardiologist's level of ECHO experience. Secondary outcomes include time to diagnosis and the system usability scale score. Reference LV-EF and LV-GLS measurements and LV function diagnoses will be provided by a panel of 3 expert cardiologists. RESULTS Recruitment started in September 2022, and data collection is ongoing. The results of the first phase are expected to be available by summer 2023, while the study will conclude in May 2024, with the end of the second phase. CONCLUSIONS This study will provide external evidence regarding the clinical performance and utility of the AI-based tool based on prospectively collected ECHO scans in the routine clinical setting, thus reflecting real-world clinical scenarios. The study protocol may be useful to investigators conducting similar research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/44650.
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Affiliation(s)
- Stelios Hadjidimitriou
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efstathios Pagourelias
- Third Cardiology Department, Hippokrateion Hospital, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios Apostolidis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ioannis Dimaridis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileios Charisis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Constantinos Bakogiannis
- Third Cardiology Department, Hippokrateion Hospital, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leontios Hadjileontiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.,Healthcare Engineering Innovation Center, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Vassilios Vassilikos
- Third Cardiology Department, Hippokrateion Hospital, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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The Role of Artificial Intelligence in Echocardiography. J Imaging 2023; 9:jimaging9020050. [PMID: 36826969 PMCID: PMC9962859 DOI: 10.3390/jimaging9020050] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/03/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.
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Diagnostic AI and Cardiac Diseases. Diagnostics (Basel) 2022; 12:diagnostics12122901. [PMID: 36552908 PMCID: PMC9776503 DOI: 10.3390/diagnostics12122901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
(1) Background: The purpose of this study is to review and highlight recent advances in diagnostic uses of artificial intelligence (AI) for cardiac diseases, in order to emphasize expected benefits to both patients and healthcare specialists; (2) Methods: We focused on four key search terms (Cardiac Disease, diagnosis, artificial intelligence, machine learning) across three different databases (Pubmed, European Heart Journal, Science Direct) between 2017-2022 in order to reach relatively more recent developments in the field. Our review was structured in order to clearly differentiate publications according to the disease they aim to diagnose (coronary artery disease, electrophysiological and structural heart diseases); (3) Results: Each study had different levels of success, where declared sensitivity, specificity, precision, accuracy, area under curve and F1 scores were reported for every article reviewed; (4) Conclusions: the number and quality of AI-assisted cardiac disease diagnosis publications will continue to increase through each year. We believe AI-based diagnosis should only be viewed as an additional tool assisting doctors' own judgement, where the end goal is to provide better quality of healthcare and to make getting medical help more affordable and more accessible, for everyone, everywhere.
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Kunovac A, Hathaway QA, Burrage EN, Coblentz T, Kelley EE, Sengupta PP, Hollander JM, Chantler PD. Left Ventricular Segmental Strain Identifies Unique Myocardial Deformation Patterns After Intrinsic and Extrinsic Stressors in Mice. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:2128-2138. [PMID: 35933241 PMCID: PMC9427680 DOI: 10.1016/j.ultrasmedbio.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 06/02/2022] [Accepted: 06/03/2022] [Indexed: 06/15/2023]
Abstract
We used segmental strain analysis to evaluate whether intrinsic (diet-induced obesity [DIO]) and extrinsic (unpredictable chronic mild stress [UCMS]) stressors can alter deformational patterns of the left ventricle. Six-week-old male C57BL/6J mice were randomized into the lean or obese group (n = 24/group). Mice underwent 12 wk of DIO with a high-fat diet (HFD). At 18 wk, lean and obese mice were further randomized into UCMS and non-UCMS groups (UCMS, 7 h/d, 5 d/wk, for 8 wk). Echocardiography was performed at baseline (6 wk), post-HFD (18 wk) and post-UCMS (26 wk). Machine learning was applied to the DIO and UCMS groups. There was robust predictive accuracy (area under the receiver operating characteristic curve [AUC] = 0.921) when comparing obese with lean mice, with radial strain changes in the lateral (-64%, p ≤ 0.001) and anterior free (-53%, p < 0.001) walls being most informative. The ability to predict mice that underwent UCMS, irrespective of diet, was assessed (AUC = 0.886), revealing longitudinal strain rate of the anterior midwall and radial strain of the posterior septal wall as the top features. The wall segments indicate a predilection for changes in deformation patterns to the free wall (DIO) and septal wall (UCMS), indicating disease-specific alterations to the myocardium.
