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Adedinsewo D, Morales-Lara AC, Hardway H, Johnson P, Young KA, Garzon-Siatoya WT, Butler Tobah YS, Rose CH, Burnette D, Seccombe K, Fussell M, Phillips S, Lopez-Jimenez F, Attia ZI, Friedman PA, Carter RE, Noseworthy PA. Artificial intelligence-based screening for cardiomyopathy in an obstetric population: A pilot study. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:132-140. [PMID: 38989045 PMCID: PMC11232425 DOI: 10.1016/j.cvdhj.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024] Open
Abstract
Background Cardiomyopathy is a leading cause of pregnancy-related mortality and the number one cause of death in the late postpartum period. Delay in diagnosis is associated with severe adverse outcomes. Objective To evaluate the performance of an artificial intelligence-enhanced electrocardiogram (AI-ECG) and AI-enabled digital stethoscope to detect left ventricular systolic dysfunction in an obstetric population. Methods We conducted a single-arm prospective study of pregnant and postpartum women enrolled at 3 sites between October 28, 2021, and October 27, 2022. Study participants completed a standard 12-lead ECG, digital stethoscope ECG and phonocardiogram recordings, and a transthoracic echocardiogram within 24 hours. Diagnostic performance was evaluated using the area under the curve (AUC). Results One hundred women were included in the final analysis. The median age was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, and 21% as Hispanic. Five percent and 6% had left ventricular ejection fraction (LVEF) <45% and <50%, respectively. The AI-ECG model had near-perfect classification performance (AUC: 1.0, 100% sensitivity; 99%-100% specificity) for detection of cardiomyopathy at both LVEF categories. The AI-enabled digital stethoscope had an AUC of 0.98 (95% CI: 0.95, 1.00) and 0.97 (95% CI: 0.93, 1.00), for detection of LVEF <45% and <50%, respectively, with 100% sensitivity and 90% specificity. Conclusion We demonstrate an AI-ECG and AI-enabled digital stethoscope were effective for detecting cardiac dysfunction in an obstetric population. Larger studies, including an evaluation of the impact of screening on clinical outcomes, are essential next steps.
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Affiliation(s)
| | | | - Heather Hardway
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Patrick Johnson
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Kathleen A Young
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | | | - Carl H Rose
- Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, Minnesota
| | - David Burnette
- Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, Minnesota
| | | | - Mia Fussell
- Agape Community Health Center, Jacksonville, Florida
| | - Sabrina Phillips
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida
| | | | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
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Braunwald E. Cardio-obstetrics: a new specialty. Eur Heart J 2024; 45:1589-1592. [PMID: 38569057 DOI: 10.1093/eurheartj/ehae202] [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: 04/05/2024] Open
Affiliation(s)
- Eugene Braunwald
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Hale Building, Suite 7022, 60 Fenwood Road, Boston, MA 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Karabayir I, Wilkie G, Celik T, Butler L, Chinthala L, Ivanov A, Moore Simas TA, Davis RL, Akbilgic O. Development and validation of an electrocardiographic artificial intelligence model for detection of peripartum cardiomyopathy. Am J Obstet Gynecol MFM 2024; 6:101337. [PMID: 38447673 DOI: 10.1016/j.ajogmf.2024.101337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/17/2024] [Accepted: 02/27/2024] [Indexed: 03/08/2024]
Abstract
BACKGROUND This study used electrocardiogram data in conjunction with artificial intelligence methods as a noninvasive tool for detecting peripartum cardiomyopathy. OBJECTIVE This study aimed to assess the efficacy of an artificial intelligence-based heart failure detection model for peripartum cardiomyopathy detection. STUDY DESIGN We first built a deep-learning model for heart failure detection using retrospective data at the University of Tennessee Health Science Center. Cases were adult and nonpregnant female patients with a heart failure diagnosis; controls were adult nonpregnant female patients without heart failure. The model was then tested on an independent cohort of pregnant women at the University of Tennessee Health Science