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Pencovich N, Smith BH, Attia ZI, Jimenez FL, Bentall AJ, Schinstock CA, Khamash HA, Jadlowiec CC, Jarmi T, Mao SA, Park WD, Diwan TS, Friedman PA, Stegall MD. Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation. Transplantation 2024; 108:1976-1985. [PMID: 38557657 DOI: 10.1097/tp.0000000000005023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. METHODS We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data. RESULTS Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality ( P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). CONCLUSIONS The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.
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Affiliation(s)
- Niv Pencovich
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
- Department of General Surgery and Transplantation, Sheba Medical Center, Tel Hashomer, Tel-Aviv University, Tel-Aviv, Israel
| | - Byron H Smith
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Andrew J Bentall
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | - Carrie A Schinstock
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | | | | | - Tambi Jarmi
- Department of Transplant, Mayo Clinic Florida, Jacksonville, FL
| | - Shennen A Mao
- Division of Transplant Surgery, Department of Surgery, Mayo Clinic, Phoenix, AZ
| | - Walter D Park
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | - Tayyab S Diwan
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Mark D Stegall
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
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2
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Mayourian J, Gearhart A, La Cava WG, Vaid A, Nadkarni GN, Triedman JK, Powell AJ, Wald RM, Valente AM, Geva T, Duong SQ, Ghelani SJ. Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease. J Am Coll Cardiol 2024; 84:815-828. [PMID: 39168568 DOI: 10.1016/j.jacc.2024.05.062] [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: 02/16/2024] [Revised: 04/23/2024] [Accepted: 05/20/2024] [Indexed: 08/23/2024]
Abstract
BACKGROUND Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD). OBJECTIVES The purpose of this study was to develop and externally validate an AI-ECG model to predict cardiovascular magnetic resonance (CMR)-defined biventricular dysfunction/dilation in patients with CHD. METHODS We trained (80%) and tested (20%) a convolutional neural network on paired ECG-CMRs (≤30 days apart) from patients with and without CHD to detect left ventricular (LV) dysfunction (ejection fraction ≤40%), RV dysfunction (ejection fraction ≤35%), and LV and RV dilation (end-diastolic volume z-score ≥4). Performance was assessed during internal testing and external validation on an outside health care system using area under receiver-operating curve (AUROC) and area under precision recall curve. RESULTS The internal and external cohorts comprised 8,584 ECG-CMR pairs (n = 4,941; median CMR age 20.7 years) and 909 ECG-CMR pairs (n = 746; median CMR age 25.4 years), respectively. Model performance was similar for internal testing (AUROC: LV dysfunction 0.87; LV dilation 0.86; RV dysfunction 0.88; RV dilation 0.81) and external validation (AUROC: LV dysfunction 0.89; LV dilation 0.83; RV dysfunction 0.82; RV dilation 0.80). Model performance was lowest in functionally single ventricle patients. Tetralogy of Fallot patients predicted to be at high risk of ventricular dysfunction had lower survival (P < 0.001). Model explainability via saliency mapping revealed that lateral precordial leads influence all outcome predictions, with high-risk features including QRS widening and T-wave inversions for RV dysfunction/dilation. CONCLUSIONS AI-ECG shows promise to predict biventricular dysfunction/dilation, which may help inform CMR timing in CHD.
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MESH Headings
- Humans
- Deep Learning
- Electrocardiography/methods
- Female
- Male
- Heart Defects, Congenital/physiopathology
- Heart Defects, Congenital/complications
- Heart Defects, Congenital/diagnosis
- Adult
- Adolescent
- Young Adult
- Child
- Ventricular Dysfunction, Left/physiopathology
- Ventricular Dysfunction, Left/diagnosis
- Ventricular Dysfunction, Left/diagnostic imaging
- Ventricular Dysfunction, Right/physiopathology
- Ventricular Dysfunction, Right/diagnostic imaging
- Ventricular Dysfunction, Right/diagnosis
- Magnetic Resonance Imaging, Cine/methods
- Child, Preschool
- Predictive Value of Tests
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Affiliation(s)
- Joshua Mayourian
- Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Addison Gearhart
- Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - William G La Cava
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - John K Triedman
- Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew J Powell
- Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Rachel M Wald
- Division of Cardiology, University of Toronto, Peter Munk Cardiac Centre, Toronto, Ontario, Canada
| | - Anne Marie Valente
- Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Tal Geva
- Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Son Q Duong
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sunil J Ghelani
- Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
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3
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Lüscher TF, Wenzl FA, D'Ascenzo F, Friedman PA, Antoniades C. Artificial intelligence in cardiovascular medicine: clinical applications. Eur Heart J 2024:ehae465. [PMID: 39158472 DOI: 10.1093/eurheartj/ehae465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/07/2024] [Accepted: 07/03/2024] [Indexed: 08/20/2024] Open
Abstract
Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.
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Affiliation(s)
- Thomas F Lüscher
- Royal Brompton and Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College London, UK
- Cardiovascular Academic Group, King's College, London, UK
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
| | - Florian A Wenzl
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
- National Disease Registration and Analysis Service, NHS, London, UK
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Department of Clinical Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Fabrizio D'Ascenzo
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza Hospital, Turin, Italy
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic and Mayo Foundation, Rochester, MN, USA
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, RDM Division of Cardiovascular Medicine, University of Oxford, Headley Way, Headington, Oxford OX39DU, UK
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Chai C, Peng SZ, Zhang R, Li CW, Zhao Y. Advancing Emergency Department Triage Prediction With Machine Learning to Optimize Triage for Abdominal Pain Surgery Patients. Surg Innov 2024:15533506241273449. [PMID: 39150388 DOI: 10.1177/15533506241273449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
BACKGROUND The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use machine learning models to predict emergency surgical abdominal pain patients in triage, and then compare their performance with conventional Logistic regression models. METHODS Using 38 214 patients presenting with acute abdominal pain at Zhongnan Hospital of Wuhan University between March 1, 2014, and March 1, 2022, we identified all adult patients (aged ≥18 years). We utilized routinely available triage data in electronic medical records as predictors, including structured data (eg, triage vital signs, gender, and age) and unstructured data (chief complaints and physical examinations in free-text format). The primary outcome measure was whether emergency surgery was performed. The dataset was randomly sampled, with 80% assigned to the training set and 20% to the test set. We developed 5 machine learning models: Light Gradient Boosting Machine (Light GBM), eXtreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Random Forest (RF). Logistic regression (LR) served as the reference model. Model performance was calculated for each model, including the area under the receiver-work characteristic curve (AUC) and net benefit (decision curve), as well as the confusion matrix. RESULTS Of all the 38 214 acute abdominal pain patients, 4208 underwent emergency abdominal surgery while 34 006 received non-surgical treatment. In the surgery outcome prediction, all 4 machine learning models outperformed the reference model (eg, AUC, 0.899 [95%CI 0.891-0.903] in the Light GBM vs. 0.885 [95%CI 0.876-0.891] in the reference model), Similarly, most machine learning models exhibited significant improvements in net reclassification compared to the reference model (eg, NRIs of 0.0812[95%CI, 0.055-0.1105] in the XGBoost), with the exception of the RF model. Decision curve analysis shows that across the entire range of thresholds, the net benefits of the XGBoost and the Light GBM models were higher than the reference model. In particular, the Light GBM model performed well in predicting the need for emergency abdominal surgery with higher sensitivity, specificity, and accuracy. CONCLUSIONS Machine learning models have demonstrated superior performance in predicting emergency abdominal pain surgery compared to traditional models. Modern machine learning improves clinical triage decisions and ensures that critically needy patients receive priority for emergency resources and timely, effective treatment.
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Affiliation(s)
- Chen Chai
- Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitation, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shu-Zhen Peng
- Wuhan University School of Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Zhang
- Xiaomi's Wuhan Headquarters, Wuhan, Hubei, China
| | - Cheng-Wei Li
- Information Center, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yan Zhao
- Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitation, Zhongnan Hospital of Wuhan University, Wuhan, China
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Sawano S, Kodera S, Setoguchi N, Tanabe K, Kushida S, Kanda J, Saji M, Nanasato M, Maki H, Fujita H, Kato N, Watanabe H, Suzuki M, Takahashi M, Sawada N, Yamasaki M, Sato M, Katsushika S, Shinohara H, Takeda N, Fujiu K, Daimon M, Akazawa H, Morita H, Komuro I. Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies. PLoS One 2024; 19:e0307978. [PMID: 39141600 PMCID: PMC11324121 DOI: 10.1371/journal.pone.0307978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 07/15/2024] [Indexed: 08/16/2024] Open
Abstract
The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. We aimed to apply self-supervised learning using masked autoencoders (MAEs) to improve the performance of the 12-lead electrocardiography (ECG) analysis model using limited ECG data. We pretrained Vision Transformer (ViT) models by reconstructing the masked ECG data with MAE. We fine-tuned this MAE-based ECG pretrained model on ECG-echocardiography data from The University of Tokyo Hospital (UTokyo) for the detection of left ventricular systolic dysfunction (LVSD), and then evaluated it using multi-center external validation data from seven institutions, employing the area under the receiver operating characteristic curve (AUROC) for assessment. We included 38,245 ECG-echocardiography pairs from UTokyo and 229,439 pairs from all institutions. The performances of MAE-based ECG models pretrained using ECG data from UTokyo were significantly higher than that of other Deep Neural Network models across all external validation cohorts (AUROC, 0.913-0.962 for LVSD, p < 0.001). Moreover, we also found improvements for the MAE-based ECG analysis model depending on the model capacity and the amount of training data. Additionally, the MAE-based ECG analysis model maintained high performance even on the ECG benchmark dataset (PTB-XL). Our proposed method developed high performance MAE-based ECG analysis models using limited ECG data.
