Deep learning-based automated guide for defining a standard imaging plane for developmental dysplasia of the hip screening using ultrasonography: a retrospective imaging analysis.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Kyung-Sik Ahn, Ji Hye Choi, Heejou Kwon, Seoyeon Lee, Yongwon Cho, Woo Young Jang
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引用次数: 0

Abstract

Background: We aimed to propose a deep-learning neural network model for automatically detecting five landmarks during a two-dimensional (2D) ultrasonography (US) scan to develop a standard plane for developmental dysplasia of the hip (DDH) screening.

Method: A model of global and local networks was developed to detect five landmarks for DDH screening during 2D US. Patients (N = 532) who underwent hip US for DDH screening from January 2016 to December 2021 at a tertiary medical center were enrolled. All datasets were randomly split into training, validation, and test sets in a 70:10:20 ratio for the final assessment of landmark detection. The performance of this model for detecting five landmarks for guiding DDH was analyzed using the root mean square error (RMSE) and dice similarity coefficient.

Results: The RMSE value for the five landmarks for diagnosing and classifying DDH using global and local networks was 4.023 ± 3.723. The point results using EfficientNetB2 were 1.69 ± 1.26 (first point), 3.34 ± 2.37 (second point), 2.54 ± 1.61 (third point), 5.92 ± 4.25 (fourth point), and 6.61 ± 4.82 (fifth point).

Conclusions: Our deep-learning network model is feasible for detecting five landmarks for DDH using ultrasound images. The primary parameters to determine DDH will be significantly detected by applying the deep-learning model in clinical settings.

基于深度学习的自动指南,用于定义超声检查髋关节发育不良的标准成像平面:回顾性成像分析。
背景:我们旨在提出一种深度学习神经网络模型,用于在二维(2D)超声扫描(US)过程中自动检测五个地标,以建立一个标准的髋关节发育不良(DDH)筛查平面。方法:开发了一个全球和局部网络模型,以检测二维美国期间DDH筛查的五个标志。从2016年1月至2021年12月在三级医疗中心接受髋关节超声筛查DDH的患者(N = 532)入组。所有数据集随机分为训练集、验证集和测试集,以70:10:20的比例进行地标检测的最终评估。利用均方根误差(RMSE)和骰子相似系数分析了该模型对引导DDH的5个路标的检测性能。结果:应用全局和局部网络对DDH诊断和分类的5个标志的RMSE值为4.023±3.723。使用EfficientNetB2的积分结果分别为1.69±1.26(第一点)、3.34±2.37(第二点)、2.54±1.61(第三点)、5.92±4.25(第四点)和6.61±4.82(第五点)。结论:我们的深度学习网络模型可用于超声图像检测DDH的5个标志。通过在临床环境中应用深度学习模型,确定DDH的主要参数将得到显著检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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