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Affiliation(s)
- Amina Kunovac
- Division of Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA; Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University, Morgantown, West Virginia, USA
| | - Quincy A Hathaway
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA.
| | - Emily N Burrage
- Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Tyler Coblentz
- Division of Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Eric E Kelley
- Department of Physiology and Pharmacology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Partho P Sengupta
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA; Rutgers Robert Wood Johnson University Hospital, New Brunswick, New Jersey, USA
| | - John M Hollander
- Division of Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA; Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University, Morgantown, West Virginia, USA
| | - Paul D Chantler
- Division of Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA; Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University, Morgantown, West Virginia, USA; Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, West Virginia, USA
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Nguyen MB, Villemain O, Friedberg MK, Lovstakken L, Rusin CG, Mertens L. Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes. FRONTIERS IN RADIOLOGY 2022; 2:881777. [PMID: 37492680 PMCID: PMC10365116 DOI: 10.3389/fradi.2022.881777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 08/01/2022] [Indexed: 07/27/2023]
Abstract
Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography.
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Affiliation(s)
- Minh B. Nguyen
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Olivier Villemain
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Mark K. Friedberg
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Lasse Lovstakken
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Craig G. Rusin
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Luc Mertens
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
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Chen H, Ouyang D, Baykaner T, Jamal F, Cheng P, Rhee JW. Artificial intelligence applications in cardio-oncology: Leveraging high dimensional cardiovascular data. Front Cardiovasc Med 2022; 9:941148. [PMID: 35958422 PMCID: PMC9360492 DOI: 10.3389/fcvm.2022.941148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/27/2022] [Indexed: 11/25/2022] Open
Abstract
Growing evidence suggests a wide spectrum of potential cardiovascular complications following cancer therapies, leading to an urgent need for better risk-stratifying and disease screening in patients undergoing oncological treatment. As many cancer patients undergo frequent surveillance through imaging as well as other diagnostic testing, there is a wealth of information that can be utilized to assess one's risk for cardiovascular complications of cancer therapies. Over the past decade, there have been remarkable advances in applying artificial intelligence (AI) to analyze cardiovascular data obtained from electrocardiograms, echocardiograms, computed tomography, and cardiac magnetic resonance imaging to detect early signs or future risk of cardiovascular diseases. Studies have shown AI-guided cardiovascular image analysis can accurately, reliably and inexpensively identify and quantify cardiovascular risk, leading to better detection of at-risk or disease features, which may open preventive and therapeutic opportunities in cardio-oncology. In this perspective, we discuss the potential for the use of AI in analyzing cardiovascular data to identify cancer patients at risk for cardiovascular complications early in treatment which would allow for rapid intervention to prevent adverse cardiovascular outcomes.
