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Osteoclasts are cells responsible for bone resorption and remodeling. They originate from hematopoietic progenitor cells present in the bone marrow. Numerous progenitor cells fuse to form multinucleated cells, each with 10-20 nuclei. A single osteoclast has a diameter of 150 to 200 µM. These cells have ruffled borders that break down the underlying bone tissue and release minerals such as calcium into the blood in bone resorption. Osteoclasts cling to bones with their ruffled edges during...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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开发和比较用于骨质疏松风险预测的深度学习和机器学习算法.

Chuan Qiu1, Kuanjui Su1, Zhe Luo1

  • 1Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, United States.

Frontiers in artificial intelligence
|June 26, 2024
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概括
此摘要是机器生成的。

深度学习模型,特别是深度神经网络 (DNN),在预测骨质疏松症风险方面表现出比传统方法更高的准确性. 这种新的DNN框架有效地识别了有风险的个人,有助于早期诊断和干预.

关键词:
骨矿物质密度 骨矿物质密度深度学习是一种深度学习.疾病预测 疾病预测机器学习是机器学习.骨质疏松症是一种骨质疏松症.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 医疗保健中的机器学习
  • 老年学是指老年学的学科.

背景情况:

  • 骨质疏松症是由低骨矿物质密度 (BMD) 定义的,它是一个重大的公共卫生挑战.
  • 目前用于骨质疏松风险预测的回归和机器学习 (ML) 模型在临床环境中显示出有限的准确性.
  • 深度学习 (DL) 方法,就像深度神经网络 (DNN) 一样,通过发现复杂的数据交互来提高预测的潜力.

研究的目的:

  • 评估一种新的DNN框架在预测骨质疏松风险方面的性能.
  • 将DNN模型的预测精度与传统的ML算法和传统回归模型进行比较.
  • 通过特征重要性分析,确定骨质疏松风险的关键预测变量.

主要方法:

  • 使用部BMD和来自路易斯安那骨质疏松症研究 (LOS) 的8134名40岁以上受试者的临床数据开发了一个新的DNN框架.
  • 将DNN模型的性能与随机森林 (RF),人工神经网络 (ANN),K-最近邻居 (KNN),支持矢量机器 (SVM) 和骨质疏松自我评估工具 (OST) 相比较.
  • 使用接收器操作曲线下的面积 (AUC) 和准确度指标来评估性能.

主要成果:

  • 在分类骨质疏松症风险方面,DNN方法实现了最高的预测性能 (AUC = 0.848).
  • 通过DNN识别的关键预测变量包括体重,年龄,性别,握力和身高.
  • DNN模型保持了高性能 (AUC = 0.846) 即使减少了特征集 (前10个变量) 和样本大小 (50%的减少).

结论:

  • 一个新的DNN模型证明了在老年人群中早期诊断和干预骨质疏松症的有效性.
  • 对于骨质疏松症风险预测的传统方法而言,DNN提供了有希望的进步.
  • 该模型在减少数据的情况下的稳定性突出显示了其实际临床应用的潜力.