Center with or without peripartum cardiomyopathy. We also tested the model in an external cohort of pregnant women at Atrium Health Wake Forest Baptist. Key outcomes were assessed using the area under the receiver operating characteristic curve. We also repeated our analysis using only lead I electrocardiogram as an input to assess the feasibility of remote monitoring via wearables that can capture single-lead electrocardiogram data. RESULTS The University of Tennessee Health Science Center heart failure cohort comprised 346,339 electrocardiograms from 142,601 patients. In this cohort, 60% of participants were Black and 37% were White, with an average age (standard deviation) of 53 (19) years. The heart failure detection model achieved an area under the curve of 0.92 on the holdout set. We then tested the ability of the heart failure model to detect peripartum cardiomyopathy in an independent University of Tennessee Health Science Center cohort of pregnant women and an external Atrium Health Wake Forest Baptist cohort of pregnant women. The independent University of Tennessee Health Science Center cohort included 158 electrocardiograms from 115 patients; our deep-learning model achieved an area under the curve of 0.83 (0.77-0.89) for this data set. The external Atrium Health Wake Forest Baptist cohort involved 80 electrocardiograms from 43 patients; our deep-learning model achieved an area under the curve of 0.94 (0.91-0.98) for this data set. For identifying peripartum cardiomyopathy diagnosed ≥10 days after delivery, the model achieved an area under the curve of 0.88 (0.81-0.94) for the University of Tennessee Health Science Center cohort and of 0.96 (0.93-0.99) for the Atrium Health Wake Forest Baptist cohort. When we repeated our analysis by building a heart failure detection model using only lead-I electrocardiograms, we obtained similarly high detection accuracies, with areas under the curve of 0.73 and 0.93 for the University of Tennessee Health Science Center and Atrium Health Wake Forest Baptist cohorts, respectively. CONCLUSION Artificial intelligence can accurately detect peripartum cardiomyopathy from electrocardiograms alone. A simple electrocardiographic artificial intelligence-based peripartum screening could result in a timelier diagnosis. Given that results with 1-lead electrocardiogram data were similar to those obtained using all 12 leads, future studies will focus on remote screening for peripartum cardiomyopathy using smartwatches that can capture single-lead electrocardiogram data.
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Affiliation(s)
- Ibrahim Karabayir
- Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC (Drs Karabayir, Celik, Butler, Ivanov, and Akbilgic)
| | - Gianna Wilkie
- Department of Obstetrics & Gynecology, UMass Chan Medical School, Worcester, MA (Drs Wilkie and Simas)
| | - Turgay Celik
- Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC (Drs Karabayir, Celik, Butler, Ivanov, and Akbilgic)
| | - Liam Butler
- Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC (Drs Karabayir, Celik, Butler, Ivanov, and Akbilgic)
| | - Lokesh Chinthala
- Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN (Mr Chinthala and Dr Davis)
| | - Alexander Ivanov
- Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC (Drs Karabayir, Celik, Butler, Ivanov, and Akbilgic)
| | - Tiffany A Moore Simas
- Department of Obstetrics & Gynecology, UMass Chan Medical School, Worcester, MA (Drs Wilkie and Simas)
| | - Robert L Davis
- Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN (Mr Chinthala and Dr Davis)
| | - Oguz Akbilgic
- Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC (Drs Karabayir, Celik, Butler, Ivanov, and Akbilgic).
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Butler L, Gunturkun F, Chinthala L, Karabayir I, Tootooni MS, Bakir-Batu B, Celik T, Akbilgic O, Davis RL. AI-based preeclampsia detection and prediction with electrocardiogram data. Front Cardiovasc Med 2024; 11:1360238. [PMID: 38500752 PMCID: PMC10945012 DOI: 10.3389/fcvm.2024.1360238] [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: 12/22/2023] [Accepted: 02/21/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction More than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings. Methods Ten-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis. Results The UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at <34 weeks of pregnancy), the model's AUC (95% CI) was 0.98 (0.89-1.00). Discussion We conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.
