Deep Learning Systems for Pneumothorax Detection on Chest Radiographs: A Multicenter External Validation Study-Reference-Cited by-同舟云学术

Deep Learning Systems for Pneumothorax Detection on Chest Radiographs: A Multicenter External Validation Study

Published:2021-07-01 Issue:4 Volume:3 Page:e200190
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Thian Yee Liang^ORCID,Ng Dianwen,Hallinan James Thomas Patrick Decourcy,Jagmohan Pooja^ORCID,Sia Soon Yiew^ORCID,Tan Cher Heng,Ting Yong Han^ORCID,Kei Pin Lin^ORCID,Pulickal Geoiphy George^ORCID,Tiong Vincent Tze Yang^ORCID,Quek Swee Tian^ORCID,Feng Mengling^ORCID

Funder

National University Health System (NUHS) internal grant funding under the NUHS Seed Fund

National Medical Research Council Health Services Research Grant

Publisher

Radiological Society of North America (RSNA)

Subject

Artificial Intelligence,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.2021200190

Reference23 articles.

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

2. Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study

3. Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation

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