Detection of Left Ventricular Systolic Dysfunction Using an Artificial Intelligence-Enabled Chest X-Ray

被引:8
|
作者
Hsiang, Chih-Weim [1 ]
Lin, Chin [2 ,3 ,4 ]
Liu, Wen-Cheng [5 ]
Lin, Chin-Sheng [5 ,6 ]
Chang, Wei-Chou [1 ,6 ]
Hsu, Hsian-He [1 ]
Huang, Guo-Shu [1 ]
Lou, Yu-Sheng [2 ,3 ]
Lee, Chia-Cheng [7 ]
Wang, Chih-Hung [6 ,8 ]
Fang, Wen-Hui [9 ]
机构
[1] Triserv Gen Hosp, Natl Def Med Ctr, Dept Radiol, Taipei, Taiwan
[2] Natl Def Med Ctr, Grad Inst Life Sci, Taipei, Taiwan
[3] Natl Def Med Ctr, Sch Publ Hlth, Taipei, Taiwan
[4] Natl Def Med Ctr, Med Technol Educ Ctr, Sch Med, Taipei, Taiwan
[5] Triserv Gen Hosp, Natl Def Med Ctr, Dept Internal Med, Div Cardiol, Taipei, Taiwan
[6] Natl Def Med Ctr, Grad Inst Med Sci, Taipei, Taiwan
[7] Triserv Gen Hosp, Natl Def Med Ctr, Dept Med Informat, Taipei, Taiwan
[8] Triserv Gen Hosp, Natl Def Med Ctr, Dept Otolaryngol Head & Neck Surg, Taipei, Taiwan
[9] Triserv Gen Hosp, Natl Def Med Ctr, Dept Family & Community Med, Taipei, Taiwan
关键词
HEART-FAILURE; RISK; PROGNOSIS; COMMUNITY;
D O I
10.1016/j.cjca.2021.12.019
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Assessment of left ventricular systolic dysfunction provides essential information related to the prognosis and management of cardiovascular diseases. The aim of this study was to develop a deep-learning model to identify left ventricular ejection fraction (LVEF) <= 35% via chest X-ray (CXR [CXR-EF <= 35%]) features and investigate the performance and clinical implications. Methods: This study collected 90,547 CXRs with the corresponding LVEF according to transthoracic echocardiography from the outpatient department in an academic medical center. Among these, 77,227 CXRs were used to develop the identification of CXR-EF <= 35%. Another 13,320 CXRs were used to validate the performance, which was evaluated by area under the receiver operating characteristic curve (AUC). Furthermore, CXR-EF <= 35% was tested to assess the long-term risks of developing LVEF <= 35% and cardiovascular outcomes, which were evaluated by Kaplan-Meier survival analysis and the Cox proportional hazards model. Results: The AUCs of CXR-EF <= 35% for the detection of LVEF <= 35% were 0.888 and 0.867 in the internal and external validation cohorts, respectively. Patients with baseline LVEF > 50% but detected as CXREF <= 35% were at higher risk of long-term development of LVEF <= 35% (hazard ratio, internal validation cohort [HRi] 3.91, 95% CI 2.98-5.14; hazard ratio, external validation cohort [HRe] 2.49, 95% CI 1.89-3.27). Furthermore, patients detected as LVEF <= 35% by CXR-EF <= 35% had significantly higher future risks of all-cause mortality (HRi 1.40, 95% CI 1.15-1.71; HRe 1.38, 95% CI 1.15-1.66), cardiovascular mortality (HRi 3.02, 95% CI 1.84-4.98; HRe 2.60, 95% CI 1.77-3.82), and new-onset atrial fibrillation (HRi 2.81, 95% CI 2.15-3.66; HRe 2.93, 95% CI 2.34-3.67) compared with those detected as no LVEF <= 35%. Conclusions: CXR-EF <= 35% may serve as a screening tool for early detection of LVEF <= 35% and could independently contribute to predictions of long-term development of LVEF <= 35% and cardiovascular outcomes. Further prospective studies are needed to confirm the model performance.
