[Development and Application of Deep Learning-Based Model for Quality Control of Children Pelvic X-Ray Images].

Q4 Medicine 中国医疗器械杂志 Pub Date : 2024-03-30 DOI:10.12455/j.issn.1671-7104.240010

Zhichen Liu, Jincong Lin, Kunjie Xie, Jia Sha, Xu Chen, Wei Lei, Luyu Huang, Yabo Yan

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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.

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[基于深度学习的儿童骨盆 X 光图像质量控制模型的开发与应用]。

目的提出一种基于深度学习的小儿骨盆X光图像质量评估方法，构建诊断模型并验证其临床可行性：方法：回顾性收集3247例儿童骨盆前位X线片，随机分为训练数据集、验证数据集和测试数据集。采用人工智能模型评估质量控制模型的可靠性：该模型的诊断准确率、ROC 曲线下面积、灵敏度和特异性分别为 99.4%、0.993、98.6% 和 100.0%。模型中骨盆倾斜指数的 95% 一致性阈值为-0.052-0.072。骨盆旋转指数的 95% 一致性临界值为-0.088-0.055：这是首次尝试将人工智能算法应用于儿童骨盆X光片的质量评估，显著改善了儿童DDH的诊断和治疗状况。

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