Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
BMJ Open. 2024 Feb 24;14(2):e079969. doi: 10.1136/bmjopen-2023-079969.
Radiographic bone age (BA) assessment is widely used to evaluate children's growth disorders and predict their future height. Moreover, children are more sensitive and vulnerable to X-ray radiation exposure than adults. The purpose of this study is to develop a new, safer, radiation-free BA assessment method for children by using three-dimensional ultrasound (3D-US) and artificial intelligence (AI), and to test the diagnostic accuracy and reliability of this method.
This is a prospective, observational study. All participants will be recruited through Paediatric Growth and Development Clinic. All participants will receive left hand 3D-US and X-ray examination at the Shanghai Sixth People's Hospital on the same day, all images will be recorded. These image related data will be collected and randomly divided into training set (80% of all) and test set (20% of all). The training set will be used to establish a cascade network of 3D-US skeletal image segmentation and BA prediction model to achieve end-to-end prediction of image to BA. The test set will be used to evaluate the accuracy of AI BA model of 3D-US. We have developed a new ultrasonic scanning device, which can be proposed to automatic 3D-US scanning of hands. AI algorithms, such as convolutional neural network, will be used to identify and segment the skeletal structures in the hand 3D-US images. We will achieve automatic segmentation of hand skeletal 3D-US images, establish BA prediction model of 3D-US, and test the accuracy of the prediction model.
The Ethics Committee of Shanghai Sixth People's Hospital approved this study. The approval number is 2022-019. A written informed consent will be obtained from their parent or guardian of each participant. Final results will be published in peer-reviewed journals and presented at national and international conferences.
ChiCTR2200057236.
放射影像学骨龄（BA）评估被广泛用于评估儿童生长障碍并预测其未来身高。此外，儿童比成人对 X 射线辐射更为敏感和脆弱。本研究旨在开发一种新的、更安全、无辐射的儿童 BA 评估方法，该方法将使用三维超声（3D-US）和人工智能（AI），并测试该方法的诊断准确性和可靠性。
这是一项前瞻性、观察性研究。所有参与者将通过儿科生长发育诊所招募。所有参与者将在上海第六人民医院同一天接受左手 3D-US 和 X 射线检查，所有图像将被记录。这些图像相关数据将被收集并随机分为训练集（所有数据的 80%）和测试集（所有数据的 20%）。训练集将用于建立 3D-US 骨骼图像分割和 BA 预测模型的级联网络，以实现图像到 BA 的端到端预测。测试集将用于评估 3D-US 的 AI BA 模型的准确性。我们开发了一种新的超声扫描设备，可以提出对手的自动 3D-US 扫描。人工智能算法，如卷积神经网络，将用于识别和分割手部 3D-US 图像中的骨骼结构。我们将实现手部骨骼 3D-US 图像的自动分割，建立 3D-US 的 BA 预测模型，并测试预测模型的准确性。
上海第六人民医院伦理委员会批准了这项研究。批准号为 2022-019。将从每位参与者的父母或监护人处获得书面知情同意书。最终结果将发表在同行评议的期刊上，并在国内和国际会议上展示。
ChiCTR2200057236。
