Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data
Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data
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DOI：
10.1364/boe.10.003889
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发表时间：
2019-08-01
影响因子：
3.4
通讯作者：
Fick, Daniel
中科院分区：
文献类型：
--
作者：
Gunaratne, Rajitha;Monteath, Isaac;Fick, Daniel
目的：评估将DRS传感纳入实时机器人手术系统是否具有优点。DRS作为一项技术相对简单。具有成本效益，并提供了一种非接触的组织分化方法。研究方法：对漫反射光谱进行监督机器学习分析，以对从外科手术中收集的人体关节组织进行分类。结果如下：我们已经在DRS人体关节组织数据集的分类中使用了监督机器学习，并实现了超过99%的分类准确率。各种类别的敏感性为：软骨99.7%，软骨下99.2%。半月板100%，松质骨100%。全波长范围是最大的分类精度所必需的。波长分辨率必须大于8 nm。要达到大于50%的分类准确度，需要优于10：1的SNR。800- 900 nm波长范围在所研究的那些中给出了最大的准确性。结论：DRS是用于区分人类关节组织的可行方法，并且具有被并入机器人矫形外科手术的潜力。(C)根据OSA开放获取出版协议的条款，2019年美国光学学会
Objective: To assess if incorporation of DRS sensing into real-time robotic surgery systems has merit. DRS as a technology is relatively simple. cost-effective and provides a non-contact approach to tissue differentiation. Methods: Supervised machine learning analysis of diffuse reflectance spectra was performed to classify human joint tissue that was collected from surgical procedures. Results: We have used supervised machine learning in the classification of a DRS human joint tissue data set and achieved classification accuracy in excess of 99%. Sensitivity for the various classes were; cartilage 99.7%, subchondral 99.2%. meniscus 100% and cancellous 100%. Full wavelength range is required for maximum classification accuracy. The wavelength resolution must be larger than 8nm. A SNR better than 10:1 was required to achieve a classification accuracy greater than 50%. The 800-900nm wavelength range gave the greatest accuracy amongst those investigated Conclusion: DRS is a viable method for differentiating human joint tissue and has the potential to be incorporated into robotic orthopaedic surgery. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement