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相关概念视频

Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over...
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深度学习算法用于检测根通道中的破碎仪器.

Ekin Deniz Çatmabacak1, İrem Çetinkaya2

  • 1Department of Endodontics, Trakya University, Balkan Campus, Edirne, Turkey.

BMC oral health
|February 23, 2025
PubMed
概括

在DenseNet201上,DenseNet201在检测骨折的内牙仪器 (FEI) 上表皮放射图上表现出卓越的性能. 这种深度学习模型显示了改善根管治疗结果的临床潜力.

科学领域:

  • 牙科 牙科是指牙科的专业.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 在周围放射图 (PA) 中识别破碎的内牙仪器 (FEI) 是关键的,但在根管治疗 (RCT) 中具有挑战性.
  • 深度学习 (DL) 模型是一个潜在的解决方案,但缺乏比较性能分析.

研究的目的:

  • 评估和比较五种深度学习模型的性能,以检测PA中的FEI.
  • 确定最有效的DL模型用于FEI检测在牙周内科.

主要方法:

  • 用于培训,验证和测试的数据集包括700个注释的PA (381个含FEI).
  • 五个DL模型 (DenseNet201,EfficientNet B0,ResNet-18,VGG-19,MaxVit-T) 使用转移学习和数据增强进行了训练.
  • 使用准确度,AUC和MCC评估性能,并通过弗里德曼测试进行统计分析.

主要成果:

  • 在FEI检测中,DenseNet201获得了最高的AUC (0.900) 和MCC (0.810).
  • ResNet-18显示出强的结果,而EfficientNet B0和VGG-19的表现中等.
  • 马克斯维特-T显著表现不佳;统计分析表明模型之间存在显著差异 (p < 0.05).
关键词:
深度学习是一种深度学习.在DenseNet 201中使用.破裂的牙周内仪器 牙周内仪器破裂周围的X射线图 (Periapical Radiography) 是指周围的X射线图,是指周围的X射线图.

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结论:

  • 丹森网201在精确的FEI检测方面具有显著的临床潜力.
  • ResNet-18提供了准确性和计算效率的良好平衡.
  • 模型-任务对齐和优化对于医学成像应用至关重要.