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Affiliation(s)
- Shinnosuke Sawano
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Satoshi Kodera
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Naoto Setoguchi
- Division of Cardiology, Mitsui Memorial Hospital, Tokyo, Japan
| | - Kengo Tanabe
- Division of Cardiology, Mitsui Memorial Hospital, Tokyo, Japan
| | - Shunichi Kushida
- Department of Cardiovascular Medicine, Asahi General Hospital, Chiba, Japan
| | - Junji Kanda
- Department of Cardiovascular Medicine, Asahi General Hospital, Chiba, Japan
| | - Mike Saji
- Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan
| | - Mamoru Nanasato
- Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan
| | - Hisataka Maki
- Division of Cardiovascular Medicine, Saitama Medical Center, Jichi Medical University, Omiya, Japan
| | - Hideo Fujita
- Division of Cardiovascular Medicine, Saitama Medical Center, Jichi Medical University, Omiya, Japan
| | - Nahoko Kato
- Department of Cardiology, Tokyo Bay Medical Center, Urayasu, Japan
| | | | - Minami Suzuki
- Department of Cardiology, JR General Hospital, Tokyo, Japan
| | | | - Naoko Sawada
- Department of Cardiology, NTT Medical Center Tokyo, Tokyo, Japan
| | - Masao Yamasaki
- Department of Cardiology, NTT Medical Center Tokyo, Tokyo, Japan
| | - Masataka Sato
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Susumu Katsushika
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroki Shinohara
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Norifumi Takeda
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Katsuhito Fujiu
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
- Department of Advanced Cardiology, The University of Tokyo, Tokyo, Japan
| | - Masao Daimon
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
- Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroshi Akazawa
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
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Barros A, German Mesner I, Nguyen NR, Moorman JR. Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset. Physiol Meas 2024; 45:08NT01. [PMID: 39048099 PMCID: PMC11334242 DOI: 10.1088/1361-6579/ad6746] [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: 02/27/2024] [Revised: 06/05/2024] [Accepted: 07/24/2024] [Indexed: 07/27/2024]
Abstract
Objective.The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediately apparent to human interpreters including age and sex. Several models have been published but no direct comparisons exist.Approach.We implemented three previously published models and one unpublished model to predict age and sex from a 12-lead ECG and then compared their performance on an open-access data set.Main results.All models converged and were evaluated on the holdout set. The best preforming age prediction model had a hold-out set mean absolute error of 8.06 years. The best preforming sex prediction model had a hold-out set area under the receiver operating curve of 0.92.Significance.We compared performance of four models on an open-access dataset.
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Affiliation(s)
- Andrew Barros
- Center for Advanced Medical Analytics (CAMA), School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Ian German Mesner
- Center for Advanced Medical Analytics (CAMA), School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - N Rich Nguyen
- Center for Advanced Medical Analytics (CAMA), School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States of America
| | - J Randall Moorman
- Center for Advanced Medical Analytics (CAMA), School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
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Carrick RT, Ahamed H, Sung E, Maron MS, Madias C, Avula V, Studley R, Bao C, Bokhari N, Quintana E, Rajesh-Kannan R, Maron BJ, Wu KC, Rowin EJ. Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach. Heart Rhythm 2024; 21:1390-1397. [PMID: 38280624 PMCID: PMC11272903 DOI: 10.1016/j.hrthm.2024.01.031] [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: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/21/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide. OBJECTIVE The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features. METHODS Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India). RESULTS ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%. CONCLUSION In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.
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Affiliation(s)
- Richard T Carrick
- Johns Hopkins University School of Medicine, Heart and Vascular Institute, Baltimore, Maryland.
| | - Hisham Ahamed
- Amrita Institute of Medical Sciences and Research Centre, Amrita Hypertrophic Cardiomyopathy Center, Kochi, Kerala, India
| | - Eric Sung
- Johns Hopkins University School of Medicine, Heart and Vascular Institute, Baltimore, Maryland
| | - Martin S Maron
- Lahey Hospital and Medical Center, Hypertrophic Cardiomyopathy Center, Burlington, Massachusetts
| | | | - Vennela Avula
- Johns Hopkins University School of Medicine, Heart and Vascular Institute, Baltimore, Maryland
| | - Rachael Studley
- Tufts Medical Center, Cardiac Arrhythmia Center, Boston, Massachusetts
| | - Chen Bao
- Tufts Medical Center, Cardiac Arrhythmia Center, Boston, Massachusetts
| | - Nadia Bokhari
- Tufts Medical Center, Cardiac Arrhythmia Center, Boston, Massachusetts
| | - Erick Quintana
- Tufts Medical Center, Cardiac Arrhythmia Center, Boston, Massachusetts
| | - Ramiah Rajesh-Kannan
- Amrita Institute of Medical Sciences and Research Centre, Amrita Hypertrophic Cardiomyopathy Center, Kochi, Kerala, India
| | - Barry J Maron
- Lahey Hospital and Medical Center, Hypertrophic Cardiomyopathy Center, Burlington, Massachusetts
| | - Katherine C Wu
- Johns Hopkins University School of Medicine, Heart and Vascular Institute, Baltimore, Maryland
| | - Ethan J Rowin
- Lahey Hospital and Medical Center, Hypertrophic Cardiomyopathy Center, Burlington, Massachusetts
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Harmon DM, Liu K, Dugan J, Jentzer JC, Attia ZI, Friedman PA, Dillon JJ. Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings. Clin J Am Soc Nephrol 2024; 19:952-958. [PMID: 39116276 PMCID: PMC11321728 DOI: 10.2215/cjn.0000000000000483] [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: 10/12/2023] [Accepted: 06/11/2024] [Indexed: 06/23/2024]
Abstract
Background Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. Methods An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). Results The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort (N=2636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. Conclusions The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.
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Affiliation(s)
- David M. Harmon
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Kan Liu
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Jennifer Dugan
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Jacob C. Jentzer
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Zachi I. Attia
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Paul A. Friedman
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - John J. Dillon
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
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9
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Takase B, Ikeda T, Shimizu W, Abe H, Aiba T, Chinushi M, Koba S, Kusano K, Niwano S, Takahashi N, Takatsuki S, Tanno K, Watanabe E, Yoshioka K, Amino M, Fujino T, Iwasaki Y, Kohno R, Kinoshita T, Kurita Y, Masaki N, Murata H, Shinohara T, Yada H, Yodogawa K, Kimura T, Kurita T, Nogami A, Sumitomo N. JCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia. J Arrhythm 2024; 40:655-752. [PMID: 39139890 PMCID: PMC11317726 DOI: 10.1002/joa3.13052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 04/22/2024] [Indexed: 08/15/2024] Open
Affiliation(s)
| | - Takanori Ikeda
- Department of Cardiovascular MedicineToho University Faculty of Medicine
| | - Wataru Shimizu
- Department of Cardiovascular MedicineNippon Medical School
| | - Haruhiko Abe
- Department of Heart Rhythm ManagementUniversity of Occupational and Environmental HealthJapan
| | - Takeshi Aiba
- Department of Clinical Laboratory Medicine and GeneticsNational Cerebral and Cardiovascular Center
| | | | - Shinji Koba
- Division of Cardiology, Department of MedicineShowa University School of Medicine
| | - Kengo Kusano
- Department of Cardiovascular MedicineNational Cerebral and Cardiovascular Center
| | - Shinichi Niwano
- Department of Cardiovascular MedicineKitasato University School of Medicine
| | - Naohiko Takahashi
- Department of Cardiology and Clinical Examination, Faculty of MedicineOita University
| | | | - Kaoru Tanno
- Cardiovascular Center, Cardiology DivisionShowa University Koto‐Toyosu Hospital
| | - Eiichi Watanabe
- Division of Cardiology, Department of Internal MedicineFujita Health University Bantane Hospital
| | | | - Mari Amino
- Department of CardiologyTokai University School of Medicine
| | - Tadashi Fujino
- Department of Cardiovascular MedicineToho University Faculty of Medicine
| | - Yu‐ki Iwasaki
- Department of Cardiovascular MedicineNippon Medical School
| | - Ritsuko Kohno
- Department of Heart Rhythm ManagementUniversity of Occupational and Environmental HealthJapan
| | - Toshio Kinoshita
- Department of Cardiovascular MedicineToho University Faculty of Medicine
| | - Yasuo Kurita
- Cardiovascular Center, Mita HospitalInternational University of Health and Welfare
| | - Nobuyuki Masaki
- Department of Intensive Care MedicineNational Defense Medical College
| | | | - Tetsuji Shinohara
- Department of Cardiology and Clinical Examination, Faculty of MedicineOita University
| | - Hirotaka Yada
- Department of CardiologyInternational University of Health and Welfare Mita Hospital
| | - Kenji Yodogawa
- Department of Cardiovascular MedicineNippon Medical School
| | - Takeshi Kimura
- Cardiovascular MedicineKyoto University Graduate School of Medicine
| | | | - Akihiko Nogami
- Department of Cardiology, Faculty of MedicineUniversity of Tsukuba
| | - Naokata Sumitomo
- Department of Pediatric CardiologySaitama Medical University International Medical Center
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10
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Marvasti TB, Gao Y, Murray KR, Hershman S, McIntosh C, Moayedi Y. Unlocking Tomorrow's Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence. Can J Cardiol 2024:S0828-282X(24)00561-0. [PMID: 39025363 DOI: 10.1016/j.cjca.2024.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 07/20/2024] Open
Abstract
As an integral aspect of health care, digital technology has enabled modelling of complex relationships to detect, screen, diagnose, and predict patient outcomes. With massive data sets, artificial intelligence (AI) can have marked effects on 3 levels: for patients, clinicians, and health systems. In this review, we discuss contemporary AI-enabled wearable devices undergoing research in the field of cardiovascular medicine. These include devices such as smart watches, electrocardiogram patches, and smart textiles such as smart socks and chest sensors for diagnosis, management, and prognostication of conditions such as atrial fibrillation, heart failure, and hypertension as well as monitoring for cardiac rehabilitation. We review the evolution of machine learning algorithms used in wearable devices from random forest models to the use of convolutional neural networks and transformers. We further discuss frameworks for wearable technologies such as the V3-stage process of verification, analytical validation, and clinical validation as well as challenges of AI integration in medicine such as data veracity, validity, and security and provide a reference framework to maintain fairness and equity. Last, clinician and patient perspectives are discussed to highlight the importance of considering end-user feedback in development and regulatory processes.