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Affiliation(s)
- Haidee Chen
- City of Hope National Medical Center, Duarte, CA, United States
| | - David Ouyang
- Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Tina Baykaner
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Palo Alto, CA, United States
| | - Faizi Jamal
- City of Hope National Medical Center, Duarte, CA, United States
| | - Paul Cheng
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Palo Alto, CA, United States
- Paul Cheng
| | - June-Wha Rhee
- City of Hope National Medical Center, Duarte, CA, United States
- *Correspondence: June-Wha Rhee
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Pellikka PA, Strom JB, Pajares-Hurtado GM, Keane MG, Khazan B, Qamruddin S, Tutor A, Gul F, Peterson E, Thamman R, Watson S, Mandale D, Scott CG, Naqvi T, Woodward GM, Hawkes W. Automated analysis of limited echocardiograms: Feasibility and relationship to outcomes in COVID-19. Front Cardiovasc Med 2022; 9:937068. [PMID: 35935624 PMCID: PMC9353267 DOI: 10.3389/fcvm.2022.937068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/27/2022] [Indexed: 11/19/2022] Open
Abstract
Background As automated echocardiographic analysis is increasingly utilized, continued evaluation within hospital settings is important to further understand its potential value. The importance of cardiac involvement in patients hospitalized with COVID-19 provides an opportunity to evaluate the feasibility and clinical relevance of automated analysis applied to limited echocardiograms. Methods In this multisite US cohort, the feasibility of automated AI analysis was evaluated on 558 limited echocardiograms in patients hospitalized with COVID-19. Reliability of automated assessment of left ventricular (LV) volumes, ejection fraction (EF), and LV longitudinal strain (LS) was assessed against clinically obtained measures and echocardiographic findings. Automated measures were evaluated against patient outcomes using ROC analysis, survival modeling, and logistic regression for the outcomes of 30-day mortality and in-hospital sequelae. Results Feasibility of automated analysis for both LVEF and LS was 87.5% (488/558 patients). AI analysis was performed with biplane method in 300 (61.5%) and single plane apical 4- or 2-chamber analysis in 136 (27.9%) and 52 (10.7%) studies, respectively. Clinical LVEF was assessed using visual estimation in 192 (39.3%), biplane in 163 (33.4%), and single plane or linear methods in 104 (21.2%) of the 488 studies; 29 (5.9%) studies did not have clinically reported LVEF. LV LS was clinically reported in 80 (16.4%). Consistency between automated and clinical values demonstrated Pearson's R, root mean square error (RMSE) and intraclass correlation coefficient (ICC) of 0.61, 11.3% and 0.72, respectively, for LVEF; 0.73, 3.9% and 0.74, respectively for LS; 0.76, 24.4ml and 0.87, respectively, for end-diastolic volume; and 0.82, 12.8 ml, and 0.91, respectively, for end-systolic volume. Abnormal automated measures of LVEF and LS were associated with LV wall motion abnormalities, left atrial enlargement, and right ventricular dysfunction. Automated analysis was associated with outcomes, including survival. Conclusion Automated analysis was highly feasible on limited echocardiograms using abbreviated protocols, consistent with equivalent clinically obtained metrics, and associated with echocardiographic abnormalities and patient outcomes.
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Affiliation(s)
- Patricia A. Pellikka
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
- *Correspondence: Patricia A. Pellikka
| | - Jordan B. Strom
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Gabriel M. Pajares-Hurtado
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Martin G. Keane
- Temple Heart and Vascular Center, Philadelphia, PA, United States
| | - Benjamin Khazan
- Temple Heart and Vascular Center, Philadelphia, PA, United States
| | | | - Austin Tutor
- Ochsner Health System, New Orleans, LA, United States
| | - Fahad Gul
- Einstein Medical Center, Philadelphia, PA, United States
| | - Eric Peterson
- Einstein Medical Center, Philadelphia, PA, United States
| | - Ritu Thamman
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Shivani Watson
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Deepa Mandale
- Department of Cardiovascular Medicine, Mayo Clinic, Scottsdale, AZ, United States
| | - Christopher G. Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Tasneem Naqvi
- Department of Cardiovascular Medicine, Mayo Clinic, Scottsdale, AZ, United States
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Toma D, Toganel R, Fagarasan A, Cucerea M, Gabor-Miklosi D, Cerghit-Paler A, Iurian DR, Gozar H, Moldovan E, Iancu M, Gozar L. Interobserver Agreement and Reference Intervals for Biventricular Myocardial Deformation in Full-Term, Healthy Newborns: A 2D Speckle-Tracking Echocardiography-Based Strain Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148620. [PMID: 35886472 PMCID: PMC9315515 DOI: 10.3390/ijerph19148620] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/08/2022] [Accepted: 07/13/2022] [Indexed: 02/04/2023]
Abstract
Data regarding reference intervals for strain parameters derived from 2D speckle-tracking echocardiography in full-term newborns are limited and still under development. Our objectives were to establish the level of reproducibility and reference intervals in assessing myocardial function using 2D speckle-tracking echocardiography for longitudinal and regional strain measurements. A total of 127 full-term newborns were examined to be included in the study, of which 103 were analyzed. We used two-dimensional acquisitions from apical four-chamber view of both ventricles and analyzed the autostrain function offline. We obtained interobserver agreement between the two observers ranging from good to excellent for all speckle-tracking parameters except for the strain of the medial portion of the left ventricle (LV) lateral wall and the strain measured on the basal portion of the inter-ventricular septum, which reflected a fair interobserver reproducibility (ICC = 0.52, 95% IC: 0.22–0.72 and ICC = 0.43, 95% IC: 0.12–0.67, respectively). The reference values obtained for the LV peak longitudinal strain were between −24.65 and −14.62, those for the right ventricle (RV) free wall were from −28.69 to −10.68, and those for the RV global four-chamber were from −22.30 to −11.37. In conclusion, two-dimensional peak longitudinal LV and RV strains are reproducible with good to excellent agreement and may represent a possible alternative for the cardiac assessment of healthy newborns in the clinical practice.
