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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; 37:736-739. [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] [MESH Headings] [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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2
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Saito Y, Omae Y, Harada T, Sorimachi H, Yuasa N, Kagami K, Murakami F, Naito A, Tani Y, Kato T, Wada N, Okumura Y, Ishii H, Obokata M. Exercise Stress Echocardiography-Based Phenotyping of Heart Failure With Preserved Ejection Fraction. J Am Soc Echocardiogr 2024; 37:759-768. [PMID: 38754750 DOI: 10.1016/j.echo.2024.05.003] [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: 02/08/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome requiring improved phenotypic classification. Previous studies have identified subphenotypes of HFpEF, but the lack of exercise assessment is a major limitation. The aim of this study was to identify distinct pathophysiologic clusters of HFpEF based on clinical characteristics, and resting and exercise assessments. METHODS A total of 265 patients with HFpEF underwent ergometry exercise stress echocardiography with simultaneous expired gas analysis. Cluster analysis was performed by the K-prototype method with 21 variables (10 clinical and resting echocardiographic variables and 11 exercise echocardiographic parameters). Pathophysiologic features, exercise tolerance, and prognosis were compared among phenogroups. RESULTS Three distinct phenogroups were identified. Phenogroup 1 (n = 112 [42%]) was characterized by preserved biventricular systolic reserve and cardiac output augmentation. Phenogroup 2 (n = 58 [22%]) was characterized by a high prevalence of atrial fibrillation, increased pulmonary arterial and right atrial pressures, depressed right ventricular systolic functional reserve, and impaired right ventricular-pulmonary artery coupling during exercise. Phenogroup 3 (n = 95 [36%]) was characterized by the smallest body mass index, ventricular and vascular stiffening, impaired left ventricular diastolic reserve, and worse exercise capacity. Phenogroups 2 and 3 had higher rates of composite outcomes of all-cause mortality or heart failure events than phenogroup 1 (log-rank P = .02). CONCLUSION Exercise echocardiography-based cluster analysis identified three distinct phenogroups of HFpEF, with unique exercise pathophysiologic features, exercise capacity, and clinical outcomes.
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Affiliation(s)
- Yuki Saito
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan; Division of Cardiology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Yuto Omae
- Department of Industrial Engineering and Management, College of Industrial Technology, Nihon University, Chiba, Japan
| | - Tomonari Harada
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Hidemi Sorimachi
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Naoki Yuasa
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Kazuki Kagami
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan; Division of Cardiovascular Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Fumitaka Murakami
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Ayami Naito
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan; Division of Cardiovascular Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Yuta Tani
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Toshimitsu Kato
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Naoki Wada
- Department of Rehabilitation Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yasuo Okumura
- Division of Cardiology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Hideki Ishii
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Masaru Obokata
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan.
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3
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Kane GC. The True Foundation of Medicine Is the Understanding of the Disease: Gaining Insights Into the Pathophysiology of Heart Failure With Preserved Ejection Fraction. J Am Soc Echocardiogr 2024; 37:769-771. [PMID: 38857851 DOI: 10.1016/j.echo.2024.06.001] [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] [Received: 06/05/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024]
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4
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Johnson CL, Press RH, Simone CB, Shen B, Tsai P, Hu L, Yu F, Apinorasethkul C, Ackerman C, Zhai H, Lin H, Huang S. Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study. Front Oncol 2024; 14:1375096. [PMID: 39055552 PMCID: PMC11269179 DOI: 10.3389/fonc.2024.1375096] [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: 01/23/2024] [Accepted: 06/20/2024] [Indexed: 07/27/2024] Open
