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  1. Article ; Online: Explainable AI for ECG-based prediction of cardiac resynchronization therapy outcomes: learning from machine learning?

    Attia, Zachi I / Friedman, Paul A

    European heart journal

    2022  Volume 44, Issue 8, Page(s) 693–695

    MeSH term(s) Humans ; Cardiac Resynchronization Therapy ; Deep Learning ; Electrocardiography ; Machine Learning ; Prognosis
    Language English
    Publishing date 2022-12-21
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 603098-1
    ISSN 1522-9645 ; 0195-668X
    ISSN (online) 1522-9645
    ISSN 0195-668X
    DOI 10.1093/eurheartj/ehac733
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    Zs.A 1563: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 640: Show issues

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  2. Article ; Online: ChatGPT hallucinating: can it get any more humanlike?

    Siontis, Konstantinos C / Attia, Zachi I / Asirvatham, Samuel J / Friedman, Paul A

    European heart journal

    2023  Volume 45, Issue 5, Page(s) 321–323

    Language English
    Publishing date 2023-12-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 603098-1
    ISSN 1522-9645 ; 0195-668X
    ISSN (online) 1522-9645
    ISSN 0195-668X
    DOI 10.1093/eurheartj/ehad766
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    Zs.MO 640: Show issues

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  3. Article ; Online: Deep Learning for Premature Ventricular Contraction-Cardiomyopathy: Are We Digging Deep Enough?

    Kowlgi, Gurukripa N / Attia, Zachi I / Asirvatham, Samuel J

    JACC. Clinical electrophysiology

    2023  Volume 9, Issue 8 Pt 2, Page(s) 1452–1454

    MeSH term(s) Humans ; Deep Learning ; Ventricular Premature Complexes/diagnosis
    Language English
    Publishing date 2023-06-06
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 2846739-5
    ISSN 2405-5018 ; 2405-500X ; 2405-500X
    ISSN (online) 2405-5018 ; 2405-500X
    ISSN 2405-500X
    DOI 10.1016/j.jacep.2023.07.003
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  4. Article ; Online: Current and future implications of the artificial intelligence electrocardiogram: the transformation of healthcare and attendant research opportunities.

    Harmon, David M / Attia, Zachi I / Friedman, Paul A

    Cardiovascular research

    2021  Volume 118, Issue 3, Page(s) e23–e25

    MeSH term(s) Artificial Intelligence ; Delivery of Health Care ; Electrocardiography
    Language English
    Publishing date 2021-04-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 80340-6
    ISSN 1755-3245 ; 0008-6363
    ISSN (online) 1755-3245
    ISSN 0008-6363
    DOI 10.1093/cvr/cvac006
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    Zs.A 565: Show issues Location:
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  5. Article ; Online: Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.

    Lopez-Jimenez, Francisco / Kapa, Suraj / Friedman, Paul A / LeBrasseur, Nathan K / Klavetter, Eric / Mangold, Kathryn E / Attia, Zachi I

    JACC. Clinical electrophysiology

    2024  Volume 10, Issue 4, Page(s) 775–789

    Abstract: Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to ... ...

    Abstract Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.
    MeSH term(s) Humans ; Electrocardiography ; Artificial Intelligence ; Aging/physiology ; Deep Learning ; Aged
    Language English
    Publishing date 2024-04-08
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2846739-5
    ISSN 2405-5018 ; 2405-500X ; 2405-500X
    ISSN (online) 2405-5018 ; 2405-500X
    ISSN 2405-500X
    DOI 10.1016/j.jacep.2024.02.011
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  6. Article ; Online: Diagnosis and treatment of new heart failure with reduced ejection fraction by the artificial intelligence-enhanced electrocardiogram.

    Harmon, David M / Witt, Daniel R / Friedman, Paul A / Attia, Zachi I

    Cardiovascular digital health journal

    2021  Volume 2, Issue 5, Page(s) 282–284

    Language English
    Publishing date 2021-08-24
    Publishing country United States
    Document type Case Reports
    ISSN 2666-6936
    ISSN (online) 2666-6936
    DOI 10.1016/j.cvdhj.2021.08.002
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  7. Article ; Online: Deep neural networks learn by using human-selected electrocardiogram features and novel features.

    Attia, Zachi I / Lerman, Gilad / Friedman, Paul A

    European heart journal. Digital health

    2021  Volume 2, Issue 3, Page(s) 446–455

    Abstract: Aims: We sought to investigate whether artificial intelligence (AI) and specifically deep neural networks (NNs) for electrocardiogram (ECG) signal analysis can be explained using human-selected features. We also sought to quantify such explainability ... ...

    Abstract Aims: We sought to investigate whether artificial intelligence (AI) and specifically deep neural networks (NNs) for electrocardiogram (ECG) signal analysis can be explained using human-selected features. We also sought to quantify such explainability and test if the AI model learns features that are similar to a human expert.
    Methods and results: We used a set of 100 000 ECGs that were annotated by human explainable features. We applied both linear and non-linear models to predict published ECG AI models output for the detection of patients' age and sex. We further used canonical correlation analysis to quantify the amount of shared information between the NN features and human-selected features. We reconstructed single human-selected ECG features from the unexplained NN features using a simple linear model. We noticed a strong correlation between the simple models and the AI output (
    Conclusion: This work shows that NNs for ECG signals extract features in a similar manner to human experts and that they also generate additional novel features that help achieve superior performance.
    Language English
    Publishing date 2021-07-17
    Publishing country England
    Document type Journal Article
    ISSN 2634-3916
    ISSN (online) 2634-3916
    DOI 10.1093/ehjdh/ztab060
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  8. Article ; Online: Artificial Intelligence-Enabled Electrocardiogram in the Detection of Patients at Risk of Atrial Secondary Mitral Regurgitation.

