Development and Validation of Artificial Intelligence– based Method for Diagnosis of Mitral Regurgitation from Chest Radiographs

Development and Validation of Artificial Intelligence– based Method for Diagnosis of Mitral Regurgitation from Chest Radiographs

Purpose: To develop an artificial intelligence–based model to detect mitral regurgitation on chest radiographs. Materials and Methods: This retrospective study included echocardiographs and associated chest radiographs consecutively collected at a single institution between July 2016 and May 2019. A...

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Bibliographic Details
Main Authors: Abo, K. (Author), Ehara, S. (Author), Iwata, S. (Author), Matsumoto, T. (Author), Miki, Y. (Author), Shimazaki, A. (Author), Ueda, D. (Author), Walston, S.L (Author), Yamamoto, A. (Author), Yoshiyama, M. (Author)
Format: Article
Language:English
Published: Radiological Society of North America Inc. 2022
Subjects:
accuracy
aged
area under the curve
Article
artificial intelligence
Cardiac
cardiovascular parameters
Computer-aided Diagnosis (CAD)
conceptual framework
controlled study
Convolutional Neural Network (CNN)
deep learning
Deep Learning Algorithms
development
diagnostic accuracy
diagnostic test accuracy study
electronic health record
female
Heart
heart function
heart left ventricle ejection fraction
human
human tissue
image analysis
image processing
image reconstruction
imaging
machine learning
Machine Learning Algorithms
major clinical study
male
mitral valve regurgitation
predictive value
receiver operating characteristic
sensitivity and specificity
Supervised Learning
thorax radiography
training
validation process
Valves
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