Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
A Majkowska, S Mittal, DF Steiner, JJ Reicher… - Radiology, 2020 - pubs.rsna.org
Background Deep learning has the potential to augment the use of chest radiography in
clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty …
clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty …
Deep learning for chest radiograph diagnosis in the emergency department
Background The performance of a deep learning (DL) algorithm should be validated in
actual clinical situations, before its clinical implementation. Purpose To evaluate the …
actual clinical situations, before its clinical implementation. Purpose To evaluate the …
Risk of bias in chest radiography deep learning foundation models
Purpose To analyze a recently published chest radiography foundation model for the
presence of biases that could lead to subgroup performance disparities across biologic sex …
presence of biases that could lead to subgroup performance disparities across biologic sex …
Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study
Background Chest x-rays are widely used in clinical practice; however, interpretation can be
hindered by human error and a lack of experienced thoracic radiologists. Deep learning has …
hindered by human error and a lack of experienced thoracic radiologists. Deep learning has …
Assessment of convolutional neural networks for automated classification of chest radiographs
Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-
performance automated binary classification of chest radiographs. Materials and Methods In …
performance automated binary classification of chest radiographs. Materials and Methods In …
Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Background Chest radiograph interpretation is critical for the detection of thoracic diseases,
including tuberculosis and lung cancer, which affect millions of people worldwide each year …
including tuberculosis and lung cancer, which affect millions of people worldwide each year …
Machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification
Aim To develop a machine learning-based model for the binary classification of chest
radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting …
radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting …
Deep learning in chest radiography: detection of findings and presence of change
R Singh, MK Kalra, C Nitiwarangkul, JA Patti… - PloS one, 2018 - journals.plos.org
Background Deep learning (DL) based solutions have been proposed for interpretation of
several imaging modalities including radiography, CT, and MR. For chest radiographs, DL …
several imaging modalities including radiography, CT, and MR. For chest radiographs, DL …
Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional …
JH Kim, SG Han, A Cho, HJ Shin, SE Baek - BMC Medical Informatics and …, 2021 - Springer
Background Interpretation of chest radiographs (CRs) by emergency department (ED)
physicians is inferior to that by radiologists. Recent studies have investigated the effect of …
physicians is inferior to that by radiologists. Recent studies have investigated the effect of …
Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs
Objectives Convolutional neural networks (CNNs) are a subtype of artificial neural network
that have shown strong performance in computer vision tasks including image classification …
that have shown strong performance in computer vision tasks including image classification …