自动化肌量化和成长图表为儿童通过成年期
Anna Zapaishchykova1,2, Kevin X Liu2, Anurag Saraf1,2
1Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
Nature communications
|November 9, 2023
概括
这项研究引入了一种自动化的深度学习方法,通过脑MRI测量肌厚度. 这项创新为儿童和成年人肌肉质量跟踪提供了关键的正常生长图表.
科学领域:
- 生物医学工程 生物医学工程
- 放射学 放射学是一门学科.
- 人体生理学 人体生理学
背景情况:
- 瘦肌量 (LMM) 对于健康至关重要,但肌肉厚度,一个潜在的LMM标志物,缺乏正常生长数据和标准化测量.
- 目前评估肌厚度的方法有限,这阻碍了其临床实用性.
研究的目的:
- 开发一个自动化的深度学习管道,从常规的大脑MRI中准确测量肌厚度.
- 在健康个体中生成特定于性别的正常生长图表和肌厚度的参考范围.
- 探索肌厚度与生理特征之间的关联,并验证测量的临床可行性.
主要方法:
- 开发了一种自动化的深度学习管道,用于从脑MRI中测量肌厚度 (iTMT).
- 该管道应用于4至35岁的健康受试者的23,876个MRI.
- 在不同的人口统计和儿科脑瘤患者中,iTMT测量得到了验证.
主要成果:
- 以性别为特征的iTMT正常生长图表与百分位数为4-35岁生成.
- iTMT与热量摄入量,体力活动,性激素和恶性瘤有关.
- iTMT管道已经证明了个性化的纵向监测和临床应用的可行性.
结论:
- 自动化的iTMT管道提供了一种标准化,准确的方法来评估肌厚度.
- 该工具提供了对人类肌发育的新见解,并使LMM跟踪用于临床决策.
- iTMT有可能提高健康评估和监测,特别是在儿科患者群体中.
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