Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance

L Guo, C Zhou, J Xu, C Huang, Y Yu, G Lu - Journal of Imaging Informatics …, 2024 - Springer
This study aimed to assess the performance of a deep learning algorithm in helping
radiologist achieve improved efficiency and accuracy in chest radiograph diagnosis. We …

Deep learning for chest radiograph diagnosis in the emergency department

EJ Hwang, JG Nam, WH Lim, SJ Park, YS Jeong… - Radiology, 2019 - pubs.rsna.org
Background The performance of a deep learning (DL) algorithm should be validated in
actual clinical situations, before its clinical implementation. Purpose To evaluate the …

Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: a prospective multicenter quality improvement study

A Govindarajan, A Govindarajan, S Tanamala… - Diagnostics, 2022 - mdpi.com
In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However,
the current workload in extensive health care facilities and lack of well-trained radiologists is …

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 …

Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

T Karthik, V Kasiraman, B Paski, K Gurram… - Authorea …, 2023 - techrxiv.org
Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging
tests. However, the complexity of interpretation and global shortage of radiologists have led …

[HTML][HTML] Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United …

AAM AlJasmi, H Ghonim, ME Fahmy, A Nair… - European Journal of …, 2024 - Elsevier
Abstract Background Chest radiographs (CXRs) are widely used to screen for infectious
diseases like tuberculosis and COVID-19 among migrants. At such high-volume settings …

Diagnosis of normal chest radiographs using an autonomous deep-learning algorithm

T Dyer, L Dillard, M Harrison, TN Morgan, R Tappouni… - Clinical radiology, 2021 - Elsevier
Aim To evaluate the suitability of a deep-learning (DL) algorithm for identifying normality as
a rule-out test for fully automated diagnosis in frontal adult chest radiographs (CXR) in an …

Development and performance testing of a deep learning computer-aided diagnosis system for chest X-rays

G Gana, T Sibanda, T Madzorera, M Nhemachena… - 2022 - Eur Respiratory Soc
Background: Medical imaging is essential for the diagnosis of many pathologies such as
pneumonia, pneumothorax and lung cancer. Chest X-rays provide an accessible and cost …

Lesion-aware convolutional neural network for chest radiograph classification

F Li, JX Shi, L Yan, YG Wang, XD Zhang, MS Jiang… - Clinical Radiology, 2021 - Elsevier
AIM To investigate the performance of a deep-learning approach termed lesion-aware
convolutional neural network (LACNN) to identify 14 different thoracic diseases on chest X …

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

P Rajpurkar, J Irvin, RL Ball, K Zhu, B Yang… - PLoS …, 2018 - journals.plos.org
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 …