Mechanical Engineering Department, Thapar Institute of Engineering and Technology Patiala, Punjab 147004 India.
Mechanical Engineering Department, Thapar Institute of Engineering and Technology Patiala, Punjab 147004 India.
Med Eng Phys. 2022 Dec;110:103869. doi: 10.1016/j.medengphy.2022.103869. Epub 2022 Aug 6.
Bone drilling is frequently used during orthopaedic surgeries to treat the fractured part of the bone. A major concern for surgeons is the increase in temperature during real-time orthopaedic bone drilling. The temperature elevation at the bone-tool interface may cause permanent death of regenerative soft tissues and cause thermal osteonecrosis. A robust predictive machine-learning model is suggested in this in-vitro research for monitoring temperature rise during surgery. The objective of the present work is to introduce different machine learning algorithms for predicting temperature elevations in rotary ultrasonic bone drilling. Different machine-learning models were compared with the standard response surface methodology. The performance and accuracy of different predictive models were compared at different error metrics. It was witnessed that support vector machines performed the best for predicting the change in temperature in comparison to other predictive models. Moreover, the error metrics for statistical response surface methodology analysis were comparatively higher than the machine learning algorithms. By using machine learning models, it is possible to predict temperature rise during bone drilling.
骨钻通常用于骨科手术中以治疗骨折部位。外科医生主要关注的是实时骨科骨钻过程中的温度升高。骨-工具界面处的温度升高可能导致再生软组织的永久性死亡，并引起热骨坏死。本体外研究中建议采用强大的预测机器学习模型来监测手术过程中的温升。本工作的目的是介绍用于预测旋转超声骨钻中温度升高的不同机器学习算法。将不同的机器学习模型与标准响应面方法进行了比较。在不同的误差指标下比较了不同预测模型的性能和准确性。结果表明，与其他预测模型相比，支持向量机在预测温度变化方面表现最佳。此外，统计响应面方法分析的误差指标明显高于机器学习算法。通过使用机器学习模型，可以预测骨钻过程中的温升。
