Retraining an Artificial Intelligence Algorithm to Calculate Left Ventricular Ejection Fraction in Pediatrics

Zuercher, Mael; Ufkes, Steven; Erdman, Lauren; Slorach, Cameron; Mertens, Luc; Taylor, Katherine

doi:10.1053/j.jvca.2022.05.004

Cited by 5 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI has the potential to improve the validity of auscultatory findings for diagnosing CHD [ 87 , 88 , 89 ]. The limitations of objective performance data have restricted wide acceptability so far [ 90 , 91 , 92 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi

Patel²,

Kaka³

et al. 2022

JCM

View full text Add to dashboard Cite

The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002–2022. We 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. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the ‘human touch’ limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.

show abstract

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi

Patel²,

Kaka³

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…Deep learning lets the data train the computer leading to predictive models that become stronger as more data are added. Imaging data sets that are being used in paediatrics to predict abnormalities have been frequently reported in the literature in the last few years with examples including (but not limited to) the detection of abnormalities in chest radiographs (Chen et al, 2020 ; Padash et al, 2022 ), the diagnosis of effusions in elbow joints (Huhtanen et al, 2022 ), the detection of intracranial pathology on CT imaging, defining abnormalities as critical or non-critical (Titano et al, 2018 ), the assessment of left ventricular function from birth to 18 years and the diagnosis of coronary artery lesions in Kawasaki disease using echocardiography (Lee et al, 2022 ; Zuercher et al, 2022 ) and the assessment of paediatric brain tumours (Grist et al, 2021 ; Huang et al, 2022 ). However, challenges remain in achieving an acceptable diagnostic accuracy and the acquisition of adequate training data sets in cohorts, particularly in relation to rare diseases due to the size of the patient populations.…”

Section: A Digital Approach To Precision Diagnostics and Medicine In ...mentioning

confidence: 99%

Precision diagnostics in children

Dimitri¹

2023

Camb. prisms Precis. med.

View full text Add to dashboard Cite

Medical practice is transforming from a reactive to a pro-active and preventive discipline that is underpinned by precision medicine. The advances in technologies in such fields as genomics, proteomics, metabolomics, transcriptomics and artificial intelligence have resulted in a paradigm shift in our understanding of specific diseases in childhood, greatly enhanced by our ability to combine data from changes within cells to the impact of environmental and population changes. Diseases in children have been reclassified as we understand more about their genomic origin and their evolution. Genomic discoveries, additional 'Omics' data and advances such as optical genome mapping have driven rapid improvements in the precision and speed of diagnoses of diseases in children, and are now being incorporated into newborn screening, have improved targeted therapies in childhood, and have supported the development of predictive biomarkers to assess therapeutic impact and determine prognosis in congenital and acquired diseases of childhood.New medical device technologies are facilitating data capture at a population level to support higher diagnostic accuracy and tailored therapies in children according to predicted population outcome, and digital ecosystems now tailor therapies and provide support for their specific needs. By capturing biological and environmental data as early as possible in childhood, we can understand factors that predict disease or maintain health, and track changes across a more extensive longitudinal path. Data from multiple health and external sources over long time periods starting from birth or even in the in-utero environment will provide further clarity about how to sustain health and prevent or predict disease. In this respect, we will not only use data to diagnose disease, but precision diagnostics will aid the 'diagnosis of good health'. The principle of 'start early and gain more' will thus underpin the value of applying a personalised medicine approach early in life.

show abstract