The ACC recently partnered with Booz Allen Hamilton and the National Heart, Lung, and Blood Institute (NHLBI), to put data science to work in the cardiology field in the 2015 Data Science Bowl – a competition that uses data science for social good. This year’s competition challenged data scientists to create an algorithm to automate the process of assessing cardiac function via magnetic resonance imaging (MRI). The National Institutes of Health and Children’s National Medical Center compiled data from more than 1,000 patients for examination – a data set an order of magnitude larger than any previously released data set of its kind. With it came the opportunity for the data science community to take action to transform how clinicians diagnose cardiovascular disease. Prem Soman, MD, FACC, immediate past chair of ACC’s Imaging Section, sat down with Cardiology to explain the impact of the results of the Data Science Bowl. For further information, visit DataScienceBowl.com. What kind of impact would automated, real-time MRI results have on cardiovascular imaging? While MRI is considered to be highly repeatable for left ventricular function assessment, the requirement for manual border tracing does introduce some operator dependence. The ability to automate the process will therefore probably improve precision. This is assuming that the approach to automation and the algorithm used are robust and do not affect precision adversely. If the NHLBI finds the winning algorithms to be effective, do you anticipate that these solutions will be readily adopted into practice? If such an approach is shown to be superior to manual border tracing, it is likely to be accepted in practice, particularly if it results in less operator time as well. How can competitions like the Data Science Bowl help to bridge the gap between proving the effectiveness of crowdsourced algorithms and actually implementing solutions in clinical practice? The power of crowdsourcing is tremendous and has been harnessed effectively by entities such as Wikipedia. Competitions such as the Data Science Bowl could be an effective mechanism to do the same in medicine. What do you see as the future of cardiovascular imaging? Imaging is a critical component of cardiovascular medicine. There are few cardiology patient encounters that are completely devoid of an imaging component. Thus, the value of imaging service is obvious. However, as paradigms of health care delivery change, and value-based utilizations become the norms, it will be imperative for us to demonstrate the value of imaging in patient care more objectively, in terms of cardiovascular outcomes. Do you see artificial intelligence playing a larger role in imaging? Yes, there already are major efforts to harness the strengths of machine learning in clinical imaging applications. Analyses of “big data” have provided unique insights – this trend can only get stronger. Read a longer version with responses from Viktor Ferrari, MD, FACC, chair of ACC's Imaging Section, on ACC.org. Learn more about the Imaging Section at ACC.org/Imaging. Visit the Noninvasive Imaging Clinical Topic Collection on ACC.org for the latest clinical news, patient case quizzes and more. ACC Imaging Section Weighs Screening Options For Diabetic Patients Coronary artery calcium (CAC) screening is the best non-invasive tool for measuring the risk of cardiovascular disease in asymptomatic patients with diabetes, according to a state-of-the-art paper from ACC’s Imaging Section published Feb. 1 in JACC: Cardiovascular Imaging. According to the authors, patients with type-2 diabetes have higher amounts of CAC than nondiabetic patients, but a high proportion of adults with diabetes have a CAC score of 0 or very low. They explain that CAC provides strong risk stratification of these patients, with an increase in mortality for each increase in CAC score category.
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