Article
Geriatrics & Gerontology
Caroline C. Claus, Salka S. Staekenborg, Kim H. W. Verweij, Jacqueline Schuur, Sieberen P. van der Werf, Philip Scheltens, Jules J. Claus
Summary: The clock drawing test (CDT) is an important additional screening tool to the Mini Mental State Examination (MMSE). An abnormal CDT with a normal MMSE is an indicator for cognitive impairment, while an abnormal CDT in combination with an abnormal MMSE can be considered as an indicator of disease progression. The CDT significantly contributes to discriminating patients with subjective cognitive impairment (SCI) from both mild cognitive impairment (MCI) and Alzheimer's disease (AD).
INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY
(2023)
Article
Geriatrics & Gerontology
Ayu Imai, Teruyuki Matsuoka, Yuka Kato, Jin Narumoto
Summary: This study aims to clarify the diagnostic performance and neural basis of the Clock Drawing Test (CDT) by combining free- and pre-drawn methods. The results show that the combination method has a higher diagnostic accuracy for Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) compared to the individual methods. Voxel-based morphometry analysis also reveals differences in gray matter volume between different groups. The combination method may have the potential to screen for a wider range of brain dysfunction and improve early detection and treatment of AD.
INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY
(2022)
Article
Clinical Neurology
C. Carnero-Pardo, I. Rego-Garcia, J. M. Barrios-Lopez, S. Blanco-Madera, R. Calle-Calle, S. Lopez-Alcalde, R. M. Vilchez-Carrillo
Summary: This study aims to analyze the diagnostic usefulness of the Mini-Cog and Clock Drawing Test (CDT) in detecting cognitive impairment. The Mini-Cog showed greater diagnostic usefulness than CDT, and both tests were less effective in individuals with a low education level.
Article
Clinical Neurology
Natthanan Ruengchaijatuporn, Itthi Chatnuntawech, Surat Teerapittayanon, Sira Sriswasdi, Sirawaj Itthipuripat, Solaphat Hemrungrojn, Prodpran Bunyabukkana, Aisawan Petchlorlian, Sedthapong Chunamchai, Thiparat Chotibut, Chaipat Chunharas
Summary: This research proposes a novel deep learning framework for detecting MCI by combining data from the CDT, cube-copying, and trail-making tests. Soft label and self-attention techniques are applied to improve model performance and provide visual explanations. The model achieves better classification performance and interpretability compared to the baseline model.
ALZHEIMERS RESEARCH & THERAPY
(2022)
Article
Behavioral Sciences
Feng-Feng Pan, Liang Cui, Qing-Jie Li, Qi-Hao Guo
Summary: A modified Chinese version of Mini-Addenbrooke's Cognitive Examination (C-MACE) was developed and showed good classification accuracy in detecting MCI. C-MACE performed best in identifying MCI in older individuals with lower education levels.
BRAIN AND BEHAVIOR
(2022)
Article
Health Care Sciences & Services
Jing Yuan, Rhoda Au, Cody Karjadi, Ting Fang Ang, Sherral Devine, Sanford Auerbach, Charles DeCarli, David J. Libon, Jesse Mez, Honghuang Lin
Summary: This study investigated the association between digital Clock Drawing Test (dCDT) features and brain volume in a large population-based cohort. The findings showed that dCDT composite scores were significantly associated with multiple brain MRI measures. The results suggest that dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of mild cognitive impairment (MCI).
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Chemistry, Multidisciplinary
Akhilesh Vyas, Fotis Aisopos, Maria-Esther Vidal, Peter Garrard, George Paliouras
Summary: The study examines various clinical attributes of dementia patients, using machine learning models to calibrate MMSE scores accurately determining cognitive status and provides an effective classification mechanism to identify inaccurate MMSE values.
APPLIED SCIENCES-BASEL
(2021)
Article
Clinical Neurology
Yuki Asahara, Masashi Kameyama, Kenji Ishii, Kenji Ishibashi
Summary: The study suggests that the diagnostic performance of the CIS ratio for differentiating DLB from AD changes depending on the MMSE score, with higher sensitivity and specificity at MMSE scores of 20-24.
