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使用Vis-NIR和SWIR高光谱成像以及机器学习来检测骨感染

Lukas Kampik¹, Richard Andreas Lindtner¹, David Putzer¹

¹Department of Orthopaedics and Traumatology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|July 10, 2025

概括

超光谱成像 (HSI) 可以使用可见光和近红外 (Vis-NIR) 光快速检测骨感染. 这项技术在识别细菌污染方面具有很高的准确性,有助于更快的手术决策.

科学领域:

整形外科手术整形外科手术
医疗成像医学成像
微生物学微生物学

背景情况:

骨感染在骨科手术中带来了重大的诊断和治疗挑战.
目前的诊断方法,如组织培养,速度慢,缺乏灵敏度,延迟了关键的临床决策.
快速准确地检测骨感染对于有效的治疗和患者的治疗结果至关重要.

研究的目的:

调查高光谱成像 (HSI) 在可见和近红外 (Vis-NIR) 和短波红外 (SWIR) 光谱范围中的有效性,以快速检测骨感染.
评估用于分析光谱数据的机器学习算法,以区分受感染与未受感染的骨样.
评估HSI作为实时,无标签工具的潜力,用于手术内骨感染诊断.

主要方法:

活体人类骨样本被用来建立一个体外生物膜模型,其中包括金黄色葡萄球菌和上皮葡萄球菌.
超光谱成像 (HSI) 在Vis-NIR和SWIR光谱范围进行.
使用机器学习算法分析了光谱数据,包括k-最近邻居 (kNN),支向量机 (SVM),部分最小平方差分分析 (PLS-DA) 和类类类比的软独立建模 (SIMCA).

主要成果:

与基于SWIR的分类相比,Vis-NIR-HSI模型显示出更高的性能.
在区分注射和未注射的人类骨样本方面,获得了高达99.58%的分类精度.
这种Vis-NIR-HSI方法使得细菌物种的准确分化成为可能.

结论:

视-NIR-HSI显示出作为一个实时,无标签的内科手术工具来检测骨感染的巨大潜力.
这项技术可以弥合术前成像和延迟的微生物学结果之间的差距.
这些发现支持在怀疑骨感染的情况下立即做出手术决策.

关键词:

骨感染骨感染诊断诊断诊断诊断诊断与骨折相关的感染.超光谱成像技术的使用.机器学习,手术内成像周围前列腺关节感染频谱学是一种光谱学.

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