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Affiliation(s)
- Tina Binesh Marvasti
- Ted Rogers Centre for Heart Research, Ajmera Transplant Centre, University of Toronto, Toronto, Ontario, Canada
| | - Yuan Gao
- Ted Rogers Centre for Heart Research, Ajmera Transplant Centre, University of Toronto, Toronto, Ontario, Canada
| | - Kevin R Murray
- Ted Rogers Centre for Heart Research, Ajmera Transplant Centre, University of Toronto, Toronto, Ontario, Canada
| | - Steve Hershman
- Ted Rogers Centre for Heart Research, Ajmera Transplant Centre, University of Toronto, Toronto, Ontario, Canada
| | - Chris McIntosh
- Ted Rogers Centre for Heart Research, Ajmera Transplant Centre, University of Toronto, Toronto, Ontario, Canada
| | - Yasbanoo Moayedi
- Ted Rogers Centre for Heart Research, Ajmera Transplant Centre, University of Toronto, Toronto, Ontario, Canada; Ajmera Transplant Centre, University of Toronto, Toronto, Ontario, Canada
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11
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Barros A, German-Mesner I, Rich Nguyen N, Moorman JR. Age Prediction From 12-lead Electrocardiograms Using Deep Learning: A Comparison of Four Models on a Contemporary, Freely Available Dataset. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.02.24302201. [PMID: 38352374 PMCID: PMC10862990 DOI: 10.1101/2024.02.02.24302201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
Objective The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediately apparent to human interpreters including age and sex. Several models have been published but no direct comparisons exist. Approach We implemented three previously published models and one unpublished model to predict age and sex from a 12-lead ECG and then compared their performance on an open-access data set. Main results All models converged and were evaluated on the holdout set. The best preforming age prediction model had a hold-out set mean absolute error of 8.06 years. The best preforming sex prediction model had a hold-out set area under the receiver operating curve of 0.92. Significance We compared performance of four models on an open-access dataset.
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12
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Matasic DS, Zeitoun R, Fonarow GC, Razavi AC, Blumenthal RS, Gulati M. Advancements in Incident Heart Failure Risk Prediction and Screening Tools. Am J Cardiol 2024; 227:105-110. [PMID: 39029721 DOI: 10.1016/j.amjcard.2024.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/08/2024] [Accepted: 07/11/2024] [Indexed: 07/21/2024]
Abstract
Heart failure (HF) is a major cause of mortality and morbidity in the United States that carries substantial healthcare costs. Multiple risk prediction models and strategies have been developed over the past 30 years with the aim of identifying those at high risk of developing HF and of implementing preventive therapies effectively. This review highlights recent developments in HF risk prediction tools including emerging risk factors, innovative risk prediction models, and novel screening strategies from artificial intelligence to biomarkers. These developments allow more accurate prediction, but their impact on clinical outcomes remains to be investigated. Implementation of these risk models in clinical practice is a considerable challenge, but HF risk prediction tools offer a promising opportunity to improve outcomes while maintaining value.
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Affiliation(s)
- Daniel S Matasic
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Johns Hopkins University, Baltimore, Maryland
| | - Ralph Zeitoun
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Johns Hopkins University, Baltimore, Maryland
| | - Gregg C Fonarow
- Division of Cardiology, University of California Los Angeles, Los Angeles, California
| | - Alexander C Razavi
- Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
| | - Roger S Blumenthal
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Johns Hopkins University, Baltimore, Maryland
| | - Martha Gulati
- Barbra Streisand Women's Heart Center, Cedars-Sinai Smidt Heart Institute, Los Angeles, California.
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13
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Oikonomou EK, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic J Cardiol 2024:S1109-9666(24)00158-1. [PMID: 39025234 DOI: 10.1016/j.hjc.2024.07.003] [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: 03/11/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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14
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Sabino EC, Nunes MCP, Blum J, Molina I, Ribeiro ALP. Cardiac involvement in Chagas disease and African trypanosomiasis. Nat Rev Cardiol 2024:10.1038/s41569-024-01057-3. [PMID: 39009679 DOI: 10.1038/s41569-024-01057-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/13/2024] [Indexed: 07/17/2024]
Abstract
Trypanosomiases are diseases caused by various species of protozoan parasite in the genus Trypanosoma, each presenting with distinct clinical manifestations and prognoses. Infections can affect multiple organs, with Trypanosoma cruzi predominantly affecting the heart and digestive system, leading to American trypanosomiasis or Chagas disease, and Trypanosoma brucei primarily causing a disease of the central nervous system known as human African trypanosomiasis or sleeping sickness. In this Review, we discuss the effects of these infections on the heart, with particular emphasis on Chagas disease, which continues to be a leading cause of cardiomyopathy in Latin America. The epidemiology of Chagas disease has changed substantially since 1990 owing to the emigration of over 30 million Latin American citizens, primarily to Europe and the USA. This movement of people has led to the global dissemination of individuals infected with T. cruzi. Therefore, cardiologists worldwide must familiarize themselves with Chagas disease and the severe, chronic manifestation - Chagas cardiomyopathy - because of the expanded prevalence of this disease beyond traditional endemic regions.
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Affiliation(s)
- Ester Cerdeira Sabino
- Department of Pathology, Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
| | - Maria Carmo P Nunes
- Hospital das Clínicas and Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
| | - Johannes Blum
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Israel Molina
- International Health Unit Vall d'Hebron-Drassanes, Infectious Diseases Department, Vall d'Hebron University Hospital, PROSICS Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Antonio Luiz P Ribeiro
- Hospital das Clínicas and Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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15
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Islam MS, Kalmady SV, Hindle A, Sandhu R, Sun W, Sepehrvand N, Greiner R, Kaul P. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications. Can J Cardiol 2024:S0828-282X(24)00523-3. [PMID: 38992812 DOI: 10.1016/j.cjca.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.
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Affiliation(s)
- Md Saiful Islam
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sunil Vasu Kalmady
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Padma Kaul
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
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16
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Oikonomou EK, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. Eur Heart J 2024:ehae415. [PMID: 38976371 DOI: 10.1093/eurheartj/ehae415] [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: 02/11/2024] [Revised: 04/23/2024] [Accepted: 06/18/2024] [Indexed: 07/10/2024] Open
Abstract
The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, 195 Church St, 6th Floor, New Haven, CT 06510, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 College Street, New Haven, 06511 CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06510 CT, USA
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17
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Cho Y, Yoon M, Kim J, Lee JH, Oh IY, Lee CJ, Kang SM, Choi DJ. Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study. J Med Internet Res 2024; 26:e52139. [PMID: 38959500 PMCID: PMC11255523 DOI: 10.2196/52139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 02/22/2024] [Accepted: 05/29/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability. OBJECTIVE We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF. METHODS We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF). RESULTS Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001). CONCLUSIONS Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients. TRIAL REGISTRATION ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.
