高度预测模型的评估:从传统方法到人工智能
Ana G Chávez-Vázquez1, Miguel Klünder-Klünder2, Nayely G Garibay-Nieto3
1Unit of Epidemiological Research in Endocrinology and Nutrition, Hospital Infantil de México Federico Gómez, Mexico City, Mexico.
Pediatric research
|September 21, 2023
概括
使用BoneXpert的自动骨年龄读数提供了比传统手动方法更可靠的方法来预测成人身高. 这种人工智能驱动的方法减少了变化,并提高了儿童预测成年人身高的准确性.
科学领域:
- 儿科内分泌学 儿科内分泌学
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 传统的成年人身高预测 (AHP) 依赖于手动骨年龄 (BA) 评估.
- 人工智能 (AI) 正在提高BA读数和AHP模型的准确性.
研究的目的:
- 确定当前墨西哥人口中最准确的成年人身高预测 (AHP) 模型.
- 为了比较传统的手动BA读数与AHP中的自动化方法 (BoneXpert) 的性能.
主要方法:
- 对1173名年龄在5-18岁之间的参与者进行了横截面研究.
- 骨年龄 (BA) 读数由两名专家手动执行,并使用BoneXpert.自动执行.
- 成人身高预测 (AHP) 模型的评估基于接近人口的平均身高.
主要成果:
- 所有测试的AHP模型都高估了人口的平均身高.
- 骨Xpert显示男性的差异最小;贝利和皮诺对女性的差异最小.
- 手动BA读数显示了观察者之间的显著变化 (高达43%的差异>5cm).
结论:
- 传统的AHP模型中的手动BA读数引入了高的观察者间变异性.
- 自动化BoneXpert方法是AHP最可靠的方法,减少了可变性.
- 骨Xpert提供了接近人口平均身高的一致的AHP结果.
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