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Affiliation(s)
- Liam Butler
- Department of Internal Medicine, Section on Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Fatma Gunturkun
- Quantitative Sciences Unit, Stanford School of Medicine, Stanford University, Stanford, CA, United States
| | - Lokesh Chinthala
- Center for Biomedical Informatics, UTHSC, Memphis, TN, United States
| | - Ibrahim Karabayir
- Department of Internal Medicine, Section on Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Mohammad S. Tootooni
- Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Chicago, IL, United States
| | - Berna Bakir-Batu
- Center for Biomedical Informatics, UTHSC, Memphis, TN, United States
| | - Turgay Celik
- Department of Internal Medicine, Section on Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Oguz Akbilgic
- Department of Internal Medicine, Section on Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Robert L. Davis
- Center for Biomedical Informatics, UTHSC, Memphis, TN, United States
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Almansouri NE, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi RK, Gutiérrez BC, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024; 16:e55869. [PMID: 38595869 PMCID: PMC11002715 DOI: 10.7759/cureus.55869] [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] [Accepted: 03/09/2024] [Indexed: 04/11/2024] Open
Abstract
Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.
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Affiliation(s)
| | - Mishael Awe
- Internal Medicine, Crimea State Medical University named after S.I Georgievsky, Simferopol, UKR
| | - Selvambigay Rajavelu
- Internal Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Kudapa Jahnavi
- Internal Medicine, Pondicherry Institute of Medical Sciences, Puducherry, IND
| | - Rohan Shastry
- Internal Medicine, Vydehi Institute of Medical Sciences and Research Center, Bengaluru, IND
| | - Ali Hasan
- Internal Medicine, University of Illinois at Chicago, Chicago, USA
| | - Hadi Hasan
- Internal Medicine, University of Illinois, Chicago, USA
| | | | | | - Brian Criollo Gutiérrez
- Health Sciences, Instituto Colombiano de Estudios Superiores de Incolda (ICESI) University, Cali, COL
| | - Ali Haider
- Allied Health Sciences, The University of Lahore, Gujrat, PAK
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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8
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Arany Z. Peripartum Cardiomyopathy. N Engl J Med 2024; 390:154-164. [PMID: 38197818 DOI: 10.1056/nejmra2306667] [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: 01/11/2024]
Affiliation(s)
- Zoltan Arany
- From the Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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9
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Jung YM, Kang S, Son JM, Lee HS, Han GI, Yoo AH, Kwon JM, Park CW, Park JS, Jun JK, Lee MS, Lee SM. Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy. Am J Obstet Gynecol MFM 2023; 5:101184. [PMID: 37863197 DOI: 10.1016/j.ajogmf.2023.101184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/25/2023] [Accepted: 10/04/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear. OBJECTIVE This study aimed to evaluate the effectiveness of a 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy. STUDY DESIGN This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography-based artificial intelligence/machine learning analysis (AiTiALVSD software; version 1.00.00, which was developed to screen for left ventricular systolic dysfunction in the general population) was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. The results were obtained under a 95% confidence interval and considered significant when P<.05. RESULTS Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead electrocardiography was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953-1.000), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499-0.951), a sensitivity of 0.917 (95% confidence interval, 0.760-1.000), a specificity of 0.927 (95% confidence interval, 0.890-0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245-0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983-1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895-0.993), 0.520 (95% confidence interval, 0.319-0.801), 0.833 (95% confidence interval, 0.622-1.000), 0.880 (95% confidence interval, 0.834-0.926), 0.303 (95% confidence interval, 0.146-0.460), and 0.988 (95% confidence interval, 0.972-1.000), respectively. CONCLUSION The 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device (AiTiALVSD) and 1-lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy.
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Affiliation(s)
- Young Mi Jung
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee); Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea (Drs Jung and S Lee); Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Sora Kang
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Jeong Min Son
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Hak Seung Lee
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Ga In Han
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Ah-Hyun Yoo
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Joon-Myoung Kwon
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee)
| | - Chan-Wook Park
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee)
| | - Joong Shin Park
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee)
| | - Jong Kwan Jun
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee)
| | - Min Sung Lee
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee).
| | - Seung Mi Lee
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee); Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea (Drs Jung and S Lee); Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee); Institute of Reproductive Medicine and Population, Medical Research Center, Seoul National University, Seoul, Korea (Dr S Lee).
