[Development and Application of Deep Learning-Based Model for Quality Control of Children Pelvic X-Ray Images].
Zhongguo Yi Liao Qi Xie Za Zhi
; 48(2): 144-149, 2024 Mar 30.
Article
in Zh
| MEDLINE
| ID: mdl-38605612
ABSTRACT
Objective:
A 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.Conclusion:
This 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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Artificial Intelligence
/
Deep Learning
Limits:
Child
/
Humans
Language:
Zh
Journal:
Zhongguo Yi Liao Qi Xie Za Zhi
Year:
2024
Document type:
Article