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Quarrying is the process of extracting stone from a quarry, where specialized techniques are employed to remove large blocks of stone safely and efficiently. This process can involve controlled explosions or more precision-oriented methods such as cutting and drilling.
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Building stones, essential materials for construction, are extracted from natural rock deposits and processed into specific forms and dimensions suitable for various building applications. These stones are broadly classified into three types based on their geological formation: igneous, sedimentary, and metamorphic.
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Stone masonry is a construction technique that uses individual stones to build structures and can be categorized into two main types: rubble and ashlar. Rubble masonry uses uneven, naturally shaped stones such as river rocks or fragments from quarries. This method often requires the mason to select and possibly shape each stone to fit the designated space, ensuring a proper build, even with irregular stone sizes and shapes. Ashlar masonry, on the other hand, employs uniformly cut stones that...
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人工智能和机器学习用于石头管理

Adithya Balasubramanian1, Hriday Bhambhvani1, Justin Lee2

  • 1Department of Urology, Weill Cornell Medical College, Starr Pavilion, 525 East 68th Street 9th Floor, New York, NY 10065, USA; Department of Urology, Columbia Irving Medical Center, 161 Ft. Washington Avenue, 11th Floor, New York, NY 10032, USA.

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概括
此摘要是机器生成的。

人工智能和机器学习 (ML) 正在彻底改变结石管理. 这些技术增强了诊断,预测了治疗结果,并个性化了尿病患者的护理.

关键词:
人工智能的人工智能是人工智能.结石 结石是指结石的出现.机器学习是机器学习.通过皮肤进行神经质切除术.冲击波的石灰质三氧化.石头分析石头分析尿路透镜检查是指尿路透镜检查.乌罗石质病是一种质病.

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科学领域:

  • 泌尿器科 泌尿器科 泌尿器科 泌尿器科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 石头疾病的管理正在迅速推进新技术.
  • 人工智能 (AI) 和机器学习 (ML) 为尿病护理提供了巨大的潜力.
  • 目前的应用侧重于诊断,治疗和预防策略.

研究的目的:

  • 探索ML算法在改善尿病导向成像中的作用.
  • 评估ML在预测各种石材处理结果方面的潜力.
  • 突出ML在优化石材组成分析和异常检测方面的作用.

主要方法:

  • 审查关于ML在尿病中的应用的当前文献.
  • 在诊断成像中分析ML算法潜力.
  • 评估ML预测治疗成功的自发性石头通道,尿路透镜,冲击波石,和皮肤穿透性nephrolithotomy.

主要成果:

  • ML算法在提高尿病成像的准确性方面表现有前途.
  • 使用ML的预测模型可以改善自发石头通道和手术干预的结果.
  • ML可以优化石头组成的分析和检测尿路异常.

结论:

  • 基于ML的创新已经准备好个性化结石治疗.
  • 这些技术将大大提高石头疾病管理的效率和有效性.
  • 整合ML代表了泌尿器科护理的新前沿.