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Affiliation(s)
- Youngjin Cho
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea
- ARPI Inc, Seongnam, Gyeonggi-do, Republic of Korea
| | - Minjae Yoon
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea
| | - Joonghee Kim
- ARPI Inc, Seongnam, Gyeonggi-do, Republic of Korea
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, Republic of Korea
| | - Ji Hyun Lee
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea
| | - Il-Young Oh
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea
| | - Chan Joo Lee
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seok-Min Kang
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong-Ju Choi
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea
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18
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von Bachmann P, Gedon D, Gustafsson FK, Ribeiro AH, Lampa E, Gustafsson S, Sundström J, Schön TB. Evaluating regression and probabilistic methods for ECG-based electrolyte prediction. Sci Rep 2024; 14:15273. [PMID: 38961109 PMCID: PMC11222546 DOI: 10.1038/s41598-024-65223-w] [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: 02/02/2024] [Accepted: 06/18/2024] [Indexed: 07/05/2024] Open
Abstract
Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool. We analyze our DNN models on a novel dataset of over 290,000 ECGs across four major electrolytes and compare their performance with traditional machine learning models. For improved understanding, we also study the full spectrum from continuous predictions to a binary classification of extreme concentration levels. Finally, we investigate probabilistic regression approaches and explore uncertainty estimates for enhanced clinical usefulness. Our results show that DNNs outperform traditional models but model performance varies significantly across different electrolytes. While discretization leads to good classification performance, it does not address the original problem of continuous concentration level prediction. Probabilistic regression has practical potential, but our uncertainty estimates are not perfectly calibrated. Our study is therefore a first step towards developing an accurate and reliable ECG-based method for electrolyte concentration level prediction-a method with high potential impact within multiple clinical scenarios.
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Affiliation(s)
| | - Daniel Gedon
- Department of Information Technology, Uppsala University, Uppsala, Sweden.
| | - Fredrik K Gustafsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Antônio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Erik Lampa
- Clinical Epidemiology Unit, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Stefan Gustafsson
- Clinical Epidemiology Unit, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Johan Sundström
- Clinical Epidemiology Unit, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Thomas B Schön
- Department of Information Technology, Uppsala University, Uppsala, Sweden
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19
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Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, Topol EJ. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review. J Am Coll Cardiol 2024; 84:97-114. [PMID: 38925729 DOI: 10.1016/j.jacc.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: 04/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), New Haven, Connecticut, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Girish N Nadkarni
- The Samuel Bronfman Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica R Morley
- Digital Ethics Center, Yale University, New Haven, Connecticut, USA
| | - Jenna Wiens
- Electrical Engineering and Computer Science, Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA; Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California, USA
| | - Eric J Topol
- Molecular Medicine, Scripps Research Translational Institute, Scripps Research, La Jolla, California, USA
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20
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O'Sullivan D, Anjewierden S, Greason G, Attia IZ, Lopez-Jimenez F, Friedman PA, Noseworthy P, Anderson J, Kashou A, Asirvatham SJ, Eidem BW, Johnson JN, Niaz T, Madhavan M. Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development. NPJ Digit Med 2024; 7:176. [PMID: 38956410 PMCID: PMC11220019 DOI: 10.1038/s41746-024-01165-x] [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: 12/08/2023] [Accepted: 06/12/2024] [Indexed: 07/04/2024] Open
Abstract
AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987-2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0-7 years), peripubertal (8-14 years), and postpubertal (15-18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model's discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.
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Affiliation(s)
- Donnchadh O'Sullivan
- Department of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, MN, USA.
| | - Scott Anjewierden
- Department of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, MN, USA
| | - Grace Greason
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Peter Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jason Anderson
- Department of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, MN, USA
| | - Anthony Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Benjamin W Eidem
- Department of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, MN, USA
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jonathan N Johnson
- Department of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, MN, USA
| | - Talha Niaz
- Department of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, MN, USA
| | - Malini Madhavan
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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21
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Nogimori Y, Sato K, Takamizawa K, Ogawa Y, Tanaka Y, Shiraga K, Masuda H, Matsui H, Kato M, Daimon M, Fujiu K, Inuzuka R. Prediction of adverse cardiovascular events in children using artificial intelligence-based electrocardiogram. Int J Cardiol 2024; 406:132019. [PMID: 38579941 DOI: 10.1016/j.ijcard.2024.132019] [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: 09/12/2023] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND Convolutional neural networks (CNNs) have emerged as a novel method for evaluating heart failure (HF) in adult electrocardiograms (ECGs). However, such CNNs are not applicable to pediatric HF, where abnormal anatomy of congenital heart defects plays an important role. ECG-based CNNs reflecting neurohormonal activation (NHA) may be a useful marker of pediatric HF. This study aimed to develop and validate an ECG-derived marker of pediatric HF that reflects the risk of future cardiovascular events. METHODS Based on 21,378 ECGs from 8324 children, a CNN was trained using B-type natriuretic peptide (BNP) and the occurrence of major adverse cardiovascular events (MACEs). The output of the model, or the electrical heart failure indicator (EHFI), was compared with the BNP regarding its ability to predict MACEs in 813 ECGs from 295 children. RESULTS EHFI achieved a better area under the curve than BNP in predicting MACEs within 180 days (0.826 versus 0.691, p = 0.03). On Cox univariable analyses, both EHFI and BNP were significantly associated with MACE (log10 EHFI: hazard ratio [HR] = 16.5, p < 0.005 and log10 BNP: HR = 4.4, p < 0.005). The time-dependent average precisions of EHFI in predicting MACEs were 32.4%-67.9% and 1.6-7.5-fold higher than those of BNP in the early period. Additionally, the MACE rate increased monotonically with EHFI, whereas the rate peaked at approximately 100 pg/mL of BNP and decreased in the higher range. CONCLUSIONS ECG-derived CNN is a novel marker of HF with different prognostic potential from BNP. CNN-based ECG analysis may provide a new guide for assessing pediatric HF.
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Affiliation(s)
| | - Kaname Sato
- Department of Pediatrics, The University of Tokyo Hospital, Japan
| | | | - Yosuke Ogawa
- Department of Pediatrics, The University of Tokyo Hospital, Japan
| | - Yu Tanaka
- Department of Pediatrics, The University of Tokyo Hospital, Japan
| | - Kazuhiro Shiraga
- Department of Pediatrics, The University of Tokyo Hospital, Japan
| | - Hitomi Masuda
- Department of Pediatrics, The University of Tokyo Hospital, Japan
| | - Hikoro Matsui
- Department of Pediatrics, The University of Tokyo Hospital, Japan
| | - Motohiro Kato
- Department of Pediatrics, The University of Tokyo Hospital, Japan
| | - Masao Daimon
- Department of Clinical Laboratory, The University of Tokyo Hospital, Japan
| | - Katsuhito Fujiu
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Japan
| | - Ryo Inuzuka
- Department of Pediatrics, The University of Tokyo Hospital, Japan.
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22
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Strodthoff N, Lopez Alcaraz JM, Haverkamp W. Prospects for artificial intelligence-enhanced electrocardiogram as a unified screening tool for cardiac and non-cardiac conditions: an explorative study in emergency care. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:454-460. [PMID: 39081937 PMCID: PMC11284007 DOI: 10.1093/ehjdh/ztae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/11/2024] [Accepted: 05/07/2024] [Indexed: 08/02/2024]
Abstract
Aims Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department. Methods and results In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner. Conclusion The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters.
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Affiliation(s)
- Nils Strodthoff
- Carl von Ossietzky Universität Oldenburg, School VI Medicine and Health Services, Department of Health Services Research, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
| | - Juan Miguel Lopez Alcaraz
- Carl von Ossietzky Universität Oldenburg, School VI Medicine and Health Services, Department of Health Services Research, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
| | - Wilhelm Haverkamp
- Charité Universitätsmedizin Berlin, Department of Cardiology and Metabolism, Clinic for Cardiology, Angiology, and Intensive Care Medicine, Berlin, Germany
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23
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Hughes JW, Somani S, Elias P, Tooley J, Rogers AJ, Poterucha T, Haggerty CM, Salerno M, Ouyang D, Ashley E, Zou J, Perez MV. Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:427-434. [PMID: 39081946 PMCID: PMC11284011 DOI: 10.1093/ehjdh/ztae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 03/28/2024] [Accepted: 04/23/2024] [Indexed: 08/02/2024]
Abstract
Aims Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models. Methods and results Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites. Conclusion Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.
