Artificial intelligence in cardiac computed tomography.

Journal: Progress in cardiovascular diseases
Published Date:

Abstract

Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.

Authors

  • Afolasayo A Aromiwura
    Department of Medicine, University of Louisville, Louisville, KY, USA.
  • Tyler Settle
    Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA.
  • Muhammad Umer
    Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
  • Jonathan Joshi
    Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
  • Matthew Shotwell
    Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA.
  • Jishanth Mattumpuram
    Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA.
  • Mounica Vorla
    Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA.
  • Maryta Sztukowska
    Clinical Trials Unit, University of Louisville, Louisville, KY, USA; University of Information Technology and Management, Rzeszow, Poland.
  • Sohail Contractor
    Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
  • Amir Amini
  • Dinesh K Kalra
    Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA. Electronic address: dinesh.kalra@louisville.edu.