Facial expression monitoring system for predicting patient's sudden movement during radiotherapy using deep learning.

Journal of Applied Clinical Medical Physics
Kwang Hyeon KimJong Hwi Jeong

Abstract

Imaging, breath-holding/gating, and fixation devices have been developed to minimize setup errors so that the prescribed dose can be exactly delivered to the target volume in radiotherapy. Despite these efforts, additional patient monitoring devices have been installed in the treatment room to view patients' whole-body movement. We developed a facial expression recognition system using deep learning with a convolutional neural network (CNN) to predict patients' advanced movement, enhancing the stability of the radiation treatment by giving warning signs to radiation therapists. Convolutional neural network model and extended Cohn-Kanade datasets with 447 facial expressions of source images for training were used. Additionally, a user interface that can be used in the treatment control room was developed to monitor real-time patient's facial expression in the treatment room, and the entire system was constructed by installing a camera in the treatment room. To predict the possibility of patients' sudden movement, we categorized facial expressions into two groups: (a) uncomfortable expressions and (b) comfortable expressions. We assumed that the warning sign about the sudden movement was given when the uncomfortable expression wa...Continue Reading

References

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Dec 20, 2018·Psychiatry and Clinical Neurosciences·Karol GrabowskiIzabela Łucka

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Citations

Jul 22, 2021·Journal of Medical Imaging and Radiation Oncology·Adam MylonasDoan Trang Nguyen

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Methods Mentioned

BETA
irradiate

Software Mentioned

Anaconda
CNN
Keras Deep Learning Library
Python

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