Predicting progression to dementia with "comprehensive visual rating scale" and machine learning algorithms

Chaeyoon Park; Jae-Won Jang; Gihun Joo; Yeshin Kim; Seongheon Kim; Gihwan Byeon; Sang Won Park; Payam Hosseinzadeh Kasani; Sujin Yum; Jung-Min Pyun; Young Ho Park; Jae-Sung Lim; Young Chul Youn; Hyun-Soo Choi; Chihyun Park; Hyeonseung Im; SangYun Kim

doi:10.3389/fneur.2022.906257

Predicting progression to dementia with "comprehensive visual rating scale" and machine learning algorithms

Front Neurol. 2022 Aug 22:13:906257. doi: 10.3389/fneur.2022.906257. eCollection 2022.

Authors

Chaeyoon Park¹, Jae-Won Jang^{1

2

3}, Gihun Joo³, Yeshin Kim², Seongheon Kim², Gihwan Byeon⁴, Sang Won Park³, Payam Hosseinzadeh Kasani³, Sujin Yum³, Jung-Min Pyun⁵, Young Ho Park^{6

7}, Jae-Sung Lim⁸, Young Chul Youn⁹, Hyun-Soo Choi^{3

10}, Chihyun Park^{3

10}, Hyeonseung Im^{1

3

10}, SangYun Kim^{6

7}

Affiliations

¹ Department of Convergence Security, Kangwon National University, Chuncheon, South Korea.
² Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea.
³ Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea.
⁴ Department of Psychiatry, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea.
⁵ Department of Neurology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, South Korea.
⁶ Department of Neurology, Seoul National University Bundang Hospital, Seongnam, South Korea.
⁷ Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea.
⁸ Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
⁹ Department of Neurology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea.
¹⁰ Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea.

Abstract

Background and objective: Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms.

Methods: We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated.

Results: Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701-0.711) were used than when clinical data and cortical thickness (accuracy 0.650-0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression.

Conclusions: Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.

Keywords: Alzheimer's Disease; brain MRI; machine learning; mild cognition impairment; visual rating scale.