The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review. 2021

Stephanie M Helman, and Elizabeth A Herrup, and Adam B Christopher, and Salah S Al-Zaiti
Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA.

Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.

UI MeSH Term Description Entries
D008279 Magnetic Resonance Imaging Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques. Chemical Shift Imaging,MR Tomography,MRI Scans,MRI, Functional,Magnetic Resonance Image,Magnetic Resonance Imaging, Functional,Magnetization Transfer Contrast Imaging,NMR Imaging,NMR Tomography,Tomography, NMR,Tomography, Proton Spin,fMRI,Functional Magnetic Resonance Imaging,Imaging, Chemical Shift,Proton Spin Tomography,Spin Echo Imaging,Steady-State Free Precession MRI,Tomography, MR,Zeugmatography,Chemical Shift Imagings,Echo Imaging, Spin,Echo Imagings, Spin,Functional MRI,Functional MRIs,Image, Magnetic Resonance,Imaging, Magnetic Resonance,Imaging, NMR,Imaging, Spin Echo,Imagings, Chemical Shift,Imagings, Spin Echo,MRI Scan,MRIs, Functional,Magnetic Resonance Images,Resonance Image, Magnetic,Scan, MRI,Scans, MRI,Shift Imaging, Chemical,Shift Imagings, Chemical,Spin Echo Imagings,Steady State Free Precession MRI
D002648 Child A person 6 to 12 years of age. An individual 2 to 5 years old is CHILD, PRESCHOOL. Children
D006330 Heart Defects, Congenital Developmental abnormalities involving structures of the heart. These defects are present at birth but may be discovered later in life. Congenital Heart Disease,Heart Abnormalities,Abnormality, Heart,Congenital Heart Defect,Congenital Heart Defects,Defects, Congenital Heart,Heart Defect, Congenital,Heart, Malformation Of,Congenital Heart Diseases,Defect, Congenital Heart,Disease, Congenital Heart,Heart Abnormality,Heart Disease, Congenital,Malformation Of Heart,Malformation Of Hearts
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000069550 Machine Learning A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data. Transfer Learning,Learning, Machine,Learning, Transfer
D000465 Algorithms A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. Algorithm
D060388 Support Vector Machine SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples. Support Vector Network,Machine, Support Vector,Machines, Support Vector,Network, Support Vector,Networks, Support Vector,Support Vector Machines,Support Vector Networks,Vector Machine, Support,Vector Machines, Support,Vector Network, Support,Vector Networks, Support

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