# A Cascade Model with Prior Knowledge for Bone Age Assessment
###### Abstract  
Bone age is commonly used to reflect growth and development trends in children, predict adult heights, and diagnose endocrine disorders. Nevertheless, the existing automated bone age assessment (BAA) models do not consider the nonlinearity and continuity of hand bone development simultaneously. In addition, most existing BAA models are based on datasets from European and American children and may not be applicable to the developmental characteristics of Chinese children. Thus, this work proposes a cascade model that fuses prior knowledge. Specifically, a novel bone age representation is defined, which incorporates nonlinear and continuous features of skeletal development and is implemented by a cascade model. Moreover, corresponding regions of interest (Rols) based on RUS-CHN were extracted by YOLO v5 as prior knowledge inputs to the model. In addition, based on MobileNet v2, an improved feature extractor was proposed by introducing the Convolutional Block Attention Module and increasing the receptive field to improve the accuracy of the evaluation. The experimental results show that the mean absolute error (MAE) is 4.44 months and significant correlations with the reference bone age is (\(r=0.994\), \(p<0.01\)); accuracy is 94.04% for ground truth within \(\pm 1\) year. Overall, the model design adequately considers hand bone development features and has high accuracy and consistency, and it also has some applicability on public datasets, showing potential for practical and clinical applications. multi-point distribution label; cascade model; prior knowledge; bone age assessment; deep learning +
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0000-0001-9991-4500-3220]D. A.  
shown that the RUS-CHN method is more suitable for assessing the bone ages of Chinese children [11]. Automatic bone age assessment (BAA) has been one of the research focuses in radiology and other related fields due to the time-consuming and subjective factors of manual assessment, which lead to inconsistent results between the two assessments. For example, in the field of intelligence-assisted diagnosis and treatment, [PERSON] et al. proposed the first commercially available automated BAA system, called BoneXpert [12]. Although this method shortens assessment time, it does not use the carpal bone, which is necessary for infant age assessment [13]. In addition, the above method also requires the input images with extremely high quality, which poses an obstacle to its wide application. Many machine learning (ML) methods are often applied to BAA in previous studies. For example, [PERSON] et al. [14] used Multiple Kernel Learning (MKL) to incorporate heterogeneous features of the dataset into the network and then used Support Vector Regression (SVR) for classification, resulting in a mean absolute error (MAE) of 0.428 years. [PERSON] et al. [15] performed a series of image preprocessing such as background denoising, RoIs extraction, and distance length determination for hand bone images and developed BAA of semantic features. In addition, many researchers have solved BAA tasks using the decision tree method, K-Nearest-Neighbors (KNN) classification [16], and Support Vector Machine (SVM) [17]; however, the above methods utilize manual prior knowledge extraction, which is time-consuming and less accurate. With the development of deep learning (DL), DL models have been gradually applied to the medical field and have achieved certain goals. Compared with traditional manual methods that extract features using ML methods, DL models can automatically extract features quickly; thus, a multitude of BAA models based on DL have developed. For example, [PERSON] et al. [18] proposed the first BAA model based on convolutional neural networks (CNNs), called BoNet. [PERSON]. [PERSON] et al. [19] proposed a BAA model based on prior knowledge provided by the TW method. The RoIs of the TW method were extracted as prior knowledge, and then prior knowledge was input into the classification network to obtain bone age prediction values.