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
  • [31] Detection of Lung Lesions in Chest X-ray Images based on Artificial Intelligence
    Wei, Chuan-Yi
    Ou, Chih-Ying
    Chen, I-Yen
    Chang, Hsuan-Ting
    2022 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN, IEEE ICCE-TW 2022, 2022, : 173 - 174
  • [32] Artificial Intelligence-Enabled Smartwatch Used for the Detection of Idiopathic Ventricular Tachycardia: A Case Report
    Kumar, Sumit
    Banerjee, Arijita
    CUREUS JOURNAL OF MEDICAL SCIENCE, 2023, 15 (07)
  • [33] Artificial intelligence-enabled electrocardiogram screens low left ventricular ejection fraction with a degree of confidence
    Lee, Chun-Ho
    Liu, Wei-Ting
    Lou, Yu-Sheng
    Lin, Chin-Sheng
    Fang, Wen-Hui
    Lee, Chia-Cheng
    Ho, Ching-Liang
    Wang, Chih-Hung
    Lin, Chin
    DIGITAL HEALTH, 2022, 8
  • [34] An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced left ventricular function
    Katsushika, Susumu
    Kodera, Satoshi
    Sawano, Shinnosuke
    Shinohara, Hiroki
    Setoguchi, Naoto
    Tanabe, Kengo
    Higashikuni, Yasutomi
    Takeda, Norifumi
    Fujiu, Katsuhito
    Daimon, Masao
    Akazawa, Hiroshi
    Morita, Hiroyuki
    Komuro, Issei
    EUROPEAN HEART JOURNAL - DIGITAL HEALTH, 2023, 4 (03): : 254 - 264
  • [35] Artificial Intelligence-Enabled Model for Early Detection of Left Ventricular Hypertrophy and Mortality Prediction in Young to Middle-Aged Adults
    Liu, Chih-Min
    Hsieh, Ming-En
    Hu, Yu-Feng
    Wei, Tzu-Yin
    Wu, I-Chien
    Chen, Pei-Fen
    Lin, Yenn-Jiang
    Higa, Satoshi
    Yagi, Nobumori
    Chen, Shih-Ann
    Tseng, Vincent S.
    CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES, 2022, 15 (08): : 658 - 668
  • [36] Artificial intelligence for automated left ventricular systolic dysfunction detection using a wearable cardiac patch incorporated with synchronized phonocardiogram and electrocardiogram
    Zhang, W. L.
    Song, B.
    Huang, Q. J.
    Quan, W. W.
    Zhang, R. Y.
    EUROPEAN HEART JOURNAL, 2024, 45
  • [37] Artificial intelligence-enabled electrocardiogram for left ventricular diastolic dysfunction and long-term risk of new-onset atrial fibrillation
    Tsaban, G.
    Lee, E.
    Wopperer, S.
    Abbasi, M.
    Yu, H. T.
    Kane, G.
    Lopez-Jimenez, F.
    Deshmukh, A.
    Asirvatham, S.
    Noseworthy, P.
    Friedman, P.
    Attia, Z., I
    Oh, J.
    EUROPEAN HEART JOURNAL, 2024, 45
  • [38] Artificial Intelligence as an effective assistant for a chest X-Ray interpretation
    Blinov, Dmitrii
    JOURNAL OF ANATOMY, 2020, 236 : 359 - 359
  • [39] VALIDATION OF AN ARTIFICIAL INTELLIGENCE ELECTROCARDIOGRAM BASED ALGORITHM FOR THE DETECTION OF LEFT VENTRICULAR SYSTOLIC DYSFUNCTION IN SUBJECTS WITH CHAGAS DISEASE
    Attia, Zachi Itzhak
    Ribeiro, Antonio
    Friedman, Paul
    Nunes, Maria Carmo
    Gomes, Paulo
    Ferreira, Ariela
    Figueiredo, Bruno
    Sabino, Ester
    Noseworthy, Peter
    Kapa, Suraj
    Perel, Pablo
    Lopez-Jimenez, Francisco
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2021, 77 (18) : 3254 - 3254
  • [40] COVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiers
    Yildirim, Muhammed
    Eroglu, Orkun
    Eroglu, Yesim
    Cinar, Ahmet
    Cengil, Emine
    NEW GENERATION COMPUTING, 2022, 40 (04) : 1077 - 1091