Annual Conference of KIPS (한국정보처리학회:학술대회논문집)

  • 2024.10a
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  • Pages.565-568
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  • 2024
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  • 2005-0011(pISSN)
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  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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Long-Term Cognitive Prediction in Parkinson's Disease Based on Clinical Features and Deformation Morphometry

  • Yishan Jiang (Department of Artificial Intelligence Convergence, Chonnam National University) ;
  • Hyung-Jeong Yang (Department of Artificial Intelligence Convergence, Chonnam National University)
  • Published : 2024.10.31
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Abstract

Parkinson's disease (PD) is a progressive disorder. In this study, we proposed a deep learning model that utilized participants' baseline clinical features and deformation-based morphometry (DBM) to predict long-term cognitive trajectory over four years. A total of 216 participants from the PPMI (Parkinson's Progression Markers Initiative) dataset were included, with 157 being PD patients and 59 healthy controls. We identified brain connectivity patterns associated with long-term cognitive decline using DBM and independent component analysis (ICA) techniques. Results of the cognitive prediction indicated that using only clinical features, DBM features, and multimodal features yielded average accuracies of 76 ± 4%, 70 ± 6%, and 78 ± 2%, and average AUC (Area Under the Curve) of 0.71 ± 0.06, 0.62 ± 0.04, and 0.76 ± 0.06, respectively. Our study demonstrated that the potential of using DBM features to better predict disease progression.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00208397) and was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant funded by the Korea government (MSIT). This research was also supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2024-RS-2024-00437718) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).

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