引用
收藏
页码:763 / 773
页数:11
相关论文
共 50 条
  • [1] Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence-Enabled ECGs
    Anjewierden, Scott
    O'Sullivan, Donnchadh
    Mangold, Kathryn E.
    Greason, Grace
    Attia, Itzhak Zachi
    Lopez-Jimenez, Francisco
    Friedman, Paul A.
    Asirvatham, Samuel J.
    Anderson, Jason
    Eidem, Benjamin W.
    Johnson, Jonathan N.
    Prakash, Shisheer Havangi
    Niaz, Talha
    Madhavan, Malini
    JOURNAL OF THE AMERICAN HEART ASSOCIATION, 2024, 13 (21):
  • [2] Detection of Systolic Dysfunction in Pediatric Patients Using an Artificial Intelligence-Enabled Electrocardiogram
    Anjewierden, Scott
    O'Sullivan, Donnchadh
    Greason, Grace
    Attia, Zachi
    Lopez-Jimenez, Francisco
    Friedman, Paul
    Noseworthy, Peter A.
    Anderson, Jason
    Kashou, Anthony H.
    CIRCULATION, 2023, 148
  • [3] Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction
    Huang, Yu-Chang
    Hsu, Yu-Chun
    Liu, Zhi-Yong
    Lin, Ching-Heng
    Tsai, Richard
    Chen, Jung-Sheng
    Chang, Po-Cheng
    Liu, Hao-Tien
    Lee, Wen-Chen
    Wo, Hung-Ta
    Chou, Chung-Chuan
    Wang, Chun-Chieh
    Wen, Ming-Shien
    Kuo, Chang-Fu
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2023, 10
  • [4] Electrocardiograph and Chest X-ray in Prediction of Left Ventricular Systolic Dysfunction
    Basnet, B. K.
    Manandhar, K.
    Shrestha, R.
    Shrestha, S.
    Thapa, M.
    JOURNAL OF NEPAL MEDICAL ASSOCIATION, 2009, 48 (04) : 310 - 313
  • [5] Artificial Intelligence Detection of Left Ventricular Systolic Dysfunction Using Chest X-Rays: Prospective Validation, Please
    Lauzier, Pascal Theriault
    Chow, Benjamin J. W.
    CANADIAN JOURNAL OF CARDIOLOGY, 2022, 38 (06) : 720 - 722
  • [6] Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk
    Tsai, Dung-Jang
    Lin, Chin
    Lin, Chin-Sheng
    Lee, Chia-Cheng
    Wang, Chih-Hung
    Fang, Wen-Hui
    JOURNAL OF MEDICAL SYSTEMS, 2024, 48 (01)
  • [7] Challenges to implementing artificial intelligence-enabled Chest X-ray in opportunistic screening for osteoporosis
    Ye, Hongnan
    JOURNAL OF BONE AND MINERAL METABOLISM, 2025, : 182 - 183
  • [8] TESTING THE REAL-WORLD UTILITY OF BAYES' THEOREM WHEN USING AN ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAM ALGORITHM FOR DETECTION OF LEFT VENTRICULAR SYSTOLIC DYSFUNCTION
    Medina-Inojosa, Betsy J.
    Harmon, David
    Medina-Inojosa, Jose
    Carter, Rickey E.
    Attia, Zachi Itzhak
    Friedman, Paul A.
    Lopez-Jimenez, Francisco
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2023, 81 (08) : 2329 - 2329
  • [9] Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea
    Adedinsewo, Demilade
    Carter, Rickey E.
    Attia, Zachi
    Johnson, Patrick
    Kashou, Anthony H.
    Dugan, Jennifer L.
    Albus, Michael
    Sheele, Johnathan M.
    Bellolio, Fernanda
    Friedman, Paul A.
    Lopez-Jimenez, Francisco
    Noseworthy, Peter A.
    CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY, 2020, 13 (08): : E008437
  • [10] 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