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Affiliation(s)
- John Weston Hughes
- Department of Computer Science, Stanford University, 353 Jane Stanford Way, Stanford, CA 94305, USA
| | - Sulaiman Somani
- Department of Medicine, Stanford University, 1265 Pasteur Dr, Stanford, CA 94305, USA
| | - Pierre Elias
- Department of Medicine, Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, USA
| | - James Tooley
- Department of Medicine, Stanford University, 1265 Pasteur Dr, Stanford, CA 94305, USA
| | - Albert J Rogers
- Department of Medicine, Stanford University, 1265 Pasteur Dr, Stanford, CA 94305, USA
| | - Timothy Poterucha
- Department of Medicine, Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, USA
| | - Christopher M Haggerty
- Department of Medicine, Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, USA
| | - Michael Salerno
- Department of Medicine, Stanford University, 1265 Pasteur Dr, Stanford, CA 94305, USA
| | - David Ouyang
- Cedars-Sinai Medical Center, Department of Cardiology, Smidt Heart Institute, 127 S San Vicente Blvd Pavilion, Suite A3600, Los Angeles, CA 90048, USA
| | - Euan Ashley
- Department of Medicine, Stanford University, 1265 Pasteur Dr, Stanford, CA 94305, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, 1265 Welch Road, Stanford, CA 94305, USA
| | - Marco V Perez
- Department of Medicine, Stanford University, 1265 Pasteur Dr, Stanford, CA 94305, USA
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24
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Liu WT, Hsieh PH, Lin CS, Fang WH, Wang CH, Tsai CS, Hung YJ, Hsieh CB, Lin C, Tsai DJ. Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction With the Use of Electrocardiographic Artificial Intelligence: A Cost-Effectiveness Approach. Can J Cardiol 2024; 40:1310-1321. [PMID: 38092190 DOI: 10.1016/j.cjca.2023.11.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 11/06/2023] [Accepted: 11/25/2023] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm for asymptomatic LVD detection and evaluate its cost-effectiveness for opportunistic screening. METHODS In this prospective observational study, patients undergoing ECG at outpatient clinics or health check-ups were enrolled in 2 hospitals in Taiwan. Patients were stratified into LVD (left ventricular ejection fraction ≤ 40%) risk groups according to a previously developed ECG algorithm. The performance of AI-ECG was used to conduct a cost-effectiveness analysis of LVD screening compared with no screening. Incremental cost-effectiveness ratio (ICER) and sensitivity analyses were used to examine the cost-effectiveness and robustness of the results. RESULTS Among the 29,137 patients, the algorithm demonstrated areas under the receiver operating characteristic curves of 0.984 and 0.945 for detecting LVD within 28 days in the 2 hospital cohorts. For patients not initially scheduled for ECG, the algorithm predicted future echocardiograms (high-risk, 46.2%; medium-risk, 31.4%; low-risk, 14.6%) and LVD (high-risk, 26.2%; medium-risk, 3.4%; low-risk, 0.1%) at 12 months. Opportunistic screening with AI-ECG could result in a negative ICER of -$7,439 for patients aged 65 years, with consistent cost-savings across age groups and particularly in men. Approximately 91.5% of the cases were found to be cost-effective at the willingness-to-pay threshold of $30,000 in the probabilistic analysis. CONCLUSIONS The use of AI-ECG for asymptomatic LVD risk stratification is promising, and opportunistic screening in outpatient clinics has the potential to reduce costs.
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Affiliation(s)
- Wei-Ting Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ping-Hsuan Hsieh
- School of Pharmacy, National Denfense Medical Center, Taipei, Taiwan
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Wen-Hui Fang
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan; Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chih-Hung Wang
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chien-Sung Tsai
- Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Yi-Jen Hung
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chung-Bao Hsieh
- Division of General Surgery, Department of Surgery, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
| | - Chin Lin
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan; Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Dung-Jang Tsai
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan; Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan; Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan.
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25
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Ju Y, Waugh JL, Singh S, Rusin CG, Patel AB, Jain PN. A multimodal deep learning tool for detection of junctional ectopic tachycardia in children with congenital heart disease. Heart Rhythm O2 2024; 5:452-459. [PMID: 39119021 PMCID: PMC11305876 DOI: 10.1016/j.hroo.2024.04.014] [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: 08/10/2024] Open
Abstract
Background Junctional ectopic tachycardia (JET) is a prevalent life-threatening arrhythmia in children with congenital heart disease. It has a marked resemblance to normal sinus rhythm, often leading to delay in diagnosis and management. Objective The study sought to develop a novel multimodal automated arrhythmia detection tool that outperforms existing JET detection tools. Methods This is a cohort study performed on 40 patients with congenital heart disease at Texas Children's Hospital. Electrocardiogram and central venous pressure waveform data produced by bedside monitors are captured by the Sickbay platform. Convolutional neural networks (CNNs) were trained to classify each heartbeat as either normal sinus rhythm or JET based only on raw electrocardiogram signals. Results Our best model improved the area under the curve from 0.948 to 0.952 and the true positive rate at 5% false positive rate from 71.8% to 80.6%. Using a 3-model ensemble further improved the area under the curve to 0.953 and the true positive rate at 5% false positive rate to 85.2%. Results on a subset of data show that adding central venous pressure can significantly improve area under the receiver-operating characteristic curve from 0.646 to 0.825. Conclusion This study validates the efficacy of deep neural networks to notably improve JET detection accuracy. We have built a performant and reliable model that can be used to create a bedside alarm that diagnoses JET, allowing for precise diagnosis of this life-threatening postoperative arrhythmia and prompt intervention. Future validation of the model in a larger cohort is needed.
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Affiliation(s)
- Yilong Ju
- Department of Computer Science, Rice University, Houston, Texas
| | | | - Satpreet Singh
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts
| | - Craig G. Rusin
- Division of Cardiology, Department of Pediatrics, Baylor College of Medicine, Houston, Texas
- Department of Pediatrics, Texas Children’s Hospital, Houston, Texas
| | - Ankit B. Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas
| | - Parag N. Jain
- Division of Critical Care Medicine, Department of Pediatrics, Baylor College of Medicine, Texas Children’s Hospital, Houston, Texas
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26
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Howard JP, Vasudevan N, Sarkar S, Landman S, Koehler J, Keene D. Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:435-443. [PMID: 39081943 PMCID: PMC11284004 DOI: 10.1093/ehjdh/ztae035] [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: 11/26/2023] [Revised: 01/27/2024] [Accepted: 02/21/2024] [Indexed: 08/02/2024]
Abstract
Aims Implantable loop recorders (ILRs) provide continuous single-lead ambulatory electrocardiogram (aECG) monitoring. Whether these aECGs could be used to identify worsening heart failure (HF) is unknown. Methods and results We linked ILR aECG from Medtronic device database to the left ventricular ejection fraction (LVEF) measurements in Optum® de-identified electronic health record dataset. We trained an artificial intelligence (AI) algorithm [aECG-convolutional neural network (CNN)] on a dataset of 35 741 aECGs from 2247 patients to identify LVEF ≤ 40% and assessed its performance using the area under the receiver operating characteristic curve. Ambulatory electrocardiogram-CNN was then used to identify patients with increasing risk of HF hospitalization in a real-world cohort of 909 patients with prior HF diagnosis. This dataset provided 12 467 follow-up monthly evaluations, with 201 HF hospitalizations. For every month, time-series features from these predictions were used to categorize patients into high- and low-risk groups and predict HF hospitalization in the next month. The risk of HF hospitalization in the next 30 days was significantly higher in the cohort that aECG-CNN identified as high risk [hazard ratio (HR) 1.89; 95% confidence interval (CI) 1.28-2.79; P = 0.001] compared with low risk, even after adjusting patient demographics (HR 1.88; 95% CI 1.27-2.79 P = 0.002). Conclusion An AI algorithm trained to detect LVEF ≤40% using ILR aECGs can also readily identify patients at increased risk of HF hospitalizations by monitoring changes in the probability of HF over 30 days.
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Affiliation(s)
- James Philip Howard
- National Heart and Lung Institute, Imperial College London, Du Cane Road, W12 0HS, London, UK
| | - Neethu Vasudevan
- Research and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA
| | - Shantanu Sarkar
- Research and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA
| | - Sean Landman
- Research and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA
| | - Jodi Koehler
- Research and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA
| | - Daniel Keene
- National Heart and Lung Institute, Imperial College London, Du Cane Road, W12 0HS, London, UK
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27
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Bauer A, Dlaska C. Implantable cardiac monitors: the digital future of risk prediction? EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:397-398. [PMID: 39081935 PMCID: PMC11284001 DOI: 10.1093/ehjdh/ztae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Affiliation(s)
- Axel Bauer
- Department of Cardiology, Medical University of Innsbruck, Anichstr. 35, 6020 Innsbruck, Austria
| | - Clemens Dlaska
- Department of Cardiology, Medical University of Innsbruck, Anichstr. 35, 6020 Innsbruck, Austria
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28
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DuBrock HM, Wagner TE, Carlson K, Carpenter CL, Awasthi S, Attia ZI, Frantz RP, Friedman PA, Kapa S, Annis J, Brittain EL, Hemnes AR, Asirvatham SJ, Babu M, Prasad A, Yoo U, Barve R, Selej M, Agron P, Kogan E, Quinn D, Dunnmon P, Khan N, Soundararajan V. An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension. Eur Respir J 2024; 64:2400192. [PMID: 38936966 PMCID: PMC11269769 DOI: 10.1183/13993003.00192-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 05/19/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG. METHODS The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. RESULTS Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. CONCLUSION The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.