Abstract
Purpose To evaluate organ at risk (OAR) auto-segmentation in the head and neck region of computed tomography images using two different commercially available deep-learning-based auto-segmentation (DLAS) tools in a single institutional clinical applications. Methods Twenty-two OARs were manually contoured by clinicians according to published guidelines on planning computed tomography (pCT) images for 40 clinical head and neck cancer (HNC) cases. Automatic contours were generated for each patient using two deep-learning-based auto-segmentation models-Manteia AccuContour and MIM ProtégéAI. The accuracy and integrity of autocontours (ACs) were then compared to expert contours (ECs) using the Sørensen-Dice similarity coefficient (DSC) and Mean Distance (MD) metrics. Results ACs were generated for 22 OARs using AccuContour and 17 OARs using ProtégéAI with average contour generation time of 1 min/patient and 5 min/patient respectively. EC and AC agreement was highest for the mandible (DSC 0.90 ± 0.16) and (DSC 0.91 ± 0.03), and lowest for the chiasm (DSC 0.28 ± 0.14) and (DSC 0.30 ± 0.14) for AccuContour and ProtégéAI respectively. Using AccuContour, the average MD was<1mm for 10 of the 22 OARs contoured, 1-2mm for 6 OARs, and 2-3mm for 6 OARs. For ProtégéAI, the average mean distance was<1mm for 8 out of 17 OARs, 1-2mm for 6 OARs, and 2-3mm for 3 OARs. Conclusions Both DLAS programs were proven to be valuable tools to significantly reduce the time required to generate large amounts of OAR contours in the head and neck region, even though manual editing of ACs is likely needed prior to implementation into treatment planning. The DSCs and MDs achieved were similar to those reported in other studies that evaluated various other DLAS solutions. Still, small volume structures with nonideal contrast in CT images, such as nerves, are very challenging and will require additional solutions to achieve sufficient results.
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Affiliation(s)
| | | | | | - Brian Shen
- New York Proton Center, New York, NY, United States
| | | | - Lei Hu
- New York Proton Center, New York, NY, United States
| | - Francis Yu
- New York Proton Center, New York, NY, United States
| | | | | | - Huifang Zhai
- New York Proton Center, New York, NY, United States
| | - Haibo Lin
- New York Proton Center, New York, NY, United States
| | - Sheng Huang
- New York Proton Center, New York, NY, United States
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin, China
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5
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Shah R, Tokodi M, Jamthikar A, Bhatti S, Akhabue E, Casaclang-Verzosa G, Yanamala N, Sengupta PP. A deep patient-similarity learning framework for the assessment of diastolic dysfunction in elderly patients. Eur Heart J Cardiovasc Imaging 2024; 25:937-946. [PMID: 38315669 DOI: 10.1093/ehjci/jeae037] [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: 06/11/2023] [Revised: 01/27/2024] [Accepted: 02/01/2024] [Indexed: 02/07/2024] Open
Abstract
AIMS Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal left ventricular (LV) diastolic dysfunction (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF). METHODS AND RESULTS A previously developed DeepNN was tested on 5596 older participants (66-90 years; 57% female; 20% Black) from the Atherosclerosis Risk in Communities Study. The association of DeepNN predictions with HF or all-cause death for the American College of Cardiology Foundation/American Heart Association Stage A/B (n = 4054) and Stage C/D (n = 1542) subgroups was assessed. The DeepNN-predicted high-risk compared with the low-risk phenogroup demonstrated an increased incidence of HF and death for both Stage A/B and Stage C/D (log-rank P < 0.0001 for all). In multi-variable analyses, the high-risk phenogroup remained an independent predictor of HF and death in both Stages A/B {adjusted hazard ratio [95% confidence interval (CI)] 6.52 [4.20-10.13] and 2.21 [1.68-2.91], both P < 0.0001} and Stage C/D [6.51 (4.06-10.44) and 1.03 (1.00-1.06), both P < 0.0001], respectively. In addition, DeepNN showed incremental value over the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) guidelines [net re-classification index, 0.5 (CI 0.4-0.6), P < 0.001; C-statistic improvement, DeepNN (0.76) vs. ASE/EACVI (0.70), P < 0.001] overall and maintained across stage groups. CONCLUSION Despite training with a younger cohort, a deep patient-similarity-based learning framework for assessing LVDD provides a robust prediction of all-cause death and incident HF for older patients.
