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

Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic err...

Full description

Saved in:

Bibliographic Details
Published in:	PLoS medicine Vol. 15; no. 11; p. e1002686
Main Authors:	Rajpurkar, Pranav, Irvin, Jeremy, Ball, Robyn L., Zhu, Kaylie, Yang, Brandon, Mehta, Hershel, Duan, Tony, Ding, Daisy, Bagul, Aarti, Langlotz, Curtis P., Patel, Bhavik N., Yeom, Kristen W., Shpanskaya, Katie, Blankenberg, Francis G., Seekins, Jayne, Amrhein, Timothy J., Mong, David A., Halabi, Safwan S., Zucker, Evan J., Ng, Andrew Y., Lungren, Matthew P.
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 20.11.2018 Public Library of Science (PLoS)
Subjects:	Algorithms Analysis Artificial neural networks Atelectasis Authorship Chest Chest x-rays Clinical Competence Comparative analysis Computer and Information Sciences Computer science Data mining Datasets Decision making Deep Learning Diabetic retinopathy Diagnosis, Computer-Assisted - methods Effusion Emphysema Hernia Hiatal hernia Humans Learning Lung cancer Lung diseases Lung nodules Machine learning Medical errors Medical imaging Medical imaging equipment Medicine and Health Sciences Methods Network architectures Neural networks Ng, Matthew Nodules Patients People and Places Physical Sciences Pleural effusion Pneumonia Pneumonia - diagnostic imaging Practice Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted - methods Radiography Radiography, Thoracic - methods Radiologists Radiology Reproducibility of Results Research and Analysis Methods Retrospective Studies Software Statistical analysis Supervision Systematic review Thorax Tuberculosis Visualization United States > US California
ISSN:	1549-1676, 1549-1277, 1549-1676
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!