The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.
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TL;DR: In this paper, the authors summarized 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.
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Abstract: 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.
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Citations
The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases.
Syed Mohsin,Abubakar Gapizov,Chukwuyem Ekhator,Noor ul Ain,S. Ahmad,Mavra Khan,Chad Barker,Muqaddas Hussain,Jahnavi Malineni,Afif Ramadhan,Raghu Halappa Nagaraj +10 more
TL;DR: This narrative review delves into the potential of artificial intelligence in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease and highlights the significance of collaboration and transparency to address challenges and limitations.
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A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML)
Salah S. Al-Zaiti,Alaa Ali Alghwiri,Xiao Hu,Gilles Clermont,Aaron Peace,Peter W. Macfarlane,Raymond Bond +6 more
- 12 Apr 2022
TL;DR: A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment.
Artificial Intelligence in Pediatric Cardiology: A Scoping Review
Yashendra Sethi,Neil Patel,N. M. Kaka,Ami Desai,Oroshay Kaiwan,Mili Sheth,Rupal Sharma,Helen Huang,Hitesh Chopra,Mayeen Uddin Khandaker,Maha M. A. Lashin,Zuhal Yassin Ali Hamd,Talha Bin Emran +12 more
TL;DR: In this article , the authors conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022 and found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases.
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Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence–Powered Graphical User Interface for Intensive Care Unit Instability Decision Support
Stephanie Helman,Martha Ann Terry,Tiffany Pellathy,Marilyn Hravnak,Elisabeth George,Salah Al-Zaiti,Gilles Clermont +6 more
TL;DR: Gaining input from all clinical users is important to consider when designing AI-derived GUIs, and results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisionalsupport components need to been used as an adjunct to human decision-making.
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Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning
Zengjing Liu,Zhihao Meng,Di Wei,Yuan Qin,Yu Lv,Hong Qiu,Bo Xie,Lanxiang Li,Die Zhang,Boying Liang,Wen Li,Shanfang Qin,Tengyue Yan,Qiuxia Meng,Huilin Wei,Guiyang Jiang,Lingsong Su,Nili Jiang,Kai Zhang,Jia-Jian Lv,Yanling Hu +20 more
TL;DR: The LightGBM model exhibited improved comprehensive performance and thus had higher reliability in assessing the risk predictors of CHD and can potentially facilitate the development of clinical management techniques for PLHIV care in the era of EMRs.
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References
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