    Naser, Jwan A / Lee, Eunjung / Michelena, Hector I / Lin, Grace / Pellikka, Patricia A / Nkomo, Vuyisile T / Noseworthy, Peter A / Friedman, Paul A / Attia, Zachi I / Pislaru, Sorin V

    Circulation. Arrhythmia and electrophysiology

    2023  Volume 16, Issue 9, Page(s) e012033

    MeSH term(s) Humans ; Mitral Valve Insufficiency/diagnostic imaging ; Mitral Valve Insufficiency/etiology ; Atrial Fibrillation ; Artificial Intelligence ; Heart Atria ; Electrocardiography
    Language English
    Publishing date 2023-08-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2426129-4
    ISSN 1941-3084 ; 1941-3149
    ISSN (online) 1941-3084
    ISSN 1941-3149
    DOI 10.1161/CIRCEP.123.012033
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    Zs.A 6667: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  9. Article ; Online: Saliency maps provide insights into artificial intelligence-based electrocardiography models for detecting hypertrophic cardiomyopathy.

    Siontis, Konstantinos C / Suárez, Abraham Báez / Sehrawat, Ojasav / Ackerman, Michael J / Attia, Zachi I / Friedman, Paul A / Noseworthy, Peter A / Maanja, Maren

    Journal of electrocardiology

    2023  Volume 81, Page(s) 286–291

    Abstract: Introduction: A 12‑lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives ... ...

    Abstract Introduction: A 12‑lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives their performance. We hypothesized that saliency maps could be used to visually identify ECG segments that contribute to a CNN's robust classification of HCM.
    Methods: We derived a new one‑lead (lead I) CNN model based on median beats using the same methodology and cohort used for the original 12‑lead CNN model (3047 patients with HCM, and 63,926 sex- and age-matched non-HCM controls). One‑lead, median-beat saliency maps were generated and visually evaluated in an independent cohort of 100 patients with a diagnosis of HCM and a high artificial intelligence (AI)-ECG-HCM probability score to determine which ECG segments contributed to the model's detection of HCM.
    Results: The one‑lead, median-beat CNN had an AUC of 0.90 (95% CI 0.89-0.92) for HCM detection, similar to the original 12‑lead ECG model. In the independent HCM cohort (n = 100), saliency maps highlighted the ST-T segment in 92 ECGs, the atrial depolarization segment in 12 ECGs, and the QRS complex in 5 ECGs.
    Conclusions: Saliency maps of a one‑lead, median-beat-based CNN model identified perturbations in ventricular repolarization as the main region of interest in detecting HCM.
    MeSH term(s) Humans ; Electrocardiography/methods ; Artificial Intelligence ; Cardiomyopathy, Hypertrophic/diagnosis ; Neural Networks, Computer ; Diagnosis, Computer-Assisted/methods
    Language English
    Publishing date 2023-07-09
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 410286-1
    ISSN 1532-8430 ; 0022-0736
    ISSN (online) 1532-8430
    ISSN 0022-0736
    DOI 10.1016/j.jelectrocard.2023.07.002
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    Zs.A 701: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  10. Article ; Online: Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application?

    Kim, Kyung-Hee / Kwon, Joon-Myung / Pereira, Tara / Attia, Zachi I / Pereira, Naveen L

    Current cardiology reports

    2022  Volume 24, Issue 11, Page(s) 1547–1555

    Abstract: Purpose of review: Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and optimize outcomes in heart failure and specifically cardiomyopathies, offering us novel tools to ... ...

    Abstract Purpose of review: Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and optimize outcomes in heart failure and specifically cardiomyopathies, offering us novel tools to interpret data and make clinical decisions. The aim of this review is to describe the contemporary state of AI and digital health applied to cardiomyopathies as well as to define a potential pivotal role of its application by physicians in clinical practice.
    Recent findings: Many studies have been undertaken in recent years on cardiomyopathy screening especially using AI-enhanced electrocardiography (ECG). Even with mild left ventricular (LV) dysfunction, AI-ECG screening for amyloidosis, hypertrophic cardiomyopathy, or dilated cardiomyopathy is now feasible. Introduction of AI-ECG in routine clinical care has resulted in higher detection of LV systolic dysfunction; however, clinical research on a broader scale with diverse populations is necessary and ongoing. In the area of cardiac-imaging, AI automatically assesses the thickness and characteristics of myocardium to differentiate cardiomyopathies, but research on its prognostic capability has yet to be conducted. AI is also being applied to cardiomyopathy genomics, especially to predict pathogenicity of variants and identify whether these variants are clinically actionable. While the implementation of AI in the diagnosis and treatment of cardiomyopathies is still in its infancy, an ever-growing clinical research strategy will ascertain the clinical utility of these AI tools to help improve diagnosis of and outcomes in cardiomyopathies. We also need to standardize the tools used to monitor the performance of AI-based systems which can then be used to expedite decision-making and rectify any hidden biases. Given its potential important role in clinical practice, healthcare providers need to familiarize themselves with the promise and limitations of this technology.
    MeSH term(s) Humans ; Artificial Intelligence ; Genomics ; Cardiomyopathies/diagnosis
    Language English
    Publishing date 2022-09-01
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2055373-0
    ISSN 1534-3170 ; 1523-3782
    ISSN (online) 1534-3170
    ISSN 1523-3782
    DOI 10.1007/s11886-022-01776-4
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