使用人工智能在儿科骨X射线中预测生物性别.
Michael Janisch1, Mario Scherkl2, Nikolaus Stranger3
1Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Styria, Austria.
Scientific reports
|December 12, 2025
概括
人工智能 (AI) 可以准确地从儿科放射图片中预测生物性别,超过人类评估. 这种人工智能驱动的医学成像方法显示了增强儿科临床实践的前景.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 儿科放射学 儿科放射学
背景情况:
- 人工智能 (AI) 在医学成像中的应用正在扩大.
- 人工智能在从儿科放射图片中确定生物性别方面的有效性尚未得到充分证实.
研究的目的:
- 评估卷积神经网络 (CNN) 模型在从儿科放射图中分类生物性别方面的性能.
- 将人工智能模型的性能与人类评分器进行比较.
主要方法:
- 利用了大量的儿科创伤放射图 (计算机和数字放射) 数据集.
- 处理图像用于灰度正常化和对比度增强.
- 训练有素的EfficientNet CNN模型 (B0-B7),根据年龄,性别和骨折可见性平衡数据集.
- 将AI性能与由人类评分员评估的1000张图像的子集进行了比较.
主要成果:
- 人工智能模型实现了0.722±0.032的平均精度,精度,回忆和F1分数也表明了有效的分类.
- 性能显示患者年龄的增加,在13-18岁年龄组达到顶峰.
- 盆腔X射线产生了最高的分类指标.
- 与人工智能模型相比,人类评级者表现出明显较低的协议.
结论:
- 人工智能模型可以准确地从儿科放射图片中分类生物性别,超过人类的性能.
- 人工智能分类的有效性取决于患者的年龄和解剖区域 (例如骨盆).
- 人工智能辅助的医学成像具有改善儿科临床工作流程的潜力.
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