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Mahmoud Z, Sanusi M, Nartey C, Adedinsewo D. Using Technology to Deliver Cardiovascular Care in African Countries. Curr Cardiol Rep 2023; 25:1823-1830. [PMID: 37966691 DOI: 10.1007/s11886-023-01988-2] [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] [Accepted: 10/30/2023] [Indexed: 11/16/2023]
Abstract
PURPOSE OF REVIEW This review aims to explore the applications of digital technology in cardiovascular care across African countries. It highlights the opportunities and challenges associated with leveraging technology to enhance patient self-monitoring, remote patient-clinician interactions, telemedicine, clinician and patient education, and research facilitation. The purpose is to highlight how technology can transform cardiovascular care in Africa. RECENT FINDINGS Recent findings indicate that the increasing penetration of mobile phones and internet connectivity in Africa offers a unique opportunity to improve cardiovascular care. Smartphone-based applications and text messaging services have been employed to promote self-monitoring and lifestyle management, although challenges related to smartphone ownership and digital literacy persist. Remote monitoring of patients by clinicians using home-based devices and wearables shows promise but requires greater accessibility and validation studies in African populations. Telemedicine diagnosis and management of cardiovascular conditions demonstrates significant potential but faces adoption challenges. Investing in targeted clinician and patient education on novel digital technology and devices as well as promoting technology-assisted research for participant recruitment and data collection can facilitate cardiovascular care advancements in Africa. Technology has the potential to revolutionize cardiovascular care in Africa by improving access, efficiency, and patient outcomes. However, barriers related to limited resources, supportive infrastructure, digital literacy, and access to devices must be addressed. Strategic actions, including investment in digital infrastructure, training programs, community collaboration, and policy advocacy, are crucial to ensuring equitable integration of digital health solutions.
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Affiliation(s)
- Zainab Mahmoud
- Division of Cardiology, Department of Medicine, Washington University in St. Louis, 660 South Euclid Avenue, Campus Box 8086, St. Louis, MO, 63110-1093, USA.
| | | | - Cecilia Nartey
- Division of Cardiology, Department of Medicine, Washington University in St. Louis, 660 South Euclid Avenue, Campus Box 8086, St. Louis, MO, 63110-1093, USA
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Sau A, Ng FS. --The emerging role of artificial intelligence enabled electrocardiograms in healthcare. BMJ MEDICINE 2023; 2:e000193. [PMID: 37841970 PMCID: PMC10568120 DOI: 10.1136/bmjmed-2022-000193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 06/01/2023] [Indexed: 10/17/2023]
Affiliation(s)
- Arunashis Sau
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
- Department of Cardiology, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK
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Okeibunor JC, Jaca A, Iwu-Jaja CJ, Idemili-Aronu N, Ba H, Zantsi ZP, Ndlambe AM, Mavundza E, Muneene D, Wiysonge CS, Makubalo L. The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review. Front Public Health 2023; 11:1102185. [PMID: 37469694 PMCID: PMC10352788 DOI: 10.3389/fpubh.2023.1102185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
Background Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care. Methods We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data. Results Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health. Conclusions Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.