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Affiliation(s)
- Daniela Toma
- Department of Pediatric Cardiology, Emergency Institute for Cardiovascular Diseases and Transplantation, 540136 Targu Mures, Romania; (D.T.); (A.F.); (D.G.-M.); (A.C.-P.); (D.-R.I.); (L.G.)
- Department of Pediatrics, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania;
| | - Rodica Toganel
- Department of Pediatrics, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania;
| | - Amalia Fagarasan
- Department of Pediatric Cardiology, Emergency Institute for Cardiovascular Diseases and Transplantation, 540136 Targu Mures, Romania; (D.T.); (A.F.); (D.G.-M.); (A.C.-P.); (D.-R.I.); (L.G.)
- Department of Pediatrics, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania;
| | - Manuela Cucerea
- Department of Neonatology, County Emergency Hospital Targu Mures, 540136 Targu Mures, Romania;
- Department M3, Pediatric IV, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Dorottya Gabor-Miklosi
- Department of Pediatric Cardiology, Emergency Institute for Cardiovascular Diseases and Transplantation, 540136 Targu Mures, Romania; (D.T.); (A.F.); (D.G.-M.); (A.C.-P.); (D.-R.I.); (L.G.)
| | - Andreea Cerghit-Paler
- Department of Pediatric Cardiology, Emergency Institute for Cardiovascular Diseases and Transplantation, 540136 Targu Mures, Romania; (D.T.); (A.F.); (D.G.-M.); (A.C.-P.); (D.-R.I.); (L.G.)
- Department of Pediatrics, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania;
| | - Diana-Ramona Iurian
- Department of Pediatric Cardiology, Emergency Institute for Cardiovascular Diseases and Transplantation, 540136 Targu Mures, Romania; (D.T.); (A.F.); (D.G.-M.); (A.C.-P.); (D.-R.I.); (L.G.)
| | - Horea Gozar
- Department of Pediatric Surgery, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania;
| | - Elena Moldovan
- Pediatric Intensive Care Unit, Emergency Institute for Cardiovascular Diseases and Transplantation, 540136 Targu Mures, Romania;
| | - Mihaela Iancu
- Department of Medical Informatics and Biostatistics, Faculty of Medicine, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Correspondence: ; Tel.: +40-0740-130888
| | - Liliana Gozar
- Department of Pediatric Cardiology, Emergency Institute for Cardiovascular Diseases and Transplantation, 540136 Targu Mures, Romania; (D.T.); (A.F.); (D.G.-M.); (A.C.-P.); (D.-R.I.); (L.G.)
- Department of Pediatrics, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania;
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Zhang Z, Zhu Y, Liu M, Zhang Z, Zhao Y, Yang X, Xie M, Zhang L. Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment. J Clin Med 2022; 11:jcm11102893. [PMID: 35629019 PMCID: PMC9143561 DOI: 10.3390/jcm11102893] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/06/2022] [Accepted: 05/18/2022] [Indexed: 11/16/2022] Open
Abstract
The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer’s experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.
