在二维超声波中自动测量的尺寸与专家超声波仪相比较
Rohit Singla1, Cailin Ringstrom2, Ricky Hu3
1University of British Columbia, School of Biomedical Engineering, Applied Science and Medicine, Vancouver, British Columbia, Canada.
Journal of medical imaging (Bellingham, Wash.)
|June 12, 2023
概括
一个自动化的机器学习工具准确地从二维超声图像中测量的尺寸,为专家超声学家提供可比的结果,以提高疾病监测和效率.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 脏测量对于诊断和监测脏疾病和异常至关重要.
- 从超声波图像中手动测量的尺寸容易出现错误和变化.
- 对生物识别的自动定量可以解决手工方法的局限性.
研究的目的:
- 开发和评估一种自动机器学习方法,用于从二维超声图像中量化脏的长度,宽度和体积.
- 将自动化方法的准确性和可重复性与专家和新手手工测量进行比较.
- 评估自动化工具在原生和移植脏的临床应用中的潜力.
主要方法:
- 在514个脏超声波图像上训练了一种nnU-net机器学习模型进行细分.
- 专家和新手在132个超声波器上手动测量了的长度和宽度.
- 应用了自动细分算法来测量尺寸和估计体积.
主要成果:
- 该自动化算法在测量的长度和宽度方面,表现出与专家超声波仪器相似的准确性.
- 布兰德-阿尔特曼分析显示了算法和专家测量 (2.6毫米) 之间的最小平均差异.
- 这种自动化方法的准确性和可重复性与手动测量对体积估计的准确性和可重复性相当.
结论:
- 从二维超声波测量生物识别的自动化工具是可行的和准确的.
- 开发的工具显示了提高效率和帮助跟踪疾病进展的潜力.
- 这种方法为手工测量提供了可靠的替代方案,减少了变化和错误.
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