Researchers and colleagues at Cedars-Sinai have created an artificial intelligence (AI) tool that employs computed tomography (CT) scans to detect patients who are at risk of decreased blood flow to the heart. The tool can correctly predict decreased blood flow within the coronary arteries as well as within the heart muscle. The benefit of this AI tool is that it could possibly be used in real-time when patients come in for a CT scan to help doctors establish the subsequent step in the treatment strategy. Coronary arteries blockages usually happen because of the accumulation of fatty plaques. This may limit blood flow to the heart, resulting in chest pain, heart attacks, or even death.

The number of cardiovascular imaging studies is growing exponentially, and so is the demand to improve the efficacy of the imaging workflow. Over the past decade, studies have demonstrated that machine learning (ML) holds promise to revolutionize cardiovascular research and clinical care. ML may improve several aspects of cardiovascular imaging, such as image acquisition, segmentation, image interpretation, diagnostics, therapy planning, and prognostication. In this review, we discuss the most promising applications of ML in cardiovascular imaging and also highlight the several challenges to its widespread implementation in clinical practice.

Diagnosing emphysema and classifying its severity have long been more art than science. "Everybody has a different trigger threshold for what they would call normal and what they would call disease," said U. Joseph Schoepf, M.D., director of cardiovascular imaging for MUSC Health and assistant dean for clinical research in the Medical University of South Carolina College of Medicine. And until recently, scans of damaged lungs have been a moot point, he said. In the past, if you lost lung tissue, that was it. The lung tissue was gone, and there was very little you could do in terms of therapy to help patients.

Human analysis of cardiac MRI scans is subject to enough noise and bias that a quick machine-learning approach easily matched it for accuracy, researchers found. A single expert reader contouring a scan to get left ventricular (LV) ejection fraction and LV mass had intra-observer error manifest as coefficients of variation of 5.4 and 3.8, respectively, while junior trainees had similar 5.2 and 5.5 coefficients. When a scan was repeated on the same person but at a different time, there was no difference in overall variation when comparing results from an expert, two trainees, and an automated deep-learning neural network, reported Charlotte Manisty, PhD, of University College London and Barts Heart Centre, and colleagues in Circulation: Cardiovascular Imaging. "Given that the greatest sources of measurement error were human factors (i.e., non-modifiable intra- and inter-observer variability), we believe that, with improvement, it is only a matter of time before automated approaches are super-human," according to the investigators. "These data demonstrated that human (intra-observer) error was greater than half of scan-rescan error, an effect that was not minimized by an expert when compared with junior clinicians after appropriate training, despite fifteen years' additional experience," they added.

Artificial intelligence (AI) was by far the hottest trend discussed in sessions and across the expo floor at the world's largest radiology conference, the 2018 Radiological Society Of North America (RSNA). At the meeting in late November, there was an explosion of AI and deep learning algorithms across the expo floor. How machine learning will impact medical imaging was the key takeaway from the opening session, where examples of how AI will alter medical imaging in the near future were highlighted. Here is an overview of the types of AI software being developed and a few examples from RSNA that are specific to cardiovascular imaging. Artificial intelligence has been a growing topic in past years at RSNA, but this year several companies showed products that recently gained U.S. Food and Drug Administration (FDA) market clearance.

According to Rock Health, the digital health funding in the first quarter of 2018 reached a whopping $1.6 billion. This is the highest first quarter on record as it surpassed the much-hyped $1.4 billion funding in venture capital during Q1 2016. AI is being applied in 5 different areas of healthcare to improve its overall operations. It is helping in administration, care provision, clinical decision-making, big data analysis, and remote patient monitoring. Founded by Neal Liu, an MIT graduate, the company started receiving funding in 2016.