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Affiliation(s)
| | - Anelisa Jaca
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | | | - Ngozi Idemili-Aronu
- Department of Sociology/Anthropology, University of Nigeria, Nsukka, Nigeria
| | - Housseynou Ba
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | - Zukiswa Pamela Zantsi
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Asiphe Mavis Ndlambe
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Edison Mavundza
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | | | - Charles Shey Wiysonge
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
- HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Durban, South Africa
| | - Lindiwe Makubalo
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
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13
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Adedinsewo DA, Morales-Lara AC, Dugan J, Garzon-Siatoya WT, Yao X, Johnson PW, Douglass EJ, Attia ZI, Phillips SD, Yamani MH, Tobah YB, Rose CH, Sharpe EE, Lopez-Jimenez F, Friedman PA, Noseworthy PA, Carter RE. Screening for peripartum cardiomyopathies using artificial intelligence in Nigeria (SPEC-AI Nigeria): Clinical trial rationale and design. Am Heart J 2023; 261:64-74. [PMID: 36966922 DOI: 10.1016/j.ahj.2023.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/10/2023] [Accepted: 03/15/2023] [Indexed: 05/26/2023]
Abstract
BACKGROUND Artificial intelligence (AI), and more specifically deep learning, models have demonstrated the potential to augment physician diagnostic capabilities and improve cardiovascular health if incorporated into routine clinical practice. However, many of these tools are yet to be evaluated prospectively in the setting of a rigorous clinical trial-a critical step prior to implementing broadly in routine clinical practice. OBJECTIVES To describe the rationale and design of a proposed clinical trial aimed at evaluating an AI-enabled electrocardiogram (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria. DESIGN The protocol will enroll 1,000 pregnant and postpartum women who reside in Nigeria in a prospective randomized clinical trial. Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. Women aged 18 and older, seen for routine obstetric care at 6 sites (2 Northern and 4 Southern) in Nigeria will be included. Participants will be randomized to the study intervention or control arm in a 1:1 fashion. This study aims to enroll participants representative of the general obstetric population at each site. The primary outcome is a new diagnosis of cardiomyopathy, defined as left ventricular ejection fraction (LVEF) < 50% during pregnancy or within 12 months postpartum. Secondary outcomes will include the detection of impaired left ventricular function (at different LVEF cut-offs), and exploratory outcomes will include the effectiveness of AI-ECG tools for cardiomyopathy detection, new diagnosis of cardiovascular disease, and the development of composite adverse maternal cardiovascular outcomes. SUMMARY This clinical trial focuses on the emerging field of cardio-obstetrics and will serve as foundational data for the use of AI-ECG tools in an obstetric population in Nigeria. This study will gather essential data regarding the utility of the AI-ECG for cardiomyopathy detection in a predominantly Black population of women and pave the way for clinical implementation of these models in routine practice. TRIAL REGISTRATION Clinicaltrials.gov: NCT05438576.
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Affiliation(s)
| | | | - Jennifer Dugan
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Xiaoxi Yao
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Patrick W Johnson
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL
| | - Erika J Douglass
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Mohamad H Yamani
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL
| | | | - Carl H Rose
- Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, MN
| | - Emily E Sharpe
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL
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14
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Menon KM, Das S, Shervey M, Johnson M, Glicksberg BS, Levin MA. Automated electrocardiogram signal quality assessment based on Fourier analysis and template matching. J Clin Monit Comput 2023; 37:829-837. [PMID: 36464761 PMCID: PMC9734499 DOI: 10.1007/s10877-022-00948-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 11/10/2022] [Indexed: 12/12/2022]
Abstract
We developed and tested a novel template matching approach for signal quality assessment on electrocardiogram (ECG) data. A computational method was developed that uses a sinusoidal approximation to the QRS complex to generate a correlation value at every point of an ECG. The strength of this correlation can be numerically adapted into a 'score' for each segment of an ECG, which can be used to stratify signal quality. The algorithm was tested on lead II ECGs of intensive care unit (ICU) patients admitted to the Mount Sinai Hospital (MSH) from January to July 2020 and on records from the MIT BIH arrhythmia database. The algorithm was found to be 98.9% specific and 99% sensitive on test data from the MSH ICU patients. The routine performs in linear O(n) time and occupies O(1) heap space in runtime. This approach can be used to lower the burden of pre-processing in ECG signal analysis. Given its runtime (O(n)) and memory (O(1)) complexity, there are potential applications for signal quality stratification and arrhythmia detection in wearable devices or smartphones.
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Affiliation(s)
- Kartikeya M Menon
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Subrat Das
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mark Shervey
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew Johnson
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew A Levin
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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15
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Haq IU, Chhatwal K, Sanaka K, Xu B. Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects. Vasc Health Risk Manag 2022; 18:517-528. [PMID: 35855754 PMCID: PMC9288176 DOI: 10.2147/vhrm.s279337] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/07/2022] [Indexed: 11/23/2022] Open
Abstract
Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.
