Adams SJ, Haddad H (2021) Artificial intelligence to diagnose heart failure based on chest X-rays and potential clinical implications. Can J Cardiol 37:1153–1155

Article  PubMed  Google Scholar 

Rich JD, Burns J, Freed BH et al (2018) Meta-analysis global group in chronic (MAGGIC) heart failure risk score: validation of a simple tool for the prediction of morbidity and mortality in heart failure with preserved ejection fraction. J Am Heart Assoc 7:e009594

Article  CAS  PubMed  PubMed Central  Google Scholar 

Aimo A, Castiglione V, Lombardi CM (2020) Effects of vericiguat in heart failure with reduced ejection fraction: do not forget sST2. Letter regarding the article “Baseline features of the VICTORIA (vericiguat global study in subjects with heart failure with reduced ejection fraction) trial.” Eur J Heart Fail 22(10):1934–1935

Article  PubMed  Google Scholar 

Pocock SJ, Ariti CA, McMurray JJ et al (2013) Meta-analysis global group in chronic heart F. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J 34:1404–1413

Article  PubMed  Google Scholar 

Heidenreich PA, Bozkurt B, Aguilar D et al (2022) 2022 ACC/AHA/HFSA guideline for the management of heart failure. J Cardiac Fail 28(5):e1–e167

Article  Google Scholar 

Levy WC, Mozaffarian D, Linker DT et al (2006) The seattle heart failure model: prediction of survival in heart failure. Circulation 113:1424–1433

Article  PubMed  Google Scholar 

Seah JCY, Tang JSN, Kitchen A, Gaillard F, Dixon AF (2019) Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology 290:514–522

Article  PubMed  Google Scholar 

Nam JG, Kang HR, Lee SM et al (2022) Deep learning prediction of survival in patients with chronic obstructive pulmonary disease using chest radiographs. Radiology 305:199–208

Article  PubMed  Google Scholar 

Dong HAN, Jia NING, Feng HUANG et al (2019) Research and application of artificial intelligence in medical imaging. Big Data Res 5:39–67

Google Scholar 

Gensheimer MF, Narasimhan B (2019) A scalable discrete-time survival model for neural networks. PeerJ 7:e6257

Article  PubMed  PubMed Central  Google Scholar 

Deng Y, Liu L, Jiang H et al (2023) Comparison of state-of-the-art neural network survival models with the pooled cohort equations for cardiovascular disease risk prediction. BMC Med Res Methodol 23(1):22

Article  PubMed  PubMed Central  Google Scholar 

Hirata Y, Kusunose K, Tsuji T, Fujimori K, Kotoku J, Sata M (2021) Deep learning for detection of elevated pulmonary artery wedge pressure using standard chest X-ray. Can J Cardiol 37:1198–1206

Article  PubMed  Google Scholar 

Rajpurkar P, Irvin J, Ball RL et al (2018) Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med 15:e1002686

Article  PubMed  PubMed Central  Google Scholar 

Tohyama T, Ide T, Ikeda M et al (2021) Machine learning-based model for predicting 1 year mortality of hospitalized patients with heart failure. ESC Heart Fail 8:4077–4085

Article  PubMed  PubMed Central  Google Scholar 

Kwon JM, Kim KH, Medina-Inojosa J, Jeon KH, Park J, Oh BH (2020) Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography. J Heart Lung Trans 39:805–814

Article  Google Scholar 

Hwang EJ, Park S, Jin KN et al (2019) Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open 2:e191095

Article  PubMed  PubMed Central  Google Scholar 

Lu MT, Ivanov A, Mayrhofer T, Hosny A, Aerts H, Hoffmann U (2019) Deep learning to assess long-term mortality from chest radiographs. JAMA Netw Open 2:e197416

Article  PubMed  PubMed Central  Google Scholar 

Kusunose K, Hirata Y, Tsuji T, Kotoku J, Sata M (2020) Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray. Sci Rep 10:19311

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kim D, Lee JH, Jang MJ et al (2023) The performance of a deep learning-based automatic measurement model for measuring the cardiothoracic ratio on chest radiographs. Bioengineering (Basel). https://doi.org/10.3390/bioengineering10091077

Article  PubMed  PubMed Central  Google Scholar 

Yushkevich PA, Piven J, Hazlett HC et al (2006) (2021) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128

Article  PubMed  Google Scholar 

Youden WJ (1950) Index for rating diagnostic tests. Cancer 3(1):32–35

Article  CAS  PubMed  Google Scholar 

Truszkiewicz K, Poręba R, Gać P (2021) Radiological cardiothoracic ratio in evidence-based medicine. J Clin Med. https://doi.org/10.3390/jcm10092016

Article  PubMed  PubMed Central  Google Scholar 

Truszkiewicz K, Macek P, Poręba M, Poręba R, Gać P (2022) Radiological cardiothoracic ratio as a potential marker of left ventricular hypertrophy assessed by echocardiography. Radiol Res Pract 2022:4931945

PubMed  PubMed Central  Google Scholar 

Zhu Y, Xu H, Zhu X et al (2015) Which can predict left ventricular size and systolic function: cardiothoracic ratio or transverse cardiac diameter. J Xray Sci Technol 23:557–565

PubMed  Google Scholar 

Jiang M, Xu H, Yu D et al (2021) Risk-score model to predict prognosis of malignant airway obstruction after interventional bronchoscopy. Transl Lung Cancer Res 10:3173–3190

Article  PubMed  PubMed Central  Google Scholar 

Majkowska A, Mittal S, Steiner DF et al (2020) Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation. Radiology 294:421–431

Article  PubMed  Google Scholar 

Patel B, Sengupta P (2020) Machine learning for predicting cardiac events: what does the future hold? Expert Rev Cardiovasc Ther 18:77–84

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chao H, Shan H, Homayounieh F et al (2021) Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. Nat Commun 12:2963

Article  CAS  PubMed  PubMed Central  Google Scholar