Abstract
Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from “training data,” that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.
Highlights
Artificial Intelligence (AI) is an academic discipline founded in the early 1950’s and is considered as any method that allows computers to accomplish functions, that require human intelligence
Cardiovascular Magnetic Resonance imaging (CMR) is already an established tool for routine clinical decision-making including diagnosis, follow-up, pre-procedural planning and real-time procedures. It is ideally suited for various Artificial intelligence (AI) techniques due to the digitalisation of the Magnetic Resonance Imaging (MRI) signal and the diversity in the contrast and parametric information that can be obtained from the images
This preliminary investigation showed that the regional distribution patterns of machinedetected, CMR-derived, regional contractile injury could have predictive value with regards to clinical endpoints in Idiopathic Dilated Cardiomyopathy Heart Failure patients
Summary
Artificial Intelligence (AI) is an academic discipline founded in the early 1950’s and is considered as any method that allows computers to accomplish functions, that require human intelligence. A fully convolutional neural network was trained to perform cardiac segmentation from hand-labelled CMR images, computing smooth time-resolved 3D renderings of the cardiac motion Those 3D representations were employed as input data to a supervised denoising autoencoder prediction network, designed to capture robust discriminative features for survival prediction in patients with pulmonary hypertension [33]. A differentiated approach was adopted by MacGregor et al [36] who in addition to incorporating ML-derived measurements in predictive models, proposed a deep-learning based predictive clinical algorithm, advancing previously applied statistical predictive models This preliminary investigation showed that the regional distribution patterns of machinedetected, CMR-derived, regional contractile injury could have predictive value with regards to clinical endpoints in Idiopathic Dilated Cardiomyopathy Heart Failure patients. This may improve workflow efficiency and accuracy by guiding the reviewer to focus mainly on problematic segmentations
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