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Affiliation(s)
- Ikram U Haq
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
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16
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Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
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Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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17
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Adedinsewo DA, Pollak AW, Phillips SD, Smith TL, Svatikova A, Hayes SN, Mulvagh SL, Norris C, Roger VL, Noseworthy PA, Yao X, Carter RE. Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools. Circ Res 2022; 130:673-690. [PMID: 35175849 PMCID: PMC8889564 DOI: 10.1161/circresaha.121.319876] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman's life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.
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Affiliation(s)
- Demilade A. Adedinsewo
- Department of Cardiovascular Medicine (D.A.A., A.W.P., S.D.P.), Mayo Clinic, Jacksonville, FL
| | - Amy W. Pollak
- Department of Cardiovascular Medicine (D.A.A., A.W.P., S.D.P.), Mayo Clinic, Jacksonville, FL
| | - Sabrina D. Phillips
- Department of Cardiovascular Medicine (D.A.A., A.W.P., S.D.P.), Mayo Clinic, Jacksonville, FL
| | - Taryn L. Smith
- Division of General Internal Medicine (T.L.S.), Mayo Clinic, Jacksonville, FL
| | - Anna Svatikova
- Department of Cardiovascular Diseases (A.S.), Mayo Clinic, Phoenix, AZ
| | - Sharonne N. Hayes
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
| | - Sharon L. Mulvagh
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
- Division of Cardiology, Dalhousie University, Halifax, Nova Scotia, Canada (S.L.M.)
| | - Colleen Norris
- Cardiovascular Health and Stroke Strategic Clinical Network, Edmonton, Canada (C.N.)
| | - Veronique L. Roger
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
- Department of Quantitative Health Sciences (V.L.R.), Mayo Clinic, Rochester, MN
- Epidemiology and Community Health Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD (V.L.R.)
| | - Peter A. Noseworthy
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
| | - Xiaoxi Yao
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery (X.Y.), Mayo Clinic, Rochester, MN
| | - Rickey E. Carter
- Department of Quantitative Health Sciences (R.E.C.), Mayo Clinic, Jacksonville, FL
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18
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Grosser M, Lin H, Wu M, Zhang Y, Tipper S, Venter D, Lu J, dos Remedios CG. A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning. Biophys Rev 2022; 14:381-401. [PMID: 35340600 PMCID: PMC8921361 DOI: 10.1007/s12551-022-00933-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 01/24/2022] [Indexed: 12/14/2022] Open
Abstract
As developments in artificial intelligence and machine learning become more widespread in healthcare, their potential to transform clinical outcomes also increases. Peripartum cardiomyopathy is a rare and poorly-characterised condition that presents as heart failure in the last trimester prior to delivery or within 5-6 months postpartum. The lack of a definitive understanding of the molecular causes and clinical progress of this condition suggests that bibliometrics will be well-suited to creating new insights into this serious clinical problem. We examine similarities and differences between peripartum and its closely related familial dilated cardiomyopathy and idiopathic dilated cardiomyopathy. Using PubMed as the source of bibliometric data, we apply artificial intelligence-supported natural language processing to compare extracted data and genes association with these cardiomyopathies. Gene data were enhanced with additional metadata from third-party datasets and then analysed for their impact and specificity for peripartum cardiomyopathy. Artificial intelligence identified 14 genes that distinguished peripartum from both dilated and familial dilated cardiomyopathy. They are as follows: CTSD, RLN2, MMP23B*, SLC17A5, ST2*, PTHLH, CFH*, CFI, GPT, MR1, Rln1, SRI, STAT5A* and THBD. We then used the Human Protein Atlas website that uses affinity-purified rabbit polyclonal antibodies to identify genes that are expressed at the protein level (bold), or as RNA transcripts (*) in healthy human left ventricles. Additional analysis focussed on the full set of peripartum genes on linkage and specificity to cardiomyopathy yielded a different set of thirteen genes (bold font indicates those expressed in cardiomyocytes: PRL, RLN2, PLN, ST2, CTSD, F2, ACE, STAT3, TTN, SPP1, LGALS3, miR-146a, GNB3, SRI). This type of analysis can highlight new avenues for research, aimed at improving genomics-driven peripartum cardiomyopathy diagnosis as well as potential pathological and clinical sub-classification. We expect that this will allow for future improvements in identification, treatment and management of this condition. The first step in the application of these bibliometric-based artificial intelligence methods is to understand the current knowledge, and it is the aim of this paper to show how this might be achieved.
