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相关概念视频

Bone Disorders01:29

Bone Disorders

3.4K
Aging and its effect on bone remodeling is the most common cause of bone disorders. In young and healthy people, bone deposition and resorption happen at an equal rate to maintain optimal bone health.
Bone deposition is also affected by the levels of sex hormones like estrogen and testosterone that promote osteoblast activity and bone matrix synthesis. When the level of these hormones decreases due to aging, it causes a reduction in bone deposition. As a result, bone resorption by osteoclasts...
3.4K

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相关实验视频

Updated: May 22, 2025

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
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Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

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自动骨龄评估:一项土耳其人口研究.

Samet Öztürk1, Murat Yüce2, Gül Gizem Pamuk3

  • 1Esenler Obstetrics & Gynecology and Pediatrics Hospital, Clinic of Radiology, İstanbul, Türkiye.

Diagnostic and interventional radiology (Ankara, Turkey)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

这项研究为土耳其人口开发了一种使用深度学习的自动骨年龄评估 (BAA) 模型,与传统方法相比,显示出有希望的准确性和效率.

关键词:
骨年龄评估 骨年龄评估开始V3 开始V3人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.

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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
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Author Spotlight: An Economic and Efficient Method for Quantitative Evaluation of Bone Microarchitecture in a Murine Osteoporosis Model
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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
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科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 儿科内分泌学 儿科内分泌学

背景情况:

  • 传统的骨质年龄评估 (BAA) 方法,如Greulich和Pyle地图集,由于人口变化和观察者主观性而存在局限性.
  • 使用深度学习的自动化BAA提供了提高速度和一致性的潜力,但跨多种群体的研究是有限的.

研究的目的:

  • 在土耳其人群中评估BAA深度学习算法的性能.
  • 通过调查人口因素和数据异质性的影响来增强骨龄模型.

主要方法:

  • 经过修改的InceptionV3深度学习模型使用来自土耳其队列 (Bağcılar医院) 的2,730张手部放射图进行了训练和验证,并与公共数据集 (RSNA,RHPE) 结合起来.
  • 该模型处理了500 × 500像素的图像,并根据验证集中最低平均绝对误差 (MAE) 选择了表现最好的模型.
  • 总共分析了19387张X射线图,其中546张被随机分为内部测试.

主要成果:

  • 组合模型实现了高准确性,在内部测试组中的94%情况下,在24个月内估计骨年龄.
  • 平均绝对误差 (MAE) 总体为9.2个月,公共测试组为7个月,土耳其 (Bağcılar) 内部测试数据为11.5个月.
  • 仅在土耳其数据上训练的模型的MAE为12.7个月,与土耳其数据集上的组合模型相比没有显著差异 (P > 0.05),而仅在公共模型上的表现明显差 (MAE为16.5个月,P < 0.05).

结论:

  • 使用深度学习成功开发了纳入土耳其人口的自动化BAA模型,解决了当前研究中的差距.
  • 该模型在临床环境中证明了其有效性,为传统,耗时的BAA方法提供了可靠和高效的替代方案.
  • 持续的数据积累和各种数据集的整合可以进一步完善模型的准确性,改善临床决策和患者护理.