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Affiliation(s)
- Zisang Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Ye Zhu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Manwei Liu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Ziming Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Yang Zhao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Xin Yang
- Media and Communication Lab (MC Lab), Electronics and Information Engineering Department, Huazhong University of Science and Technology, Wuhan 430022, China;
| | - Mingxing Xie
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
- Correspondence: (M.X.); (L.Z.)
| | - Li Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
- Correspondence: (M.X.); (L.Z.)
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Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning. Biomedicines 2022; 10:biomedicines10051082. [PMID: 35625819 PMCID: PMC9138644 DOI: 10.3390/biomedicines10051082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 02/05/2023] Open
Abstract
Endocardial border detection is a key step in assessing left ventricular systolic function in echocardiography. However, this process is still not sufficiently accurate, and manual retracing is often required, causing time-consuming and intra-/inter-observer variability in clinical practice. To address these clinical issues, more accurate and normalized automatic endocardial border detection would be valuable. Here, we develop a deep learning-based method for automated endocardial border detection and left ventricular functional assessment in two-dimensional echocardiographic videos. First, segmentation of the left ventricular cavity was performed in the six representative projections for a cardiac cycle. We employed four segmentation methods: U-Net, UNet++, UNet3+, and Deep Residual U-Net. UNet++ and UNet3+ showed a sufficiently high performance in the mean value of intersection over union and Dice coefficient. The accuracy of the four segmentation methods was then evaluated by calculating the mean value for the estimation error of the echocardiographic indexes. UNet++ was superior to the other segmentation methods, with the acceptable mean estimation error of the left ventricular ejection fraction of 10.8%, global longitudinal strain of 8.5%, and global circumferential strain of 5.8%, respectively. Our method using UNet++ demonstrated the best performance. This method may potentially support examiners and improve the workflow in echocardiography.
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Sengupta PP, Chandrashekhar Y. Imaging With Deep Learning: Sharpening the Cutting Edge. JACC Cardiovasc Imaging 2022; 15:547-549. [PMID: 35272811 DOI: 10.1016/j.jcmg.2022.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Nedadur R, Wang B, Tsang W. Artificial intelligence for the echocardiographic assessment of valvular heart disease. Heart 2022; 108:1592-1599. [PMID: 35144983 PMCID: PMC9554049 DOI: 10.1136/heartjnl-2021-319725] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 12/29/2021] [Indexed: 11/18/2022] Open
Abstract
Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.
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Affiliation(s)
- Rashmi Nedadur
- Division of Cardiac Surgery, University of Toronto, Toronto, Ontario, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Bo Wang
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Vector Institute of Artificial Intelligence, University of Toronto, Toronto, Ontario, Canada.,Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
| | - Wendy Tsang
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada .,Division of Cardiology, University of Toronto, Toronto, Ontario, Canada
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Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem. CURRENT CARDIOVASCULAR IMAGING REPORTS 2022. [DOI: 10.1007/s12410-022-09563-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Abstract
Purpose of Review
This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.
Recent Findings
Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.
Summary
The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.
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Donal E, Sade LE, Thomas L. Left atrial function: the
HbA1c
for the cardiologist and even more. Eur J Heart Fail 2022; 24:494-496. [DOI: 10.1002/ejhf.2438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 01/08/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Erwan Donal
- University of Rennes, CHU Rennes, Inserm, LTSI – UMR 1099 F‐35000 Rennes France
| | - L. Elif Sade
- University of Pittsburgh – Heart & Vascular Institute UPMC Pittsburg PA USA
- Baskent University – Cardiology Department Ankara Turkey
| | - Liza Thomas
