Radiologists and doctors make use of X-ray images of the non-dominant hands of children and infants to assess the possibility of genetic conditions and growth abnormalities. This is done by assessing the difference between the actual extent of growth found using the X-rays and the chronological age of the subject. The assessment was done conventionally using The Greulich Pyle (GP) or Tanner Whitehouse (TW) approach. These approaches require a high level of expertise and may often lead to observer bias. Hence, to automate the process of assessing the X-rays, and to increase its accuracy and efficiency, several machine learning models have been developed. These machine-learning models have several differences in their accuracy and efficiencies, leading to an unclear choice for the suitable model depending on their needs and available resources. Methods: In this study, we have analyzed the 3 most widely used models for the automation of bone age prediction, which are the Xception model, VGG model and CNN model. These models were trained on the preprocessed dataset and the accuracy was measured using the MAE in terms of months for each model. Using this, the comparison between the models was done. Results: The 3 models, Xception, VGG, and CNN models have been tested for accuracy and other relevant factors.

翻译：放射科医师和医生利用儿童及婴幼儿非优势手的X射线图像来评估遗传性疾病和生长异常的可能性。这一过程通过比较X射线所显示的实际生长程度与受试者的实际年龄差异来实现。传统评估方法采用Greulich Pyle（GP）或Tanner Whitehouse（TW）方法。这些方法需要高度专业知识，且常易产生观察者偏差。因此，为自动化X射线评估过程并提高其准确性与效率，已开发出多种机器学习模型。这些机器学习模型在准确性和效率上存在显著差异，导致根据具体需求和可用资源选择合适模型时缺乏明确标准。方法：本研究分析了三种最广泛使用的骨龄预测自动化模型——Xception模型、VGG模型和CNN模型。这些模型在预处理数据集上进行训练，并采用平均绝对误差（MAE）以月为单位衡量各模型准确性。基于此结果进行模型间比较。结果：已对Xception、VGG和CNN三种模型的准确性及其他相关因素进行了测试评估。