Annual Conference of KIPS (한국정보처리학회:학술대회논문집)

  • 2024.10a
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  • Pages.686-687
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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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Prognosis Prediction of Alzheimer's Disease: Multi-Horizon MMSE Prediction from MRI and Metadata

알츠하이머병 예후 예측: MRI 및 메타데이터를 활용한 MMSE 점수 예측 모델

  • Chaeeun Cho (Dept. of Medicine, Korea University College of Medicine) ;
  • Soyeon Moon (Dept. of Computer Engineering, Kyungsung University) ;
  • Yeogyeong Song (School of AI Convergence, Sungshin Women's University) ;
  • Jiwoo Jang (School of AI Convergence, Sungshin Women's University)
  • 조채은 (고려대학교 의과대학 의학과) ;
  • 문소연 (경성대학교 컴퓨터공학과) ;
  • 송여경 (성신여자대학교 AI융합학부) ;
  • 장지우 (성신여자대학교 AI융합학부)
  • Published : 2024.10.31
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Abstract

This study aims to predict MMSE scores in Alzheimer's disease (AD) patients using a CNN-LSTM model that processes MRI images and metadata. The OASIS-2 dataset was used, with MRI slices (central, ±10mm, and ±15mm) and metadata. Two datasets were created: one with central and ±10mm slices (10mm dataset), and another with central, ±10mm, and ±15mm slices (combined dataset). The CNN-LSTM model extracted features using VGG16 and combined them with metadata to predict MMSE scores. The 10mm model outperformed the combined model, achieving an MSE of 0.527 and MAE of 0.509. This study highlights the potential of predicting MMSE scores using MRI and metadata for early diagnosis of AD.

Keywords

Acknowledgement

본 논문은 과학기술정보통신부 대학디지털교육역량강화사업의 지원을 통해 수행한 ICT멘토링 프로젝트 결과물입니다.

References

  1. Samaneh A. Mofrad, "Cognitive and MRI trajectories for prediction of Alzheimer's disease", Scientific Reports, 11, 2122, 2021.
  2. Young Min Choe, "MMSE Subscale Scores as Useful Predictors of AD Conversion in Mild Cognitive Impairment", Neuropsychiatric Disease and Treatment, v16, 1767-1775, 2020.