The European Society of Cardiology (ESC) hosted the ESC Congress 2020 Digital Experience August 31-September 1, bringing together the subject matter expertise of over 116,000 cardiovascular professionals worldwide. During the conference, speakers examined topics related to cardiovascular disease, presenting the latest insights, and introducing new guidelines.
The use of artificial intelligence in cardiovascular imaging was among the topics covered by conference speakers. Speakers examined where AI can add value in cardiovascular imaging, who will benefit from the application of AI, and what the opportunities are for innovation.
1. Artificial intelligence adds value at nearly every stage in cardiovascular imaging.
AI has become widely used and embraced throughout the field of cardiovascular imaging. Thomas H. Marwick, Director and Chief Executive of the Baker Heart and Diabetes Institute in Melbourne, discussed where machine learning and deep learning are being applied in cardiovascular imaging in the presentation, The Basics of Artificial Intelligence for Imagers. According to Dr. Marwick, AI techniques have been applied in nearly every area of cardiovascular imaging from image acquisition to reporting to quality control. In applying AI in cardiology, scientists are adding value to image acquisition, measurement, reporting, and decision making while lowering costs
2. Cardiovascular AI solutions can make positive impacts on the entire healthcare system.
Applying AI in cardiovascular imaging will impact patients, medical staff, hospitals, commissioners, and the entire healthcare system according to Steffen E. Petersen, Fellow of the Alan Turing Institute and Professor of Cardiovascular Medicine at Queen Mary University of London. Dr. Petersen elaborated on the opportunities for AI to make positive impacts in the industry in the presentation Artificial Intelligence: Diagnosis and Risk Prediction in Imaging.
3. Machine learning and deep learning can be used to enhance diagnosis, improve accuracy, and increase efficiency.
Researchers around the world are applying machine learning and deep learning in cardiovascular imaging to transform diagnosis and treatment. Damini Dey, Director of Quantitative Image Analysis at Cedars Sinai, provided examples of how AI can be applied to enhance diagnosis and improve accuracy during image acquisition, quantitative analysis, and detection in her presentation, Artificial Intelligence: Image Acquisition and Analyses.
Among the applications for AI, Dr. Dey explained that AI can be used in cardiac CT acquisition to recommend patient-specific scan protocols to reduce radiation dosages and contrast. Additionally, she shared that AI can be used for the quantification and characterization of coronary plaque and stenosis. This quantitative measure is important but underutilized. Applying deep learning to CCTA stenosis could help aid decision making by combining features that can predict the risk of ischemia, for example.