Artificial Intelligence Helps Coronary CT Angiography and Accelerates the Development of Precision Medicine

This review was jointly published by Prof. Long-Jiang Zhang (Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University) and Prof. Christian Tesche (Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina and Department of Cardiology, Munich University Clinic, Ludwig-Maximilian-University).

With the continuous progress of science and technology, artificial intelligence (AI) has become an important driving force for a new round of scientific and technological revolution and industrial change. It aims to mimic human consciousness and thought processes, continuously learn and improve, and show a response close to or even beyond a real person. At present, machine learning, deep learning, and other AI technologies have been tried to be applied to intelligent diagnostic and decision making, image interpretation, accurate classification, and prognostication of cardiovascular diseases, which has led to broad application prospects and innovation potential.

As a safe and reliable non-invasive technique of choice for clinical screening of coronary heart disease, coronary CT angiography (CCTA) has been recommended by guidelines of the European and American societies as the use of Class 1A evidence for coronary artery disease. In recent years, the attempts of AI to run through the whole process of CCTA have expanded the application scope of CCTA. AI has shown great potential and advantages in many aspects of CCTA, including image quality optimization, fully automated calculation, quantitative and qualitative evaluation, etc.

This review systematically introduces the common AI techniques in the field of cardiovascular computed tomography (CT), summarizes the current research and application progress of AI in cardiovascular CT, and provides its future perspectives. We believe that the digital and intelligent management model of cardiovascular disease is expected to improve the management level and efficiency of diseases and provide patients with more accurate, safe, and appropriate diagnosis and treatment methods.

Contact author name, affiliation, and email address: Long-Jiang Zhang, Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China. kevinzhlj@163.com

Funder: This work was supported by the Key Project of the National Natural Science Foundation of China (82230068).

Conflict of interest: The authors declare that they have no conflict of interest.

See the article: Peng-Peng Xu, Tong-Yuan Liu, Fan Zhou, Qian Chen, Jacob Rowe, Christian Tesche, Long-Jiang Zhang, Artificial intelligence in coronary computed tomography angiography, Medicine Plus, 1(2024): 1,100001. https://doi.org/10.1016/j.medp.2023.100001