通过深度学习对下沟通道及其前环进行自动细分
Nicolly Oliveira-Santos1,2, Reinhilde Jacobs3,4, Fernando Fortes Picoli1,5
1OMFS IMPATH Research Group, Department of Imaging and Pathology, KU Leuven and University Hospitals Leuven, UZ Campus St Rafael, Leuven, Belgium.
Scientific reports
|July 4, 2023
概括
人工智能 (AI) 准确地划分下沟通道 (MC),包括像前环 (AL) 这样的解剖变异. 这种人工智能工具通过自动化神经血管通道划线来增强牙科植入物安置的术前规划.
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:
- 精确的下管 (MC) 划线对于预防口腔手术期间的神经损伤至关重要.
- 解剖学复杂性,包括前环 (AL),挑战了在肌肉间区域的精确MC细分.
- 圆束计算机断层扫描 (CBCT) 有助于术前规划,但由于解剖学变异和MC皮层状况差,它面临着局限性.
研究的目的:
- 开发和验证人工智能 (AI) 驱动的工具,用于精确细分MC.
- 评估人工智能工具在界定MC方面的有效性,特别是在存在像AL这样的解剖学变异的情况下.
- 评估人工智能工具在改善牙科手术前规划方面的潜力.
主要方法:
- 使用CBCT数据进行专用AI工具的培训和验证.
- MC的细分,有和没有前环 (AL).
- 使用高精度指标对细分精度的定量评估.
主要成果:
- 人工智能工具在MC细分方面实现了0.997的全球准确度,无论是用AL还是不用AL.
- 细分精度在MC的前部和中间部分是最高的,这对于手术干预至关重要.
- 人工智能工具在对MC进行细分方面表现强,尽管存在AL等解剖学变异.
结论:
- 经过验证的AI工具准确地细分下沟通道及其解剖学变异,包括前环.
- 这种人工智能驱动的方法可以自动化神经血管通道的细分,帮助临床医生进行术前规划.
- 该工具显示了改善牙植入物放置的巨大潜力,特别是在具有挑战性的牙间区域.
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