Artificial intelligence in cardiac radiology
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COMPUTED TOMOGRAPHY
Artificial intelligence in cardiac radiology Marly van Assen1 · Giuseppe Muscogiuri2 · Damiano Caruso3 · Scott J. Lee1 · Andrea Laghi3 · Carlo N. De Cecco1 Received: 25 June 2020 / Accepted: 3 September 2020 © Italian Society of Medical Radiology 2020
Abstract Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization. Keywords Cardiac imaging · Artificial intelligence · Computed tomography · Magnetic resonance imaging
Introduction The concept of artificial intelligence (AI) has first been mentioned in the 1950s [1, 2]. The field of AI has made tremendous progress since then, especially in the last few decades due to technological innovations in computing power and increased availability of data. Along with these technological developments, there has been a significant increase in attention to AI research and development by government, academic, and private sectors resulting in an increase in investment of resources [3–7]. In recent years, the medical community has joined these efforts to develop and implement AI applications for medical-related purposes. Radiology has especially proven to be an excellent field for AI applications, with one of its major focuses being pattern recognition. AI is well on its way to become an integral part of daily clinical practice and has the ability to reduce * Carlo N. De Cecco [email protected] 1
Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., 1365 Clifton Road NE, Suite ‑ AT503, Atlanta, GA 30322, USA
2
Department of Imaging, Centro Cardiologico Monzino, IRCCS, Milan, Italy
3
Dipartimento di Scienze Medico Chirurgiche e Medicina Traslazionale, Universita degli Studi Roma La Sapienza, Rome, Italy
workload and cost while increasing efficiency and improving patient care. Within radiology, and especially cardiac radiology, there has been a steep increase in the volume of radiological imaging exams per day. The clinical acceptance and recommendations for the standardized use of coronary computed tomography angiography (CCTA), calcium scoring, and interest in screening programs [8–14] are expected to further increase the number of cardiac examinations. This increased work
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