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Extraction: Advanced Methods00:56

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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杆有效的深度学习搜索方法用于法医年龄估计.

Zhi-Yong Zhang1,2,3, Chun-Xia Yan2, Qiao-Mei Min3

  • 1Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an 710004, China.

Bioengineering (Basel, Switzerland)
|July 27, 2024
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概括
此摘要是机器生成的。

这项研究介绍了AGENet和AGE-SPOS,这两种新型深度神经网络模型用于精确地估计牙年龄,使用的是orthopantomograms. 这些人工智能方法显著提高了准确性,特别是对于成年人来说,推进了法医医学.

关键词:
神经架构搜索 (NAS) 是一种神经架构搜索.年龄估计年龄估计.深度神经网络 (DNN) 是一个深度神经网络.骨科视图 (OPG) 是一种指标.

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科学领域:

  • 法医牙科 法医牙科
  • 人工智能在医学中的应用
  • 放射学 放射学是一门学科.

背景情况:

  • 传统的牙科年龄估计方法缺乏精度,特别是对于成年人.
  • 整形眼镜 (OPG) 为年龄评估提供了丰富的数据.
  • 在法医背景下需要准确有效的年龄估计.

研究的目的:

  • 开发和验证新的深度学习模型,以使用OPGs准确估计牙年龄.
  • 探索最佳的神经网络架构来分析牙科数据.
  • 为法医应用创建高性能和轻量级模型.

主要方法:

  • 创建一个大规模的牙科数据集 (27,957 OPGs) 与经过验证的年龄注释.
  • 神经网络组件的分析:深度,内核大小,多分支架构和功能重用.
  • 开发和评估两个深度神经网络模型:AGENet (高性能) 和AGE-SPOS (轻量级).

主要成果:

  • AGENet 实现了 1.70 年的平均绝对误差 (MAE),超过了 Inception-v4,并将计算成本降低了 2.7 倍.
  • 轻量级的AGE-SPOS模型实现了1.80年的MAE,比MobileNetV2.2的计算要少得多.
  • 两种模型都在法医年龄估计任务中表现出卓越的有效性和准确性.

结论:

  • 提出的深度神经网络搜索方法提供了有效的法医年龄估计从OPGs.
  • AGENet 和 AGE-SPOS 在牙科年龄预测的准确性和计算效率方面取得了重大进展.
  • 这些发现对改善口腔和大面部成像中的年龄估计技术具有重大意义.