Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms
Background and ObjectiveIdentifying 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 struct...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2022.906257/full |
| _version_ | 1811340720586358784 |
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| author | Chaeyoon Park Jae-Won Jang Jae-Won Jang 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 Young Ho Park Jae-Sung Lim Young Chul Youn Hyun-Soo Choi Hyun-Soo Choi Chihyun Park Chihyun Park Hyeonseung Im Hyeonseung Im Hyeonseung Im SangYun Kim SangYun Kim |
| author_facet | Chaeyoon Park Jae-Won Jang Jae-Won Jang 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 Young Ho Park Jae-Sung Lim Young Chul Youn Hyun-Soo Choi Hyun-Soo Choi Chihyun Park Chihyun Park Hyeonseung Im Hyeonseung Im Hyeonseung Im SangYun Kim SangYun Kim |
| author_sort | Chaeyoon Park |
| collection | DOAJ |
| description | Background and ObjectiveIdentifying 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.MethodsWe 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.ResultsOf 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.ConclusionsTree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data. |
| first_indexed | 2024-04-13T18:46:03Z |
| format | Article |
| id | doaj.art-43ec7d9be9fa4375b0122248262f1cca |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-13T18:46:03Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-43ec7d9be9fa4375b0122248262f1cca2022-12-22T02:34:35ZengFrontiers Media S.A.Frontiers in Neurology1664-22952022-08-011310.3389/fneur.2022.906257906257Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithmsChaeyoon Park0Jae-Won Jang1Jae-Won Jang2Jae-Won Jang3Gihun Joo4Yeshin Kim5Seongheon Kim6Gihwan Byeon7Sang Won Park8Payam Hosseinzadeh Kasani9Sujin Yum10Jung-Min Pyun11Young Ho Park12Young Ho Park13Jae-Sung Lim14Young Chul Youn15Hyun-Soo Choi16Hyun-Soo Choi17Chihyun Park18Chihyun Park19Hyeonseung Im20Hyeonseung Im21Hyeonseung Im22SangYun Kim23SangYun Kim24Department of Convergence Security, Kangwon National University, Chuncheon, South KoreaDepartment of Convergence Security, Kangwon National University, Chuncheon, South KoreaDepartment of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South KoreaInterdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South KoreaInterdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South KoreaDepartment of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South KoreaDepartment of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South KoreaDepartment of Psychiatry, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South KoreaInterdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South KoreaInterdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South KoreaInterdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South KoreaDepartment of Neurology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, South KoreaDepartment of Neurology, Seoul National University Bundang Hospital, Seongnam, South KoreaDepartment of Neurology, Seoul National University College of Medicine, Seoul, South KoreaDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South KoreaDepartment of Neurology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South KoreaInterdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea0Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South KoreaInterdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea0Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South KoreaDepartment of Convergence Security, Kangwon National University, Chuncheon, South KoreaInterdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea0Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South KoreaDepartment of Neurology, Seoul National University Bundang Hospital, Seongnam, South KoreaDepartment of Neurology, Seoul National University College of Medicine, Seoul, South KoreaBackground and ObjectiveIdentifying 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.MethodsWe 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.ResultsOf 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.ConclusionsTree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.https://www.frontiersin.org/articles/10.3389/fneur.2022.906257/fullmild cognition impairmentAlzheimer's Diseasebrain MRImachine learningvisual rating scale |
| spellingShingle | Chaeyoon Park Jae-Won Jang Jae-Won Jang 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 Young Ho Park Jae-Sung Lim Young Chul Youn Hyun-Soo Choi Hyun-Soo Choi Chihyun Park Chihyun Park Hyeonseung Im Hyeonseung Im Hyeonseung Im SangYun Kim SangYun Kim Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms Frontiers in Neurology mild cognition impairment Alzheimer's Disease brain MRI machine learning visual rating scale |
| title | Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms |
| title_full | Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms |
| title_fullStr | Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms |
| title_full_unstemmed | Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms |
| title_short | Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms |
| title_sort | predicting progression to dementia with comprehensive visual rating scale and machine learning algorithms |
| topic | mild cognition impairment Alzheimer's Disease brain MRI machine learning visual rating scale |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2022.906257/full |
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