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Affiliation(s)
- Rohan Shah
- Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital (RWJUH), Rutgers Robert Wood Johnson Medical School (RWJMS), 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA
| | - Marton Tokodi
- Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital (RWJUH), Rutgers Robert Wood Johnson Medical School (RWJMS), 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Ankush Jamthikar
- Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital (RWJUH), Rutgers Robert Wood Johnson Medical School (RWJMS), 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA
| | - Sabha Bhatti
- Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital (RWJUH), Rutgers Robert Wood Johnson Medical School (RWJMS), 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA
| | - Ehimare Akhabue
- Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital (RWJUH), Rutgers Robert Wood Johnson Medical School (RWJMS), 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA
| | - Grace Casaclang-Verzosa
- Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital (RWJUH), Rutgers Robert Wood Johnson Medical School (RWJMS), 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA
| | - Naveena Yanamala
- Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital (RWJUH), Rutgers Robert Wood Johnson Medical School (RWJMS), 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA
| | - Partho P Sengupta
- Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital (RWJUH), Rutgers Robert Wood Johnson Medical School (RWJMS), 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA
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Ouyang D, Carter RE, Pellikka PA. Machine Learning in Imaging: What is JASE Looking For? J Am Soc Echocardiogr 2024; 37:273-275. [PMID: 38432849 DOI: 10.1016/j.echo.2024.01.002] [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: 03/05/2024]
Affiliation(s)
- David Ouyang
- Department of Cardiology, Cedars-Sinai Medical Center
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Stoicescu L, Crişan D, Morgovan C, Avram L, Ghibu S. Heart Failure with Preserved Ejection Fraction: The Pathophysiological Mechanisms behind the Clinical Phenotypes and the Therapeutic Approach. Int J Mol Sci 2024; 25:794. [PMID: 38255869 PMCID: PMC10815792 DOI: 10.3390/ijms25020794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/27/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
Heart failure (HF) with preserved ejection fraction (HFpEF) is an increasingly frequent form and is estimated to be the dominant form of HF. On the other hand, HFpEF is a syndrome with systemic involvement, and it is characterized by multiple cardiac and extracardiac pathophysiological alterations. The increasing prevalence is currently reaching epidemic levels, thereby making HFpEF one of the greatest challenges facing cardiovascular medicine today. Compared to HF with reduced ejection fraction (HFrEF), the medical attitude in the case of HFpEF was a relaxed one towards the disease, despite the fact that it is much more complex, with many problems related to the identification of physiopathogenetic mechanisms and optimal methods of treatment. The current medical challenge is to develop effective therapeutic strategies, because patients suffering from HFpEF have symptoms and quality of life comparable to those with reduced ejection fraction, but the specific medication for HFrEF is ineffective in this situation; for this, we must first understand the pathological mechanisms in detail and correlate them with the clinical presentation. Another important aspect of HFpEF is the diversity of patients that can be identified under the umbrella of this syndrome. Thus, before being able to test and develop effective therapies, we must succeed in grouping patients into several categories, called phenotypes, depending on the pathological pathways and clinical features. This narrative review critiques issues related to the definition, etiology, clinical features, and pathophysiology of HFpEF. We tried to describe in as much detail as possible the clinical and biological phenotypes recognized in the literature in order to better understand the current therapeutic approach and the reason for the limited effectiveness. We have also highlighted possible pathological pathways that can be targeted by the latest research in this field.
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Affiliation(s)
- Laurențiu Stoicescu
- Internal Medicine Department, Faculty of Medicine, “Iuliu Haţieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (L.S.); or (D.C.); or (L.A.)
- Cardiology Department, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
| | - Dana Crişan
- Internal Medicine Department, Faculty of Medicine, “Iuliu Haţieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (L.S.); or (D.C.); or (L.A.)
- Internal Medicine Department, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
| | - Claudiu Morgovan
- Preclinical Department, Faculty of Medicine, “Lucian Blaga” University of Sibiu, 550169 Sibiu, Romania
| | - Lucreţia Avram
- Internal Medicine Department, Faculty of Medicine, “Iuliu Haţieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (L.S.); or (D.C.); or (L.A.)
