| Title: |
Development and Application of Deep Learning-Based Model for Quality Control of Children Pelvic X-Ray Images |
| Authors: |
Zhichen LIU, Jincong LIN, Kunjie XIE, Jia SHA, Xu CHEN, Wei LEI, Luyu HUANG, Yabo YAN |
| Source: |
Zhongguo yiliao qixie zazhi, Vol 48, Iss 2, Pp 144-149 (2024) |
| Publisher Information: |
Editorial Office of Chinese Journal of Medical Instrumentation, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Computer applications to medicine. Medical informatics
LCC:Medical technology |
| Subject Terms: |
developmental dysplasia of the hip (ddh), pelvic x-ray images, artificial intelligence, software, Computer applications to medicine. Medical informatics, R858-859.7, Medical technology, R855-855.5 |
| More Details: |
ObjectiveA deep learning-based method for evaluating the quality of pediatric pelvic X-ray images is proposed to construct a diagnostic model and verify its clinical feasibility. Methods Three thousand two hundred and forty-seven children with anteroposteric pelvic radiographs are retrospectively collected and randomly divided into training datasets, validation datasets and test datasets. Artificial intelligence model is conducted to evaluate the reliability of quality control model. Results The diagnostic accuracy, area under ROC curve, sensitivity and specificity of the model are 99.4%, 0.993, 98.6% and 100.0%, respectively. The 95% consistency limit of the pelvic tilt index of the model is −0.052−0.072. The 95% consistency threshold of pelvic rotation index is −0.088−0.055. ConclusionThis is the first attempt to apply AI algorithm to the quality assessment of children's pelvic radiographs, and has significantly improved the diagnosis and treatment status of DDH in children. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
Chinese |
| ISSN: |
1671-7104 |
| Relation: |
https://doaj.org/toc/1671-7104 |
| DOI: |
10.12455/j.issn.1671-7104.240010 |
| Access URL: |
https://doaj.org/article/ad6fb83460f2457f9d263ae1441375e4 |
| Accession Number: |
edsdoj.6fb83460f2457f9d263ae1441375e4 |
| Database: |
Directory of Open Access Journals |
