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Video-based evaluation system for tic action in Tourette syndrome: modeling, detection, and evaluation

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Abstract

Behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in Tourette syndrome. Detecting tic symptoms plays an important role in patient treatment and evaluation; accurate tic identification is the key to clinical diagnosis and evaluation. In this study, we proposed a tic action detection method using face video feature recognition for tic and control groups. Through facial ROI extraction, a 3D convolutional neural network was used to learn video feature representations, and multi-instance learning anomaly detection strategy was integrated to construct the tic action analysis and discrimination framework. We applied this tic recognition framework in our video dataset. The model evaluation results achieved average tic detection accuracy of 91.02%, precision of 77.07% and recall of 78.78%. And the tic score curve with postprocessing provided information of how the patient’s twitches change over time. The detection results at the individual level indicated that our method can effectively detect tic actions in videos of Tourette patients without the need for fine labeling, which is significant for the long-term evaluation of patients with Tourette syndrome.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82172069), the National Key Research and Development Program of China (No. 2022YFC2504605), the Key Research Project of Zhejiang Lab Grants (No. 2022ND0AC01), the Fundamental Research Funds for the Central Universities (226-2023-00050).

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Author notes

  1. Junya Wu and Tianshu Zhou have contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory for Biomedical Engineering of Ministry of Education, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China

    Junya Wu, Yu Tian & Jingsong li

  2. Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, 311100, China

    Tianshu Zhou & Jingsong li

  3. Department of Pediatrics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China

    Yufan Guo, Yuting Lou & Jianhua Feng

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  1. Junya Wu
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  4. Yu Tian
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  5. Yuting Lou
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Correspondence to Jingsong li.

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This study was approved by the Ethics Committee of the Second Affiliated Hospital of Zhejiang University School of Medicine (YAN2019-148).

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Wu, J., Zhou, T., Guo, Y. et al. Video-based evaluation system for tic action in Tourette syndrome: modeling, detection, and evaluation. Health Inf Sci Syst 11, 39 (2023). https://doi.org/10.1007/s13755-023-00240-z

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