Predicting patient demographics from chest radiographs with deep learning
Background Deep learning models are increasingly informing medical decision making, for
instance, in the detection of acute intracranial hemorrhage and pulmonary embolism …
instance, in the detection of acute intracranial hemorrhage and pulmonary embolism …
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 …
Demographic reporting in publicly available chest radiograph data sets: opportunities for mitigating sex and racial disparities in deep learning models
Objective Data sets with demographic imbalances can introduce bias in deep learning
models and potentially amplify existing health disparities. We evaluated the reporting of …
models and potentially amplify existing health disparities. We evaluated the reporting of …
Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
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 …
CheXternal: Generalization of deep learning models for chest X-ray interpretation to photos of chest X-rays and external clinical settings
Recent advances in training deep learning models have demonstrated the potential to
provide accurate chest X-ray interpretation and increase access to radiology expertise …
provide accurate chest X-ray interpretation and increase access to radiology expertise …
Using deep neural networks for predicting age and sex in healthy adult chest radiographs
Background: The performance of chest radiography-based age and sex prediction has not
been well validated. We used a deep learning model to predict the age and sex of healthy …
been well validated. We used a deep learning model to predict the age and sex of healthy …
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 …
Confounding variables can degrade generalization performance of radiological deep learning models
Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease
have been promising, but it has not yet been shown that models trained on x-rays from one …
have been promising, but it has not yet been shown that models trained on x-rays from one …
Identifying disease-free chest x-ray images with deep transfer learning
Chest X-rays (CXRs) are among the most commonly used medical image modalities. They
are mostly used for screening, and an indication of disease typically results in subsequent …
are mostly used for screening, and an indication of disease typically results in subsequent …
Can artificial intelligence reliably report chest x-rays?: Radiologist validation of an algorithm trained on 2.3 million x-rays
P Putha, M Tadepalli, B Reddy, T Raj… - arXiv preprint arXiv …, 2018 - arxiv.org
Background: Chest X-rays are the most commonly performed, cost-effective diagnostic
imaging tests ordered by physicians. A clinically validated AI system that can reliably …
imaging tests ordered by physicians. A clinically validated AI system that can reliably …