Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs
By Eui Jin Hwang, Sunggyun Park, K. Jin, Jung Im Kim, Jong Hyuk Lee, J. Goo, Jaehong Aum, J. Yim, Julien G. Cohen, Gilbert Ferretti and 1 more. JAMA Network Open, 2019
379 citations recorded by the source corpus.
Key Points Question Can a deep learning–based algorithm accurately discriminate abnormal chest radiograph results showing major thoracic diseases from normal chest radiograph results? Findings In this diagnostic study of 54 221 chest radiographs with normal findings and 35 613 with abnormal findings, the deep learning–based algorithm for discrimination of chest radiographs with pulmonary malignant neoplasms, active tuberculosis, pneumonia, or pneumothorax demonstrated excellent and consistent performance throughout 5 independent data sets. The algorithm outperformed physicians, including radiologists, and enhanced physician performance when used as a second reader. Meaning A deep learning–based algorithm may help improve diagnostic accuracy in reading chest radiographs and assist in prioritizing chest radiographs, thereby increasing workflow efficacy.
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/db38db478253062d2089248a9711066b0d095bcc
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In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021, background): “Many examples have gained traction recently with large volumes of work on automated diagnosis, prognosis, drug design, and testing [13–17].”
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- Preparing Medical Imaging Data for Machine Learning (2020, methodology): “More often, application of the imaging diagnosis based on expert interpretation or a consensus of experts based on a reinterpretation of the images, or free-text report is used (10).”