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Affiliation(s)
- Hilary M DuBrock
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
- Co-first authors
| | - Tyler E Wagner
- nference, Cambridge, MA, USA
- Anumana, Cambridge, MA, USA
- Co-first authors
| | | | | | - Samir Awasthi
- nference, Cambridge, MA, USA
- Anumana, Cambridge, MA, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Robert P Frantz
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jeffrey Annis
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - Evan L Brittain
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anna R Hemnes
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Melwin Babu
- Anumana, Cambridge, MA, USA
- nference Labs, Bangalore, India
| | - Ashim Prasad
- Anumana, Cambridge, MA, USA
- nference Labs, Bangalore, India
| | | | - Rakesh Barve
- Anumana, Cambridge, MA, USA
- nference Labs, Bangalore, India
| | - Mona Selej
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Peter Agron
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Emily Kogan
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Deborah Quinn
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Preston Dunnmon
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Najat Khan
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
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29
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Whitman M, Tilley P, Padayachee C, Jenkins C, Challa P. Energy wavelet signal processed ECG and standard 12 lead ECG: Diagnosis of early diastolic dysfunction. J Electrocardiol 2024; 85:1-6. [PMID: 38762938 DOI: 10.1016/j.jelectrocard.2024.05.001] [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: 07/30/2023] [Revised: 01/23/2024] [Accepted: 05/03/2024] [Indexed: 05/21/2024]
Abstract
BACKGROUND Left ventricular (LV) diastolic dysfunction (LVDD) is the result of impaired LV relaxation and identifies those at risk of developing heart failure. Echocardiography has been used as the gold standard to identify early LVDD. The signal processed electrocardiogram (hsECG) has demonstrated effectiveness to detect early LVDD. Whether or not the standard 12‑lead electrocardiogram (ECG) can accurately predict early LVDD is not known. METHODS A standard 12‑lead ECG including signal processing (hsECG) was performed in 569 patients. Patients with atrial fibrillation, bundle branch block, pre-excitation, left ventricular hypertrophy or known cardiovascular disease were excluded, leaving 464 examinations for analysis. Early LVDD was diagnosed by established methods using echocardiography. Repolarization abnormalities (T wave discordance) in V1, V6, I and aVL and the hsECG were compared to the echocardiographic findings to establish diagnostic accuracy. RESULTS A total of 84 (18.1%) patients were diagnosed with early LVDD. A combination of a borderline or abnormal finding on the hsECG produced the best diagnostic model (sensitivity 84.5%, specificity 47.9%). The best performing ECG lead was V1 with a sensitivity of 38.1% and specificity of 92.1%. Regression analysis demonstrated increasing age and V1 to be predictive of LVDD. CONCLUSIONS The hsECG displayed reasonable ability to detect early LVDD. Other than V1, repolarization abnormalities on the standard 12‑lead ECG did not. While lead V1 showed promise in detecting LVDD, whether this or any other simple ECG variable can predict future LVDD would be of further interest.
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Affiliation(s)
- Mark Whitman
- Cardiac Investigations Unit, Logan Hospital, Meadowbrook, Australia; School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, Australia.
| | - Prue Tilley
- Cardiac Investigations Unit, Logan Hospital, Meadowbrook, Australia
| | | | - Carly Jenkins
- Cardiac Investigations Unit, Logan Hospital, Meadowbrook, Australia
| | - Prasad Challa
- Division of Cardiology, Logan Hospital, Meadowbrook, Australia
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Singh M, Kumar A, Khanna NN, Laird JR, Nicolaides A, Faa G, Johri AM, Mantella LE, Fernandes JFE, Teji JS, Singh N, Fouda MM, Singh R, Sharma A, Kitas G, Rathore V, Singh IM, Tadepalli K, Al-Maini M, Isenovic ER, Chaturvedi S, Garg D, Paraskevas KI, Mikhailidis DP, Viswanathan V, Kalra MK, Ruzsa Z, Saba L, Laine AF, Bhatt DL, Suri JS. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review. EClinicalMedicine 2024; 73:102660. [PMID: 38846068 PMCID: PMC11154124 DOI: 10.1016/j.eclinm.2024.102660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Background The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding No funding received.
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Affiliation(s)
- Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Bennett University, 201310, Greater Noida, India
| | - Ashish Kumar
- Bennett University, 201310, Greater Noida, India
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Gavino Faa
- Department of Pathology, University of Cagliari, Cagliari, Italy
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura E. Mantella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | | | - Jagjit S. Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, 22901, VA, USA
| | - George Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, DY1, Dudley, UK
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | | | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada
| | - Esma R. Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 110010, Serbia
| | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | | | | | - Dimitri P. Mikhailidis
- Department of Clinical Biochemistry, Royal Free Hospital Campus, University College London Medical School, University College London (UCL), London, UK
| | | | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Andrew F. Laine
- Departments of Biomedical and Radiology, Columbia University, New York, NY, USA
| | | | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
- Department of Computer Science, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
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Huecker M, Schutzman C, French J, El-Kersh K, Ghafghazi S, Desai R, Frick D, Thomas JJ. Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study. JMIR Cardio 2024; 8:e57111. [PMID: 38924781 PMCID: PMC11237790 DOI: 10.2196/57111] [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: 02/05/2024] [Revised: 03/19/2024] [Accepted: 04/10/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients. OBJECTIVE This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone. METHODS This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness. RESULTS Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion. CONCLUSIONS This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.
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Affiliation(s)
- Martin Huecker
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Craig Schutzman
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Joshua French
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Karim El-Kersh
- Department of Pulmonary and Critical Care Medicine, The University of Arizona, Phoenix, AZ, United States
| | - Shahab Ghafghazi
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Ravi Desai
- Lehigh Valley Health Network Cardiology and Critical Care, Allentown, PA, United States
| | - Daniel Frick
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Jarred Jeremy Thomas
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
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32
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Pastika L, Sau A, Patlatzoglou K, Sieliwonczyk E, Ribeiro AH, McGurk KA, Khan S, Mandic D, Scott WR, Ware JS, Peters NS, Ribeiro ALP, Kramer DB, Waks JW, Ng FS. Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease. NPJ Digit Med 2024; 7:167. [PMID: 38918595 PMCID: PMC11199586 DOI: 10.1038/s41746-024-01170-0] [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: 01/24/2024] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.
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Affiliation(s)
- Libor Pastika
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Arunashis Sau
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | | | - Ewa Sieliwonczyk
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom
| | - Antônio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Kathryn A McGurk
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom
| | - Sadia Khan
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - William R Scott
- MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom
| | - Nicholas S Peters
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Daniel B Kramer
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Jonathan W Waks
- Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom.
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.
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Doolub G, Khurshid S, Theriault-Lauzier P, Nolin Lapalme A, Tastet O, So D, Labrecque Langlais E, Cobin D, Avram R. Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges. Can J Cardiol 2024:S0828-282X(24)00443-4. [PMID: 38901544 DOI: 10.1016/j.cjca.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/29/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.
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Affiliation(s)
- Gemina Doolub
- Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Alexis Nolin Lapalme
- Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada; Mila-Québec AI Institute, Montréal, Québec, Canada
| | - Olivier Tastet
- Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada
| | - Derek So
- University of Ottawa, Heart Institute, Ottawa, Ontario, Canada
| | | | - Denis Cobin
- Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada
| | - Robert Avram
- Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada.
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Elias P, Jain SS, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:2472-2486. [PMID: 38593946 DOI: 10.1016/j.jacc.2024.03.400] [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: 03/01/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA
| | - Sneha S Jain
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - James Pirruccello
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew J Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Geoffrey H Tison
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, New York, USA
| | - Ashley Beecy
- NewYork-Presbyterian Health System, New York, New York, USA; Division of Cardiology, Weill Cornell Medical College, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Jennifer N Avari Silva
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA.
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Ose B, Sattar Z, Gupta A, Toquica C, Harvey C, Noheria A. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Curr Cardiol Rep 2024; 26:561-580. [PMID: 38753291 DOI: 10.1007/s11886-024-02062-1] [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: 04/17/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. RECENT FINDINGS Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.
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Affiliation(s)
- Benjamin Ose
- The University of Kansas School of Medicine, Kansas City, KS, USA
| | - Zeeshan Sattar
- Division of General and Hospital Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amulya Gupta
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Chris Harvey
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA.
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Nishii N, Baba K, Morooka K, Shirae H, Mizuno T, Masuda T, Ueoka A, Asada S, Miyamoto M, Ejiri K, Kawada S, Nakagawa K, Nakamura K, Morita H, Yuasa S. Artificial intelligence to detect noise events in remote monitoring data. J Arrhythm 2024; 40:560-577. [PMID: 38939795 PMCID: PMC11199815 DOI: 10.1002/joa3.13037] [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: 01/25/2024] [Revised: 03/21/2024] [Accepted: 03/30/2024] [Indexed: 06/29/2024] Open
Abstract
Background Remote monitoring (RM) of cardiac implantable electrical devices (CIEDs) can detect various events early. However, the diagnostic ability of CIEDs has not been sufficient, especially for lead failure. The first notification of lead failure was almost noise events, which were detected as arrhythmia by the CIED. A human must analyze the intracardiac electrogram to accurately detect lead failure. However, the number of arrhythmic events is too large for human analysis. Artificial intelligence (AI) seems to be helpful in the early and accurate detection of lead failure before human analysis. Objective To test whether a neural network can be trained to precisely identify noise events in the intracardiac electrogram of RM data. Methods We analyzed 21 918 RM data consisting of 12 925 and 1884 Medtronic and Boston Scientific data, respectively. Among these, 153 and 52 Medtronic and Boston Scientific data, respectively, were diagnosed as noise events by human analysis. In Medtronic, 306 events, including 153 noise events and randomly selected 153 out of 12 692 nonnoise events, were analyzed in a five-fold cross-validation with a convolutional neural network. The Boston Scientific data were analyzed similarly. Results The precision rate, recall rate, F1 score, accuracy rate, and the area under the curve were 85.8 ± 4.0%, 91.6 ± 6.7%, 88.4 ± 2.0%, 88.0 ± 2.0%, and 0.958 ± 0.021 in Medtronic and 88.4 ± 12.8%, 81.0 ± 9.3%, 84.1 ± 8.3%, 84.2 ± 8.3% and 0.928 ± 0.041 in Boston Scientific. Five-fold cross-validation with a weighted loss function could increase the recall rate. Conclusions AI can accurately detect noise events. AI analysis may be helpful for detecting lead failure events early and accurately.