JOURNAL OF THE NEUROLOGICAL SCIENCES
(2023)
Article
Neurosciences
Knut Engedal, Jurate Saltyte Benth, Linda Gjora, Havard Kjesbu Skjellegrind, Marit Navik, Geir Selbaek
Summary: This study examined normative scores for the third Norwegian version of the MMSE and found that age and years of education influenced MMSE scores, with education being the strongest predictor.
JOURNAL OF ALZHEIMERS DISEASE
(2023)
Review
Medicine, General & Internal
Ingrid Arevalo-Rodriguez, Nadja Smailagic, Marta Roque-Figuls, Agustin Ciapponi, Erick Sanchez-Perez, Antri Giannakou, Olga L. Pedraza, Xavier Bonfill Cosp, Sarah Cullum
Summary: The review included studies focusing on the conversion from MCI to all-cause dementia, Alzheimer's disease dementia, and vascular dementia. Baseline MMSE scores had a range of accuracy in predicting dementia, but alone cannot determine the direction of progression for MCI patients.
COCHRANE DATABASE OF SYSTEMATIC REVIEWS
(2021)
Article
Biophysics
Hye Jin Kim, Hongrae Kim, Dongsung Park, Dae Sung Yoon, Jin San Lee, Kyo Seon Hwang
Summary: Using a biosensor for Alzheimer's disease (AD) screening enables early and accurate detection with high sensitivity. This method overcomes the limitations of traditional AD diagnostic methods like neuropsychological assessment and neuroimaging analysis. By analyzing the combined signals of four crucial AD biomarkers (A beta 40, A beta 42, tTau441, and pTau181) with a dielectrophoretic force on an interdigitated microelectrode sensor, the biosensor selectively concentrates and filters AD biomarkers in plasma, achieving high sensitivity (limit of detection <100 fM) and selectivity (p < 0.0001) in detection. The complex combined signal (A beta 40 - A beta 42 + tTau441 - pTau181) successfully differentiates between AD patients and healthy subjects with high accuracy (78.85%) and precision (80.95%) (p < 0.0001).
BIOSENSORS & BIOELECTRONICS
(2023)
Article
Geriatrics & Gerontology
Feng-Feng Pan, Ying Wang, Lin Huang, Yue Huang, Qi-Hao Guo
Summary: The Chinese version of ACE-III-CV is a reliable and valid screening tool for detecting MCI, and the optimal cutoff scores are closely related with education level.
AGING & MENTAL HEALTH
(2022)
Article
Geriatrics & Gerontology
Kristen E. Kehl-Floberg, Timothy S. Marks, Dorothy F. Edwards, Gordon M. Giles
Summary: This study examined the reliability, sensitivity, and specificity, as well as construct validity of two free-drawn clock drawing test scales for subtle cognitive decline in community-dwelling older adults. The Clock Drawing Interpretation Scale (CDIS) showed better psychometric properties compared to the Rouleau System, making it a more useful screening instrument for cognitive change in this population. However, both scales should be interpreted in conjunction with a comprehensive cognitive battery.
FRONTIERS IN AGING NEUROSCIENCE
(2023)
Article
Geriatrics & Gerontology
Ya-Chen Lee, Shu-Chun Lee, En-Chi Chiu
Summary: The study demonstrated that the practice effect could be minimized when alternate forms of the MMSE-2 were used. The MMSE-2 had good to excellent test-retest reliability, except for three subtests: visual-constructional ability, registration, and recall.
Article
Health Care Sciences & Services
Jie Wang, Zhuo Wang, Ning Liu, Caiyan Liu, Chenhui Mao, Liling Dong, Jie Li, Xinying Huang, Dan Lei, Shanshan Chu, Jianyong Wang, Jing Gao
Summary: This study optimized a cognitive assessment model through machine learning, which can be used for the diagnosis of MCI and dementia in patients with normal MMSE. It not only optimizes the content of cognitive evaluation, but also improves diagnosis accuracy and reduces missed diagnosis.
JOURNAL OF PERSONALIZED MEDICINE
(2022)