The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review
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 ent...
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| Veröffentlicht in: | Cardiology in the young 2021-11, Vol.31 (11), p.1770-1780 |
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| creator | Helman, Stephanie M. Herrup, Elizabeth A. Christopher, Adam B. Al-Zaiti, Salah S. |
| description | 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. |
| doi_str_mv | 10.1017/S1047951121004212 |
| format | Article |
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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. 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| subjects | Accuracy Algorithms Artificial intelligence Auscultation Cardiology Child Classification Congenital diseases Decision making Deep learning Diagnosis Discriminant analysis Echocardiography General Cardiology Heart Heart Defects, Congenital - diagnostic imaging Humans Learning algorithms Machine Learning Magnetic Resonance Imaging Mortality Neural networks Optimization Pediatrics Review Reviews Support Vector Machine Support vector machines Therapeutic applications |
| title | The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review |
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