Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD.Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley...

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Bibliographic Details
Main Authors: Zahra Hoodbhoy, Uswa Jiwani, Saima Sattar, Rehana Salam, Babar Hasan, Jai K. Das
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Artificial Intelligence
Subjects:
congenital heart disease
machine learning
diagnostic accuracy
meta-analysis
risk of bias
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2021.708365/full

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https://www.frontiersin.org/articles/10.3389/frai.2021.708365/full

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