The machine learning horizon in cardiac hybrid imaging

Abstract

Background: Machine learning (ML) represents a family of algorithms that has rapidly developed within the last years in a wide variety of knowledge areas. ML is able to elucidate and grasp complex patterns from data in order to approach prediction and classification problems. The present narrative review summarizes fundamental notions in ML as well as the evidence of its application in standard cardiac imaging and the potential for implementation in cardiac hybrid imaging. Results: ML, and in particular Deep Learning, has begun to revolutionize medical imaging though the optimization of diagnostic and prognostic estimations at the individual-patient level. On the other hand, the spread and availability of high quality non-invasive imaging has provided growing amounts of data in the characterization of suspected cardiovascular diseases. At the same time, modern combined imaging equipment has set the ground for the concept of hybrid imaging to develop. Cardiac hybrid imaging refers to the combination of diagnostic images and offers the possibility to comprehensively characterize the heart and great vessels when a pathology is suspected or clinically known. Analysis and integration of large amounts of cardiac hybrid imaging data (and corresponding clinical profiles) constitutes a highly complex process and ML will likely be able to enhance it in the near future. Conclusion: ML conveys novel and powerful approaches in the processing of large and complex datasets that may include images as well as imaging-derived data. Given the growing amount of data in the realm of cardiac hybrid imaging and the rapid development of ML, it is highly desirable to implement and test ML in the optimization of our multimodality imaging diagnostic and prognostic evaluations in cardiovascular disease.

On stethoscopes, patient records, artificial intelligence, and zettabytes: A glimpse into the future of digital medicine in Mexico

“…These algorithms are characterized by their capacity to explore, identify, and use non-linear complex dependences across multiple rounds of data exposure to optimize tasks such as image classification and effects' prediction. These advances result from the historical convergence of three necessary factors: capacity to store large amounts of data, new parallel processors, and the existence of intrinsically complex problems in medicine 6 . This means that, only until this decade, the analytic capacity of systems has turned them into extremely useful tools that in some time will be able to operate in medical settings where repetition can be avoided while human supervision can be maintained, which maximizes the benefit and personalization for patients 7 .…”

Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review

“…Table 7 shows the representation of the work done in the area of medical big data in heart related issues since January 2008 to August 2018. The details of the papers [13], [14], [22], [23], [26]- [28], [32], [35], [36], [40], [42], [45]- [47], [50], [55], [57], [58], [60], [62], [63], [68], [69], [72]- [74], [84], [92], [100], [109], [110], [114], [119], [120], [122], [125], [126], [129], [131], [138], [145], [155], [179], [181], [185] are already given in table 6, while the rest of the papers are discussed in table 7.…”

Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology

“…Every day, a large number of researchers try to push the boundaries of AI, ML, and Deep Learning with constant incremental improvements. Another upcoming domain will be that of hybrid imaging in which parallel or sequential acquisitions of PET or SPECT and CT or MR will surely be integrated when ML can account for the structural relation between the techniques' results (for further considerations on this topic, a review by Juarez-Orozco et al [1] can be helpful). From our point of view, we can only expect improvements in all domains of nuclear cardiology in the coming years.…”

“…Artificial intelligence (AI) has revolutionized the way in which we interact with the massive stream of data generated by the current process automation, connectivity, and data storage capabilities. The explosion in the implementation of AI can be traced, on the one side, to the utilization of novel machine learning (ML) algorithms (methods that can iteratively elucidate complex non-linear and high-dimensional patterns in order to optimize classification and prediction tasks) such as convolutional neural networks (i.e., Deep Learning, see ahead), and on the other side, to the recent convergence of very large high-quality datasets, massive computational power, and the existence of a range of complex problems in all areas of knowledge [1].…”