自动化的Greulich和Pyle:对细分方法的成熟?
Rashmi Chapke1, Shruti Mondkar2, Chirantap Oza2
1Department of Biology, Indian Institute of Science Education and Research Pune, Pune, India.
Frontiers in artificial intelligence
|March 27, 2024
概括
使用深度学习网络的自动骨年龄评估 (BAA) 可以预测成熟度类的分布. 一种新的细分GP方法分析骨组以了解偏差,并将Greulich和Pyle地图集扩展到新种群.
科学领域:
- 医学成像分析分析 医学成像分析
- 儿科内分泌学 儿科内分泌学
- 医疗保健中的人工智能
背景情况:
- 格鲁利奇和皮尔 (GP) 方法是使用手部X射线和图谱模板进行骨年龄评估 (BAA) 的标准.
- 深度学习方法有先进的自动化BAA,但它们与GP类的偏差需要进一步理解.
研究的目的:
- 审查自动 BAA 方法并提出关于网络预测的新假设.
- 引入和验证细分GP方法,用于合格的BAA预测.
主要方法:
- 审查当前的自动化BAA技术,重点关注深度学习的成功和局限性.
- 提出了一个假设,即网络预测分布,而不仅仅是离散类.
- 开发和应用细分GP方法来分析特定骨组的骨成熟度.
主要成果:
- 自动化BAA网络可以捕捉超出离散GP类的细微差别,预测年龄分布.
- 分段GP方法可以识别手中的差异性到期期.
- 分段GP方法提供了一种系统的方法,以适应GP图谱的多样化人口.
结论:
- 了解自动 BAA 中的偏差需要将预测视为分布.
- 分段老化分析为解释自动化BAA结果提供了有价值的工具.
- 分段GP方法增强了GP图谱的实用性,并支持更广泛的BAA应用.
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