Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction

被引:0
|
作者
Chin Lin
Feng-Chih Kuo
Tom Chau
Jui-Hu Shih
Chin-Sheng Lin
Chien-Chou Chen
Chia-Cheng Lee
Shih-Hua Lin
机构
[1] National Defense Medical Center,School of Medicine
[2] National Defense Medical Center,Graduate Institute of Aerospace and Undersea Medicine
[3] National Defense Medical Center,Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri
[4] Providence St. Vincent Medical Center,Service General Hospital
[5] Tri-Service General Hospital,Department of Medicine
[6] National Defense Medical Center,Department of Pharmacy Practice
[7] National Defense Medical Center,School of Pharmacy
[8] National Defense Medical Center,Division of Cardiology, Department of Internal Medicine, Tri
[9] National Defense Medical Center,Service General Hospital
[10] National Defense Medical Center,Division of Nephrology, Department of Medicine, Tri
来源
关键词
D O I
10.1038/s43856-024-00472-4
中图分类号
学科分类号
摘要
Hyperthyroidism occurs when the thyroid gland produces too much hormone and can cause various symptoms including faster heartbeat, weight loss, and nervousness. Diagnosis is often missed, which can lead to heart problems and even death. Measurements of the heart’s electrical activity can be obtained using Electrocardiograms (ECGs). We made a computational model that can detect hyperthyroidism from ECGs. Our model was better able to identify people with hyperthyroidism than currently available methods, especially the more severe forms of the condition. If future work demonstrates our model is safe and accurate, it could potentially be used to detect hyperthyroidism sooner, enabling faster treatment and improved health of people with hyperthyroidism.
引用
收藏
相关论文
共 50 条
  • [1] Prediction of certainty in artificial intelligence-enabled electrocardiography
    Demolder, Anthony
    Nauwynck, Maxime
    De Pauw, Michel
    De Buyzere, Marc
    Duytschaever, Mattias
    Timmermans, Frank
    De Pooter, Jan
    JOURNAL OF ELECTROCARDIOLOGY, 2024, 83 : 71 - 79
  • [2] Detection of Left Atrial Myopathy Using Artificial Intelligence-Enabled Electrocardiography
    Verbrugge, Frederik H.
    Reddy, Yogesh N. V.
    Attia, Zachi I.
    Friedman, Paul A.
    Noseworthy, Peter A.
    Lopez-Jimenez, Francisco
    Kapa, Suraj
    Borlaug, Barry A.
    CIRCULATION-HEART FAILURE, 2022, 15 (01) : E008176
  • [3] DETECTION OF HYPERTROPHIC CARDIOMYOPATHY BY ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY IN CHILDREN AND ADOLESCENTS
    Siontis, Konstantinos
    Liu, Kan
    Bos, J. Martijn
    Attia, Zachi Itzhak
    Arruda-Olson, Adelaide
    Farahani, Nasibeh Z.
    Friedman, Paul
    Noseworthy, Peter
    Ackerman, Michael
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2021, 77 (18) : 3247 - 3247
  • [4] PERFORMANCE OF ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY IN THE PREDICTION OF NONALCOHOLIC FATTY LIVER DISEASE
    Udompap, Prowpanga
    Liu, Kan
    Attia, Zachi I.
    Canning, Rachel
    Benson, Joanne T.
    Therneau, Terry M.
    Noseworthy, Peter A.
    Friedman, Paul A.
    Rattan, Puru
    Ahn, Joseph C.
    Simonetto, Douglas A.
    Shah, Vijay
    Kamath, Patrick S.
    Allen, Alina M.
    HEPATOLOGY, 2022, 76 : S617 - S618
  • [5] Artificial Intelligence-Enabled Electrocardiography to Screen Patients with Dilated Cardiomyopathy
    Shrivastava, Sanskriti
    Cohen-Shelly, Michal
    Attia, Zachi I.
    Rosenbaum, Andrew N.
    Wang, Liwei
    Giudicessi, John R.
    Redfield, Margaret
    Bailey, Kent
    Lopez-Jimenez, Francisco
    Lin, Grace
    Kapa, Suraj
    Friedman, Paul A.
    Pereira, Naveen L.
    AMERICAN JOURNAL OF CARDIOLOGY, 2021, 155 : 121 - 127
  • [6] APPLICATION OF ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY IN FAMILIAL DILATED CARDIOMYOPATHY
    Shrivastava, Sanskriti
    Shelly, Michal
    Attia, Zachi Itzhak
    Rosenbaum, Andrew
    Lopez-Jimenez, Francisco
    Bailey, Kent
    Kapa, Suraj
    Friedman, Paul
    Pereira, Naveen
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2020, 75 (11) : 3471 - 3471
  • [7] Predictive Value of Artificial Intelligence-Enabled Electrocardiography in Patients with Takotsubo Cardiomyopathy
    Kanaji, Yoshihisa
    Ozcan, Iike
    Tryon, David N.
    Ahmad, Ali
    Sara, Jaskanwal D.
    Kakuta, Tsunekazu
    Lerman, Lilach O.
    Attia, Zachi
    Lerman, Amir
    CIRCULATION, 2022, 146
  • [8] Predictive Value of Artificial Intelligence-Enabled Electrocardiography in Patients With Takotsubo Cardiomyopathy
    Kanaji, Yoshihisa
    Ozcan, Ilke
    Tryon, David N.
    Ahmad, Ali
    Sara, Jaskanwal Deep Singh
    Lewis, Brad
    Friedman, Paul
    Noseworthy, Peter A.
    Lerman, Lilach O.
    Kakuta, Tsunekazu
    Attia, Zachi I.
    Lerman, Amir
    JOURNAL OF THE AMERICAN HEART ASSOCIATION, 2024, 13 (05):
  • [9] PREDICTIVE VALUE OF ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY IN PATIENTS WITH ACUTE CORONARY SYNDROME
    Kanaji, Yoshihisa
    Ozcan, Ilke
    Tryon, David
    Ahmad, Ali
    Sara, Jaskanwal Deep Singh
    Lerman, Lilach O.
    Kakuta, Tsunekazu
    Attia, Zachi Itzhak
    Lerman, Amir
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2023, 81 (08) : 1336 - 1336
  • [10] Artificial intelligence-enabled electrocardiography identifies severe dyscalcemias and has prognostic value
    Lin, Chin
    Chen, Chien-Chou
    Chau, Tom
    Lin, Chin-Sheng
    Tsai, Shi-Hung
    Lee, Ding-Jie
    Lee, Chia-Cheng
    Shang, Hung-Sheng
    Lin, Shih-Hua
    CLINICA CHIMICA ACTA, 2022, 536 : 126 - 134