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Affiliation(s)
- Nobuhiro Nishii
- Department of Cardiovascular TherapeuticsOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Kensuke Baba
- Cyber‐Physical Engineering Informatics Research CoreOkayama UniversityOkayamaJapan
| | - Ken'ichi Morooka
- Division of Industrial Innovation Sciences, Graduate School of Natural Science and TechnologyOkayama UniversityOkayamaJapan
| | - Haruto Shirae
- Division of Industrial Innovation Sciences, Graduate School of Natural Science and TechnologyOkayama UniversityOkayamaJapan
| | - Tomofumi Mizuno
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Takuro Masuda
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Akira Ueoka
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Saori Asada
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Masakazu Miyamoto
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Kentaro Ejiri
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Satoshi Kawada
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Koji Nakagawa
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Kazufumi Nakamura
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Hiroshi Morita
- Department of Cardiovascular TherapeuticsOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Shinsuke Yuasa
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
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Haverkamp W, Strodthoff N. [Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?]. Herzschrittmacherther Elektrophysiol 2024; 35:104-110. [PMID: 38361131 DOI: 10.1007/s00399-024-00997-0] [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: 12/19/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024]
Abstract
The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes a comprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.
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Affiliation(s)
- Wilhelm Haverkamp
- Abteilung für Kardiologie und Metabolismus, Medizinische Klinik mit Schwerpunkt Kardiologie, Campus Virchow-Klinikum, Deutsches Herzzentrum der Charité, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Deutschland.
| | - Nils Strodthoff
- Department für Versorgungsforschung, Fakultät VI - Medizin und Gesundheitswissenschaften, Abteilung AI4Health, Carl von Ossietzky Universität Oldenburg, Oldenburg, Deutschland
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38
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Grewal JS, Sengupta PP. Pitfalls and Opportunities for the Growing Role of AI in Heart Failure. J Card Fail 2024; 30:838-840. [PMID: 38479576 DOI: 10.1016/j.cardfail.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 03/04/2024] [Indexed: 04/17/2024]
Affiliation(s)
- Jagpreet S Grewal
- Division of Cardiology, Department of Medicine, Robert Wood Johnson University Hospital, New Brunswick, NJ
| | - Partho P Sengupta
- Division of Cardiology, Department of Medicine, Robert Wood Johnson University Hospital, New Brunswick, NJ.
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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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Carbonati T, Eslami P, Waks JW, Fiorina L, Chaudhari A, Henry C, Johnson AE, Pollard T, Gow B, Mark RG, Horng S, Greenbaum NR. Deep neural networks detect regional wall motion abnormalities and preclinical cardiovascular disease from 12-lead ECGs. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.31.24308304. [PMID: 38854156 PMCID: PMC11160848 DOI: 10.1101/2024.05.31.24308304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Background Identifying regional wall motion abnormalities (RWMAs) is critical for diagnosing and risk stratifying patients with cardiovascular disease, particularly ischemic heart disease. We hypothesized that a deep neural network could accurately identify patients with regional wall motion abnormalities from a readily available standard 12-lead electrocardiogram (ECG). Methods This observational, retrospective study included patients who were treated at Beth Israel Deaconess Medical Center and had an ECG and echocardiogram performed within 14 days of each other between 2008 and 2019. We trained a convolutional neural network to detect the presence of RWMAs, qualitative global right ventricular (RV) hypokinesis, and varying degrees of left ventricular dysfunction (left ventricular ejection fraction [LVEF] ≤50%, LVEF ≤40%, and LVEF ≤35%) identified by echocardiography, using ECG data alone. Patients were randomly split into development (80%) and test sets (20%). Model performance was assessed using area under the receiver operating characteristic curve (AUC). Cox proportional hazard models adjusted for age and sex were performed to estimate the risk of future acute coronary events. Results The development set consisted of 19,837 patients (mean age 66.7±16.4; 46.7% female) and the test set comprised of 4,953 patients (mean age 67.5±15.8 years; 46.5% female). On the test dataset, the model accurately identified the presence of RWMA, RV hypokinesis, LVEF ≤50%, LVEF ≤40%, and LVEF ≤35% with AUCs of 0.87 (95% CI 0.858-0.882), 0.888 (95% CI 0.878-0.899), 0.923 (95% CI 0.914-0.933), 0.93 (95% CI 0.921-0.939), and 0.876 (95% CI 0.858-0.896), respectively. Among patients with normal biventricular function at the time of the index ECG, those classified as having RMWA by the model were 3 times the risk (age- and sex-adjusted hazard ratio, 2.8; 95% CI 1.9-3.9) for future acute coronary events compared to those classified as negative. Conclusions We demonstrate that a deep neural network can help identify regional wall motion abnormalities and reduced LV function from a 12-lead ECG and could potentially be used as a screening tool for triaging patients who need either initial or repeat echocardiographic imaging.
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Affiliation(s)
| | | | - Jonathan W. Waks
- Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Laurent Fiorina
- Ramsay Santé, Institut Cardiovasculaire Paris Sud, Hôpital privé Jacques Cartier, 91300 Massy, France
| | | | | | - Alistair E.W. Johnson
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Tom Pollard
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Brian Gow
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Roger G. Mark
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Steven Horng
- Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Nathaniel R. Greenbaum
- Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
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Wagner P, Mehari T, Haverkamp W, Strodthoff N. Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery. Comput Biol Med 2024; 176:108525. [PMID: 38749322 DOI: 10.1016/j.compbiomed.2024.108525] [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: 09/21/2023] [Revised: 04/22/2024] [Accepted: 04/25/2024] [Indexed: 05/31/2024]
Abstract
Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the glocal (aggregated local attributions over multiple samples) and global (concept based XAI) perspectives. We have established a set of sanity checks to identify saliency as the most sensible attribution method. We provide a dataset-wide analysis across entire patient subgroups, which goes beyond anecdotal evidence, to establish the first quantitative evidence for the alignment of model behavior with cardiologists' decision rules. Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction. We believe that these proposed methods can serve as building blocks for a complementary assessment of the internal validity during a certification process, as well as for knowledge discovery in the field of ECG analysis.
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Affiliation(s)
| | - Temesgen Mehari
- Fraunhofer Heinrich Hertz Institute, Berlin, Germany; Physikalisch-Technische Bundesanstalt, Berlin, Germany.
| | | | - Nils Strodthoff
- Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
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Dhingra LS, Aminorroaya A, Camargos AP, Khunte A, Sangha V, McIntyre D, Chow CK, Asselbergs FW, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.27.24307952. [PMID: 38854022 PMCID: PMC11160804 DOI: 10.1101/2024.05.27.24307952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Importance Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design Multicohort study. Setting Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants Individuals without HF at baseline. Exposures AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.
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Affiliation(s)
- Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Akshay Khunte
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Daniel McIntyre
- Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
| | - Clara K Chow
- Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
- Department of Cardiology, Westmead Hospital, Sydney, Australia
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Luisa Cc Brant
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sandhi M Barreto
- Department of Preventive Medicine, School of Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Moody JB, Poitrasson-Rivière A, Renaud JM, Hagio T, Alahdab F, Al-Mallah MH, Vanderver MD, Ficaro EP, Murthy VL. Self-supervised deep representation learning of a foundation transformer model enabling efficient ECG-based assessment of cardiac and coronary function with limited labels. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.25.23297552. [PMID: 37961713 PMCID: PMC10635192 DOI: 10.1101/2023.10.25.23297552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background: Although deep learning methods have shown great promise for identification of structural and functional cardiac abnormalities using electrocardiographic data, these methods are data hungry, posing a challenge for critically important tasks where ground truth labels are relatively scarce. Impaired coronary microvascular and vasomotor function is difficult to identify with standard clinical methods of cardiovascular testing such as coronary angiography and noninvasive single photon emission tomography (SPECT) myocardial perfusion imaging (MPI). Gold standard data from positron emission tomography (PET) are gaining emphasis in clinical guidelines but are expensive and only available in relatively limited centers. We hypothesized that signals embedded within resting and stress electrocardiograms (ECGs) identify individuals with microvascular and vasomotor dysfunction. Methods: We developed and pretrained a self-supervised foundation vision transformer model using a large database of unlabeled ECG waveforms (N=800,035). We then fine-tuned the foundation model for two clinical tasks: the difficult problem of identifying patients with impaired myocardial flow reserve (AI-MFR), and the relatively easier problem of detecting impaired LVEF (AI-LVEF). A second ECG database was labeled with task-specific annotations derived from quantitative PET MPI (N=4167). Diagnostic accuracy of AI predictions was tested in a holdout set of patients undergoing PET MPI (N=1031). Prognostic evaluation was performed in the PET holdout cohort, as well as independent cohorts of patients undergoing pharmacologic or exercise stress SPECT MPI (N=6635). Results: The diagnostic accuracy of AI-MFR with SSL pretraining increased significantly compared to de novo supervised training (AUROC, sensitivity, specificity: 0.758, 70.1%, 69.4% vs. 0.632, 66.1%, 57.3%, p < 0.0001). SSL pretraining also produced a smaller increase in AI-LVEF accuracy (AUROC, sensitivity, specificity: 0.946, 89.4%, 85.9% vs. 0.918, 87.6%, 82.5%, p < 0.02). Abnormal AI-MFR was found to be significantly associated with mortality risk in all three test cohorts (Hazard Ratio (HR) 2.61 [95% CI 1.83, 3.71], p < 0.0001, PET cohort; HR 2.30 [2.03, 2.61], p < 0.0001, pharmacologic stress SPECT cohort; HR 3.76 [2.36, 5.99], p < 0.0001, exercise stress SPECT cohort). Conclusion: SSL pretraining of a vision transformer foundation model enabled identification of signals predictive of impaired MFR, a hallmark of microvascular and vasomotor dysfunction, and impaired LV function in resting and stress ECG waveforms. These signals are powerful predictors of prognosis in patients undergoing routine noninvasive stress testing and could enable more efficient diagnosis and management of these common conditions.
