Published November 2021
| Version v1
Journal article
Development of retake support system for lateral knee radiographs by using deep convolutional neural network
- 1. MedCity21, Division of Premier Preventive Medicine, Osaka City University Hospital, Abeno Harukasu 21F, Abenosuji 1-1-43, Abeno-ku Osaka, Osaka 545-8545 (Japan)
- 2. Department of Radiology, Osaka University Hospital, Yamadaoka 2-15, Suita, Osaka 565-0871 (Japan)
- 3. Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Yamadaoka 1-7, Suita, Osaka 565-0871 (Japan)
Description
Lateral radiography of the knee joint is frequently performed; however, the retake rate is high owing to positioning errors. Therefore, in this study, to reduce the required number and time of image retakes, we developed a system that can classify the tilting directions of lateral knee radiographs and evaluated the accuracy of the proposed method.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.radi.2021.05.002Additional details
Identifiers
- DOI
- 10.1016/j.radi.2021.05.002;
- PII
- S1078817421000559;
Publishing Information
- Journal Title
- Radiography (London 1995)
- Journal Volume
- 27
- Journal Issue
- 4
- Journal Page Range
- p. 1110-1117
- ISSN
- 1078-8174
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54003175
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- ACCURACY; BONE JOINTS; ERRORS; IMAGES; MACHINE LEARNING; NEURAL NETWORKS
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; BODY; LEARNING; MATHEMATICAL LOGIC; ORGANS; SKELETON
Optional Information
- Copyright
- Copyright (c) 2021 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.