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Affiliation(s)
- M. Grosser
- 23 Strands Pty Ltd, 107, 26 Pirrama Rd, Pyrmont, NSW Australia
| | - H. Lin
- 23 Strands Pty Ltd, 107, 26 Pirrama Rd, Pyrmont, NSW Australia
| | - M. Wu
- University Technology Sydney, 15 Broadway, Ultimo, NSW Australia
| | - Y. Zhang
- University Technology Sydney, 15 Broadway, Ultimo, NSW Australia
| | - S. Tipper
- 23 Strands Pty Ltd, 107, 26 Pirrama Rd, Pyrmont, NSW Australia
| | - D. Venter
- 23 Strands Pty Ltd, 107, 26 Pirrama Rd, Pyrmont, NSW Australia
| | - J. Lu
- University Technology Sydney, 15 Broadway, Ultimo, NSW Australia
| | - C. G. dos Remedios
- Victor Chang Cardiac Research Institute, 405 Liverpool St, Darlinghurst, Australia ,Sydney Heart Bank, University of Sydney, Sydney, Australia
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19
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Lee YJ, Choi B, Lee MS, Jin U, Yoon S, Jo YY, Kwon JM. An artificial intelligence electrocardiogram analysis for detecting cardiomyopathy in the peripartum period. Int J Cardiol 2022; 352:72-77. [PMID: 35122911 DOI: 10.1016/j.ijcard.2022.01.064] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/19/2022] [Accepted: 01/28/2022] [Indexed: 01/08/2023]
Abstract
BACKGROUND Peripartum cardiomyopathy (PPCM) is a fatal maternal complication, with left ventricular systolic dysfunction (LVSD; Left ventricular ejection fraction 45% or less) occurring at the end of pregnancy or in the months following delivery. The scarcity of screening tools for PPCM leads to a delayed diagnosis and increases its mortality and morbidity. We aim to evaluate an electrocardiogram (ECG)-deep learning model (DLM) for detecting cardiomyopathy in the peripartum period. METHODS For the DLM development and internal performance test for detecting LVSD, we obtained a dataset of 122,733 ECG-echocardiography pairs from 58,530 male and female patients from two community hospitals. For the DLM external validation, this study included 271 ECG-echocardiography pairs (157 unique pregnant and postpartum period women) examined in the Ajou University Medical Center (AUMC) between January 2007 and May 2020. All included cases underwent an ECG within two weeks before or after the day of transthoracic echocardiography, which was performed within a month before delivery, or within five months after delivery. Based on the diagnostic criteria of PPCM, we analyzed the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to evaluate the model effectiveness. RESULTS The ECG-based DLM detected PPCM with an AUROC of 0.877. Moreover, its sensitivity, specificity, PPV, and NPV for the detection of PPCM were 0.877, 0.833, 0.809, 0.352, and 0.975, respectively. CONCLUSIONS An ECG-based DLM non-invasively and effectively detects cardiomyopathies occurring in the peripartum period and could be an ideal screening tool for PPCM.
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Affiliation(s)
- Ye Ji Lee
- Department of Obstetrics and Gynecology, Gangdong Miz Women's Hospital, Seoul, Republic of Korea
| | - Byungjin Choi
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Min Sung Lee
- Medical research team, Medical AI, Seoul, Republic of Korea.
| | - Uram Jin
- Department of Cardiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Seokyoung Yoon
- Ajou University School of Medicine, Department of Obstetrics and Gynecology, Republic of Korea
| | - Yong-Yeon Jo
- Medical research team, Medical AI, Seoul, Republic of Korea
| | - Joon-Myoung Kwon
- Medical research team, Medical AI, Seoul, Republic of Korea; Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, Republic of Korea.; Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea
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