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Kalmady SV, Salimi A, Sun W, Sepehrvand N, Nademi Y, Bainey K, Ezekowitz J, Hindle A, McAlister F, Greiner R, Sandhu R, Kaul P. Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level. NPJ Digit Med 2024; 7:133. [PMID: 38762623 PMCID: PMC11102430 DOI: 10.1038/s41746-024-01130-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/26/2024] [Indexed: 05/20/2024] Open
Abstract
Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80-90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions.
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Affiliation(s)
- Sunil Vasu Kalmady
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Amir Salimi
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yousef Nademi
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Kevin Bainey
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Justin Ezekowitz
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Finlay McAlister
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Russel Greiner
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, CA, USA
| | - Padma Kaul
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada.
- Department of Medicine, University of Alberta, Edmonton, AB, Canada.
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Riaz Gondal MU, Atta Mehdi H, Khenhrani RR, Kumari N, Ali MF, Kumar S, Faraz M, Malik J. Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology. Cardiol Rev 2024:00045415-990000000-00270. [PMID: 38761137 DOI: 10.1097/crd.0000000000000715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/20/2024]
Abstract
Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.
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Affiliation(s)
| | - Hassan Atta Mehdi
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Raja Ram Khenhrani
- Department of Medicine, Internal Medicine Fellow, Shaheed Mohtarma Benazir Bhutto Medical College and Lyari General Hospital, Karachi, Pakistan
| | - Neha Kumari
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Muhammad Faizan Ali
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Sooraj Kumar
- Department of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan; and
| | - Maria Faraz
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
| | - Jahanzeb Malik
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
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Perkins AD, Awori JO, Jobe M, Lucinde RK, Siemonsma M, Oyando R, Leon DA, Herrett E, Prentice AM, Shah ASV, Perel P, Etyang A. Determining the optimal diagnostic and risk stratification approaches for people with hypertension in two rural populations in Kenya and The Gambia: a study protocol for IHCoR-Africa Work Package 2. NIHR OPEN RESEARCH 2024; 3:68. [PMID: 39139279 PMCID: PMC11319908 DOI: 10.3310/nihropenres.13509.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/15/2024] [Indexed: 08/15/2024]
Abstract
Background Sub-Saharan Africa (SSA) has one of the highest prevalences of hypertension worldwide. The impact of hypertension is of particular concern in rural SSA, where access to clinics and hospitals is limited. Improvements in the management of people with hypertension in rural SSA could be achieved by sharing diagnosis and care tasks between the clinic and the community. To develop such a community-centred programme we need optimal approaches to identify and risk stratify patients with elevated blood pressure. The aim of the study is to improve the evidence base for diagnosis and risk estimation for a community-centred hypertension programme in two rural settings in SSA. Methods We will conduct a cross-sectional study of 1250 adult participants in Kilifi, Kenya and Kiang West, The Gambia. The study has five objectives which will determine the: (1) accuracy of three blood pressure (BP) measurement methods performed by community health workers in identifying people with hypertension in rural SSA, compared to the reference standard method; (2) relationship between systolic BP and cardiovascular risk factors; (3) prevalence of hypertension-mediated organ damage (HMOD); (4) accuracy of innovative point-of-care (POC) technologies to identify patients with HMOD; and (5) cost-effectiveness of different combinations of BP and HMOD measurements for directing hypertension treatment initiation. Expected findings This study will determine the accuracy of three methods for community BP measurement and POC technologies for HMOD assessment. Using the optimal methods in this setting it will estimate the prevalence of hypertension and provide the best estimate to date of HMOD prevalence in SSA populations. The cost-effectiveness of decision-making approaches for initiating treatment of hypertension will be modelled. These results will inform the development of a community-centred programme to improve care for hypertensive patients living in rural SSA. Existing community engagement networks will be used to disseminated within the research setting.
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Affiliation(s)
- Alexander D Perkins
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Juliet Otieno Awori
- Department of Epidemiology and Demography, KEMRI Wellcome Trust Research Programme, Kilifi, Kenya
| | - Modou Jobe
- Medical Research Council Unit The Gambia at LSHTM, Banjul, The Gambia
| | - Ruth K Lucinde
- Department of Epidemiology and Demography, KEMRI Wellcome Trust Research Programme, Kilifi, Kenya
| | - Meike Siemonsma
- Medical Research Council Unit The Gambia at LSHTM, Banjul, The Gambia
| | - Robinson Oyando
- Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
| | - David A Leon
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Emily Herrett
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | | | - Anoop SV Shah
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Pablo Perel
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Anthony Etyang
- Department of Epidemiology and Demography, KEMRI Wellcome Trust Research Programme, Kilifi, Kenya
| | - The IHCoR-Africa Collaborators
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- Department of Epidemiology and Demography, KEMRI Wellcome Trust Research Programme, Kilifi, Kenya
- Medical Research Council Unit The Gambia at LSHTM, Banjul, The Gambia
- Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
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Mangold KE, Carter RE, Siontis KC, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Friedman PA, Attia ZI. Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:314-323. [PMID: 38774362 PMCID: PMC11104462 DOI: 10.1093/ehjdh/ztae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 05/24/2024]
Abstract
Aims Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.
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Affiliation(s)
- Kathryn E Mangold
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| | | | - Peter A Noseworthy
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | | | - Samuel J Asirvatham
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
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Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024; 30:1257-1268. [PMID: 38740998 DOI: 10.1038/s41591-024-02970-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.
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Affiliation(s)
- Chris Varghese
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Greg O'Grady
- Department of Surgery, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, CA, USA.
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Medhi D, Kamidi SR, Mamatha Sree KP, Shaikh S, Rasheed S, Thengu Murichathil AH, Nazir Z. Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review. Cureus 2024; 16:e59661. [PMID: 38836155 PMCID: PMC11148729 DOI: 10.7759/cureus.59661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.
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Affiliation(s)
- Diptiman Medhi
- Internal Medicine, Gauhati Medical College and Hospital, Guwahati, Guwahati, IND
| | | | | | - Shifa Shaikh
- Cardiology, SMBT Institute of Medical Sciences and Research Centre, Igatpuri, IND
| | - Shanida Rasheed
- Emergency Medicine, East Sussex Healthcare NHS Trust, Eastbourne, GBR
| | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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50
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Lu L, Zhu T, Ribeiro AH, Clifton L, Zhao E, Zhou J, Ribeiro ALP, Zhang YT, Clifton DA. Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:247-259. [PMID: 38774384 PMCID: PMC11104458 DOI: 10.1093/ehjdh/ztae014] [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: 10/02/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 05/24/2024]
Abstract
Aims Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence (AI) to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. We hypothesize that AI models with a specific design can provide fine-grained interpretation of ECGs to advance cardiovascular diagnosis, stratify mortality risks, and identify new clinically useful information. Methods and results Utilizing a data set of 2 322 513 ECGs collected from 1 558 772 patients with 7 years follow-up, we developed a deep-learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hypertension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (95% CI, 0.963-0.965), and 0.839 (95% CI, 0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep-learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Conclusion Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis and the advancement in mortality risk stratification. In addition, it demonstrated the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available.
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Affiliation(s)
- Lei Lu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
- School of Life Course and Population Sciences, King’s College London, London, SE1 1UL, UK
| | - Tingting Zhu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
| | - Antonio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Lei Clifton
- Nuffield Department of Population Health, University of Oxford Big Data Institute, Oxford, OX3 7LF, UK
| | - Erying Zhao
- Psychological Science and Health Management Center, Harbin Medical University, Harbin, 150076, China
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Jiandong Zhou
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Division of Health Science, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Yuan-Ting Zhang
- Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR, China
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
- Oxford Suzhou Centre for Advanced Research, Suzhou, 215123, China
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