- Internal Medicine Department, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
| | - Steliana Ghibu
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Haţieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania;
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8
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Lee E, Ito S, Miranda WR, Lopez-Jimenez F, Kane GC, Asirvatham SJ, Noseworthy PA, Friedman PA, Carter RE, Borlaug BA, Attia ZI, Oh JK. Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure. NPJ Digit Med 2024; 7:4. [PMID: 38182738 PMCID: PMC10770308 DOI: 10.1038/s41746-023-00993-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024] Open
Abstract
Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645-1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298-1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.
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Affiliation(s)
- Eunjung Lee
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Saki Ito
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - William R Miranda
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Garvan C Kane
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Rickey E Carter
- Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
| | - Barry A Borlaug
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jae K Oh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
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9
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Yeung DF, Abolmaesumi P, Tsang TSM. Artificial Intelligence for Left Ventricular Diastolic Function Assessment: A New Paradigm on the Horizon. J Am Soc Echocardiogr 2023; 36:1079-1082. [PMID: 37578403 DOI: 10.1016/j.echo.2023.07.006] [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: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023]
Affiliation(s)
- Darwin F Yeung
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Purang Abolmaesumi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Teresa S M Tsang
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada
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Akerman AP, Porumb M, Scott CG, Beqiri A, Chartsias A, Ryu AJ, Hawkes W, Huntley GD, Arystan AZ, Kane GC, Pislaru SV, Lopez-Jimenez F, Gomez A, Sarwar R, O'Driscoll J, Leeson P, Upton R, Woodward G, Pellikka PA. Automated Echocardiographic Detection of Heart Failure With Preserved Ejection Fraction Using Artificial Intelligence. JACC. ADVANCES 2023; 2:100452. [PMID: 38939447 PMCID: PMC11198161 DOI: 10.1016/j.jacadv.2023.100452] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/18/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2024]
Abstract
Background Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate. Objectives The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF. Methods A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber video clips to classify patients with HFpEF (diagnosis of heart failure, ejection fraction ≥50%, and echocardiographic evidence of increased filling pressure; cases) vs without HFpEF (ejection fraction ≥50%, no diagnosis of heart failure, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or nondiagnostic (high uncertainty). Performance was assessed in an independent multisite data set and compared to previously validated clinical scores. Results Training and validation included 2,971 cases and 3,785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (area under receiver-operating characteristic curve: 0.97 [95% CI: 0.96-0.97] and 0.95 [95% CI: 0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were nondiagnostic; sensitivity (87.8%; 95% CI: 84.5%-90.9%) and specificity (81.9%; 95% CI: 78.2%-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing (HFA-PEFF), and Final Etiology and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder, and Filling Pressure (H2FPEF) scores, the AI HFpEF model correctly reclassified 73.5% and 73.6%, respectively. During follow-up (median: 2.3 [IQR: 0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (HR: 1.9 [95% CI: 1.5-2.4]). Conclusions An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with vs without HFpEF, more often than clinical scores, and identified patients with higher mortality.
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Affiliation(s)
| | | | - Christopher G. Scott
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Alexander J. Ryu
- Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Geoffrey D. Huntley
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Ayana Z. Arystan
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Garvan C. Kane
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Sorin V. Pislaru
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Rizwan Sarwar
- Ultromics Ltd, Oxford, United Kingdom
- Cardiovascular Clinical Research Facility, University of Oxford, Oxford, United Kingdom
- Experimental Therapeutics, Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Jamie O'Driscoll
- Ultromics Ltd, Oxford, United Kingdom
- Department of Cardiology, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom
| | - Paul Leeson
- Ultromics Ltd, Oxford, United Kingdom
- Cardiovascular Clinical Research Facility, University of Oxford, Oxford, United Kingdom
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Pellikka PA. What Are JASE Readers Reading? J Am Soc Echocardiogr 2023; 36:567-568. [PMID: 37270257 DOI: 10.1016/j.echo.2023.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
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12
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Nagueh SF. Left Ventricular Diastolic Dysfunction: Diagnostic and Prognostic Perspectives. J Am Soc Echocardiogr 2023; 36:307-309. [PMID: 36572368 DOI: 10.1016/j.echo.2022.12.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Sherif F Nagueh
- Methodist DeBakey Heart and Vascular Center, Houston, Texas.
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13
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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: 23] [Impact Index Per Article: 23.0] [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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