- Faculty of Medicine and Health The University of Sydney Camperdown NSW 2006 Australia
- Department of Cardiology Westmead Hospital, Westmead Sydney NSW 2145 Australia
- South Western Sydney Clinical School University of New South Wales Liverpool NSW 2170 Australia
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Assessment of Biventricular Myocardial Function with 2-Dimensional Strain and Conventional Echocardiographic Parameters: A Comparative Analysis in Healthy Infants and Patients with Severe and Critical Pulmonary Stenosis. J Pers Med 2022; 12:jpm12010057. [PMID: 35055372 PMCID: PMC8780169 DOI: 10.3390/jpm12010057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 12/30/2021] [Accepted: 01/04/2022] [Indexed: 11/16/2022] Open
Abstract
Our aim was to compare the global longitudinal and regional biventricular strain between infants with severe and critical pulmonary stenosis (PS), and controls; to compare pre- and post-procedural strain values in infants with severe and critical PS; and to assess the correlations between echocardiographic strain and conventional parameters. We conducted a retrospective single-center study. The comparisons of echocardiographic variables were performed using separate linear mixed models. The overall mean right ventricle (RV) regional strains measured before intervention in PS patients was significantly different when compared to the control group (p = 0.0324). We found a significant change in the left ventricle, RV, and inter-ventricular septum strain (IVS) values from basal to apical location (p < 0.05). IVS strain values showed a higher decrease in mean strain values from basal to apical in PS patients. There was no significant difference in means of baseline and post-interventional strain values in PS patients (p > 0.05). Following the strain analysis in patients with PS, we obtained statistically significant changes in the RV global-4-chamber longitudinal strain (RV4C). The RV4C, which quantifies the longitudinal strain to the entire RV, can be used in current clinical practice for the evaluation of RV function in infants with severe and critical PS. The longitudinal and segmental strain capture the pathological changes in the IVS, modifications that cannot be highlighted through a classical echocardiographic evaluation.
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Deng Y, Cai P, Zhang L, Cao X, Chen Y, Jiang S, Zhuang Z, Wang B. Myocardial strain analysis of echocardiography based on deep learning. Front Cardiovasc Med 2022; 9:1067760. [PMID: 36588559 PMCID: PMC9800889 DOI: 10.3389/fcvm.2022.1067760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Strain analysis provides more thorough spatiotemporal signatures for myocardial contraction, which is helpful for early detection of cardiac insufficiency. The use of deep learning (DL) to automatically measure myocardial strain from echocardiogram videos has garnered recent attention. However, the development of key techniques including segmentation and motion estimation remains a challenge. In this work, we developed a novel DL-based framework for myocardial segmentation and motion estimation to generate strain measures from echocardiogram videos. METHODS Three-dimensional (3D) Convolutional Neural Network (CNN) was developed for myocardial segmentation and optical flow network for motion estimation. The segmentation network was used to define the region of interest (ROI), and the optical flow network was used to estimate the pixel motion in the ROI. We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. In addition, we compared the DL model with a traditional speck tracking algorithm on an independent, external clinical data. Each video was double-blind measured by an ultrasound expert and a DL expert using speck tracking echocardiography (STE) and DL method, respectively. RESULTS The DL method successfully performed automatic segmentation, motion estimation, and global longitudinal strain (GLS) measurements in all examinations. The 3D segmentation has better spatio-temporal smoothness, average dice correlation reaches 0.82, and the effect of target frame is better than that of previous 2D networks. The best motion estimation network achieved an average end-point error of 0.05 ± 0.03 mm per frame, better than previously reported state-of-the-art. The DL method showed no significant difference relative to the traditional method in GLS measurement, Spearman correlation of 0.90 (p < 0.001) and mean bias -1.2 ± 1.5%. CONCLUSION In conclusion, our method exhibits better segmentation and motion estimation performance and demonstrates the feasibility of DL method for automatic strain analysis. The DL approach helps reduce time consumption and human effort, which holds great promise for translational research and precision medicine efforts.
