比较三种模型在骨龄预测中的表现，帮医生选更准更快的AI工具。
Comparative Analysis of Machine Learning Approaches for Bone Age Assessment: A Comprehensive Study on Three Distinct Models
- 用Xception、VGG、CNN三模型对比骨龄预测效果。
- 测试显示各模型在月级误差上存在差异，具体数值需查原文。
- 适合临床医生和研究者参考模型选择与部署方案。
放射科医生通过拍摄儿童非优势手的X光片来评估遗传病和生长异常的可能性，方法是将骨骼发育程度与实际年龄进行对比。传统上依赖Greulich-Pyle（GP）或Tanner-Whitehouse（TW）标准，但需要高专业水平且易受观察者主观偏见影响。为提高自动化程度与准确性，已开发多种机器学习模型。本研究系统分析了当前应用最广的三种模型：Xception、VGG和CNN，在预处理数据集上训练并以月份为单位计算平均绝对误差（MAE），进行性能比较。结果显示三者在预测精度与效率方面各有优劣，具体差异体现在实际误差数值上。
原文摘要 · Abstract (English)
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.
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