Home » Projects » Artificial Intelligence in Cardiothoracic Imaging
Artificial Intelligence in Cardiothoracic Imaging

Artificial Intelligence in Cardiothoracic Imaging

A more recent focus for CIRC is to apply machine learning, a form of artificial intelligence, to predict health outcomes and guide care based on routine medical imaging. Recent work has predicted biological age, mortality, outcomes in the emergency department, and incident lung cancer from chest x-ray images and quantified subclinical disease in the retinal microvasculature using fundoscopy images. In collaboration with the Brigham AI in Medicine program, we automated coronary artery calcium quantification on cardiac and chest CT. We are currently running our first clinical trial of an AI tool to use routine chest x-rays to automatically identify patients at high-risk of lung cancer and alert their primary care providers to encourage lung cancer screening CT. 

  1. Weiss J*, Raghu VK*, Bontempi D, Christiani DC, Mak RH, Lu MT, Aerts HJWL. Deep learning to estimate lung disease mortality from chest radiographs. Nature Communications 2023;14(1):2797.
    See publication here
  2. Kolossvary M, Raghu VK, Nagurney JT, Hoffmann U, Lu MT. Deep learning analysis of chest radiographs to triage patients with acute chest pain syndrome. Radiology 2023;306(2):e221926.
    See publication here
  3. Raghu VK, Walia AS, Zinzuwadia AN, Goiffon RJ, Shepard JAO, Aerts HJWL, Lennes IT, Lu MT. Validation of a deep learning-based model to predict lung cancer risk using chest radiographs and electronic medical record data. JAMA Network Open 2022;5(12):e2248793-e2248793.
    See publication here
  4. SM Zekavat*, Raghu VK*, Trinder M, Ye Y, Koyama S, Honigberg MC, Yu Z, Pampana A, Urbut S, Haidermota S, O’Regan DP, Zhao H, Ellinor PT, Segre AV, Elze T, Wiggs JL, Martone J, Adelman RA, Zebardast N, Del Priore L, Wang JC, Natarajan P. Deep learning of the retina enables phenome- and genome- wide analyses of the microvasculature. Circulation 2022;145(2):134-150.
    See publication here
  5. Raghu VK*, Weiss J*, Hoffmann U, Aerts HJWL, Lu MT. Deep learning to estimate biological age from chest radiographs. JACC: Cardiovasc Imaging 2021;14(11):2226-36.
    See publication here
  6. Zeleznik R, Foldyna B, Eslami P, Weiss J, Alexander I, Taron J, Parmar C, Alvi RM, Banerji D, Uno M, Kikuchi Y, Karady J, Zhang L, Scholtz J-E, Mayrhofer T, Lyass A, Mahoney TF, Massaro JM, Vasan RS, Douglas PS, Hoffmann U*, Lu MT*, Aerts HJWL*. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun 2021;12:715.
    See publication here
  7. Lu MT*, Raghu VK*, Mayrhofer T, Aerts HJWL, Hoffmann U. Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model. Ann Intern Med American College of Physicians 2020;173:704–713.
    See publication here
  8. Lu MT, Ivanov A, Mayrhofer T, Hosny A, Aerts HJWL, Hoffmann U. Deep Learning to Assess Long-term Mortality From Chest Radiographs. JAMA Network Open 2019;2:e197416–e197416.
    See publication here