基于手部放射图的骨年龄评估,使用基于回归的多模式深度学习
Jeoung Kun Kim1, Donghwi Park2, Min Cheol Chang3
1Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.
Life (Basel, Switzerland)
|June 27, 2024
概括
一个新的深度学习模型使用手部X射线和临床数据准确评估骨年龄. 该模型在女性,特别是年轻女性中表现出卓越的表现,突出了其用于儿科骨年龄评估的潜力.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 儿科 儿科 儿科
背景情况:
- 准确的骨年龄评估 (BAA) 对于诊断和管理儿科生长障碍至关重要.
- 传统的BAA方法可能是主观和耗时的.
- 深度学习为客观和高效的BAA提供了潜力.
研究的目的:
- 开发和评估骨年龄评估的多模式深度学习模型.
- 将手部放射图像与临床数据相结合,以提高BAA准确度.
- 为了解决BAA.数据不平衡和样本大小限制的问题.
主要方法:
- 一个基于回归的多模态深度学习模型是使用2974个儿科手部X射线图开发的.
- 使用EfficientNetV2S卷积神经网络 (CNN) 处理了手部放射图.
- 临床数据 (性别,时间年龄) 通过深度神经网络 (DNN) 进行了整合.
主要成果:
- 该模型实现了0.410的总平均绝对误差 (MAE),准确率为91.1%.
- 在女性中观察到更高的精度 (≤11年:MAE 0.267,95.0%的精度;>11年:MAE 0.402,92.4%的精度).
- 男性的准确性较低 (≤13年:MAE 0.665,准确率为79.7%;>13年:MAE 0.647,准确率为84.6%).
结论:
- 开发的多模式深度学习模型在骨年龄评估方面表现良好.
- 该模型在与男性相比的女性儿科患者中表现出更高的准确性.
- 该模型在11岁及以下的女性儿科医生中表现特别强.
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