Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic-Reference-Cited by-同舟云学术

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Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic

Published:2023 Issue:9 Volume:24 Page:890
ISSN:1229-6929
Container-title:Korean Journal of Radiology
language:en
Short-container-title:Korean J Radiol

Author:

Hong Wonju¹^ORCID,Hwang Eui Jin²^ORCID,Park Chang Min²³⁴^ORCID,Goo Jin Mo²³⁴^ORCID

Affiliation:

1. Department of Radiology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea.

2. Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.

3. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.

4. Cancer Research Institute, Seoul National University, Seoul, Republic of Korea.

Publisher

XMLink

Subject

Radiology, Nuclear Medicine and imaging

Link

https://kjronline.org/pdf/10.3348/kjr.2023.0255

Reference20 articles.

1. Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs

2. Deep Learning for Chest Radiograph Diagnosis in the Emergency Department

3. Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs

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

5. Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs

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