A neural network framework for immediate temperature prediction of surgical hand-held drilling

Background and objective: Heat generation and associated temperature rise in surgical drilling can cause irreversible tissue damage. It is nearly impossible to provide immediate temperature prediction for a hand-held drilling process since both feed rate and motion vary with time. The objective of this study is to present and test a framework for immediate bone drilling temperature visualization based on a neural network (NN) model and a linear time-invariant (LTI) model. Methods: In this study, the finite element analysis (FEA) model is used as the ground truth. The NN model is used to predict the location-dependent thermal responses of FEA, while LTI is used to superimpose these responses based on the location history of the heat source. The use of LTI can eliminate the uncertainty of the unlimited possibility in the time domain. To test the framework, two three-dimensional drilling cases are studied, one with a constant drilling feed and straight path and the other with a varying feed and a varying path. Results: The NN model using U-net architecture can achieve the predicted correlation of over 97% with only 1% of the total number of data points. Using the framework with U-net and LTI, both case studies show good agreement in temporal and spatial temperature distributions with the ground truth. The average error near the drilling path is less than 10%. Discrepancies are mainly found near the heat source and the regions near the removed material. Conclusions: An FEA surrogate model for rapid and accurate prediction of 3D temperature during arbitrary bone drilling is successfully made. The overall error is less than 5% on average in the two case studies. Future improvements include strategies for training data selection and data formating. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

The objective of this study is to develop and test a framework for immediate visualization of bone drilling temperature based on a neural network and a linear time-invariant model. The results show that the framework can accurately predict the temperature distribution during bone drilling, with an average error of less than 5%. Future improvements include strategies for training data selection and formatting.