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Abstract：
BACKGROUND: 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.
METHODS: 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.
BACKGROUND: 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.
BACKGROUND: ChiCTR2200057236.
METHODS: 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.
BACKGROUND: 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.
BACKGROUND: ChiCTR2200057236.
摘要：
背景：放射学骨龄（BA）评估被广泛用于评估儿童的生长障碍和预测他们未来的身高。此外，儿童比成人更敏感，更容易受到X射线辐射的影响。本研究的目的是开发一种新的,更安全,使用三维超声（3D-US）和人工智能（AI）的儿童无辐射BA评估方法，并检验该方法的诊断准确性和可靠性。
方法：这是一个前瞻性的，观察性研究。所有参与者将通过儿科成长和发展诊所招募。所有参与者将于同日在上海市第六人民医院接受左手3D-US和X光检查，将记录所有图像。这些图像相关数据将被收集并随机分为训练集(全部的80%)和测试集(全部的20%)。利用训练集建立3D-US骨架图像分割和BA预测模型的级联网络,实现图像到BA的端到端预测。测试集将用于评估3D-US的AIBA模型的准确性。我们开发了一种新的超声波扫描装置，这可以提出自动3D-US扫描的手。AI算法，比如卷积神经网络，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-USBA预测模型，并检验预测模型的准确性。
背景：上海市第六人民医院伦理委员会批准了本研究。批准号为2022-019。将从每个参与者的父母或监护人处获得书面知情同意书。最终结果将在同行评审的期刊上发表，并在国家和国际会议上发表。
背景：ChiCTR2200057236。
方法：这是一个前瞻性的，观察性研究。所有参与者将通过儿科成长和发展诊所招募。所有参与者将于同日在上海市第六人民医院接受左手3D-US和X光检查，将记录所有图像。这些图像相关数据将被收集并随机分为训练集(全部的80%)和测试集(全部的20%)。利用训练集建立3D-US骨架图像分割和BA预测模型的级联网络,实现图像到BA的端到端预测。测试集将用于评估3D-US的AIBA模型的准确性。我们开发了一种新的超声波扫描装置，这可以提出自动3D-US扫描的手。AI算法，比如卷积神经网络，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-USBA预测模型，并检验预测模型的准确性。
背景：上海市第六人民医院伦理委员会批准了本研究。批准号为2022-019。将从每个参与者的父母或监护人处获得书面知情同意书。最终结果将在同行评审的期刊上发表，并在国家和国际会议上发表。
背景：ChiCTR2200057236。