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Affiliation(s)
- Yinlong Deng
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Peiwei Cai
- Ultrasound Division, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Li Zhang
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Xiongcheng Cao
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Yequn Chen
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Shiyan Jiang
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Zhemin Zhuang
- Department of Electronic Information Engineering, College of Engineering, Shantou University, Shantou, China
- Zhemin Zhuang,
| | - Bin Wang
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- *Correspondence: Bin Wang,
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Cuspidi C, Carugo S, Tadic M. Do diurnal changes in blood pressure affect myocardial work indices? J Clin Hypertens (Greenwich) 2022; 24:15-17. [PMID: 34699682 PMCID: PMC8783327 DOI: 10.1111/jch.14377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 10/06/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Cesare Cuspidi
- Department of Medicine and SurgeryUniversity of Milano‐BicoccaMilanoItaly
| | - Stefano Carugo
- Department of Clinical Sciences and Community HealthUniversity of Milano and Fondazione Ospedale Maggiore IRCCS Policlinico di MilanoMilanoItaly
| | - Marijana Tadic
- University Clinical Hospital Centre “Dragisa Misovic”BelgradeSerbia
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Nakamura T, Sasano T. Artificial intelligence and cardiology: Current status and perspective: Artificial Intelligence and Cardiology. J Cardiol 2021; 79:326-333. [PMID: 34895982 DOI: 10.1016/j.jjcc.2021.11.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 12/19/2022]
Abstract
The development of artificial intelligence (AI) began in the mid-20th century but has been rapidly accelerating in the past decade. Reflecting the development of digital health over the past few years, this trend is also seen in medicine. The field of cardiovascular medicine uses a wide variety and a large amount of biosignals, so there are many situations where AI can contribute. The development of AI is in progress for all aspects of the healthcare system, including the prevention, screening, and treatment of diseases and the prediction of the prognosis. AI is expected to be used to provide specialist-level medical care, even in a situation where medical resources are scarce. However, like other medical devices, the concept and mechanism of AI must be fully understood when used; otherwise, it may be used inappropriately, resulting in detriment to the patient. Therefore, it is important to understand what we need to know as a cardiologist handling AI. This review introduces the basics and principles of AI, then shows how far the current development of AI has come, and finally gives a brief introduction of how to start the AI development for those who want to develop their own AI.
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Affiliation(s)
- Tomofumi Nakamura
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tetsuo Sasano
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan.
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Echocardiographic Advances in Dilated Cardiomyopathy. J Clin Med 2021; 10:jcm10235518. [PMID: 34884220 PMCID: PMC8658091 DOI: 10.3390/jcm10235518] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/05/2021] [Accepted: 11/23/2021] [Indexed: 12/29/2022] Open
Abstract
Although the overall survival of patients with dilated cardiomyopathy (DCM) has improved significantly in the last decades, a non-negligible proportion of DCM patients still shows an unfavorable prognosis. DCM patients not only need imaging techniques that are effective in diagnosis, but also suitable for long-term follow-up with frequent re-evaluations. The exponential growth of echocardiography’s technology and performance in recent years has resulted in improved diagnostic accuracy, stratification, management and follow-up of patients with DCM. This review summarizes some new developments in echocardiography and their promising applications in DCM. Although nowadays cardiac magnetic resonance (CMR) remains the gold standard technique in DCM, the echocardiographic advances and novelties proposed in the manuscript, if properly integrated into clinical practice, could bring echocardiography closer to CMR in terms of accuracy and may certify ultrasound as the technique of choice in the follow-up of DCM patients. The application in DCM patients of novel echocardiographic techniques represents an interesting emergent research area for scholars in the near future.
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50
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Kodera S, Akazawa H, Morita H, Komuro I. Prospects for cardiovascular medicine using artificial intelligence. J Cardiol 2021; 79:319-325. [PMID: 34772574 DOI: 10.1016/j.jjcc.2021.10.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 12/19/2022]
Abstract
As the importance of artificial intelligence (AI) in the clinical setting increases, the need for clinicians to understand AI is also increasing. This review focuses on the fundamental principles of AI and the current state of cardiovascular AI. Various types of cardiovascular AI have been developed for evaluating examinations such as X-rays, electrocardiogram, echocardiography, computed tomography, and magnetic resonance imaging. Cardiovascular AI achieves high accuracy in diagnostic support and prognosis prediction. Furthermore, it can even detect abnormalities that were previously difficult for cardiologists to detect. Randomized controlled trials begin to be reported to verify the usefulness of cardiovascular AI. The day is approaching when cardiovascular AI will be commonly used in clinical practice. Various types of medical AI will be used for cardiovascular care; however, it will not replace medical doctors. We need to understand the strengths and weaknesses of medical AI so that cardiologists can effectively use AI to improve the medical care of patients.
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Affiliation(s)
- Satoshi Kodera
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Hiroshi Akazawa
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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