Machine Learning for the Preliminary Diagnosis of Dementia

Zhu, Fubao; Li, Xiaonan; Tang, Haipeng; He, Zhuo; Zhang, Chaoyang; Hung, Guang-Uei; Chiu, Pai-Yi; Zhou, Weihua

doi:10.1155/2020/5629090

Cited by 39 publications

(31 citation statements)

References 20 publications

(25 reference statements)

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“…Zhu et al . analyzed a Taiwan cohort and ranked the relative importance of neuropsychological tests using Information Gain, Random Forest, and the Relief algorithm [18]. They classified normal, MCI, VMD, and dementia.…”

Section: Discussionmentioning

confidence: 99%

“…A large array of neurocognitive tests are currently used to detect cognitive impairment and classify amongst normal controls (CN), EMCI, LMCI and AD [7][8]. Many studies have identified a few top classifiers using logistic regression and machine learning methods [9][10][11][12][13][14][15][16][17][18]. Some studies have also used MRI and genetic data in conjunction with neurocognitive measures for classification [19][20].…”

Section: Introductionmentioning

confidence: 99%

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Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning

Nagaraj

Duong

2020

Preprint

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A large battery of neurocognitive tests are used to detect cognitive impairment and classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s Disease (AD) from cognitive normal (CN). The goal of this study was to develop a deep learning algorithm to identify a few top neurocognitive tests that can accurately classify these four groups. We also derived a simplified risk-stratification score model for classification. Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from 383 EMCI, 644 LMCI, 394 AD patients, and 516 cognitive normal from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A neural network algorithm was trained on data split 90% for training and 10% testing. We evaluated five different feature selection methods. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis. The five different feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes (CDRSB), Delayed total recall (LDELTOTAL), Modified Preclinical Alzheimer Cognitive Composite with Trails test (mPACCtrailsB), the Modified Preclinical Alzheimer Cognitive Composite with Digit test (mPACCdigit), and Mini-Mental State Examination (MMSE). The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.962 on the test dataset. The deep-learning algorithm and simplified risk score derived from our deep-learning algorithm accurately classifies EMCI, LMCI, AD and CN patients using a few commonly available neurocognitive tests.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning

Nagaraj

Duong

2020

Preprint

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“…This study is a retrospective analysis of the dementia registry database from the Show Chwan Healthcare System, currently applied in three centers in Taiwan (two in central Taiwan and one in southern Taiwan). In the database, the detailed clinical history of each participant was recorded using a structured questionnaire called the History-Based Artificial Intelligent Clinical Dementia Diagnostic System (HAICDDS), which has been well-validated (Lin et al, 2018 ; Chiu et al, 2019a , b , 2020 ; Tsai and Chiu, 2019 ; Chang et al, 2020 ; Wang et al, 2020 ; Zhu et al, 2020a , b ; Huang et al, 2021 ). In addition, CDR was used for staging dementia, and the daily function was assessed using the Instrumental Activities of Daily Living (IADL) Scale (Lawton and Brody, 1969 ) and Barthel Index (BI) (Mahoney and Barthel, 1965 ).…”

Section: Methodsmentioning

confidence: 99%

“…In our recent experience and studies using artificial intelligence (AI) for the diagnosis of cognitive impairment (CI) and dementia, the CDR and CDR-SB have become perfect references for machine learning in our newly designed questionnaires (Chiu et al, 2019a ; Chang et al, 2020 ; Zhu et al, 2020a , b ). Therefore, we highly recommend CDR as a further AI study.…”

Section: Introductionmentioning

confidence: 99%

Operational Determination of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using Sum of Boxes of the Clinical Dementia Rating Scale

Yang

Hsu

Wei

et al. 2021

Front. Aging Neurosci.

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Objectives: The Clinical Dementia Rating (CDR) Scale is the gold standard for the staging of dementia due to Alzheimer's disease (AD). However, the application of CDR for the staging of subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in AD remains controversial. This study aimed to use the sum of boxes of the CDR (CDR-SB) plus an SCD single questionnaire to operationally determine the different stages of cognitive impairment (CI) due to AD and non-AD.Methods: This was a two-phase study, and we retrospectively analyzed the Show Chwan Dementia registry database using the data selected from 2015 to 2020. Individuals with normal cognition (NC), SCD, MCI, and mild dementia (MD) due to AD or non-AD with a CDR < 2 were included in the analysis.Results: A total of 6,946 individuals were studied, including 875, 1,009, 1,585, and 3,447 with NC, SCD, MCI, and MD, respectively. The cutoff scores of CDR-SB for NC/SCD, SCD/MCI, and MCI/dementia were 0/0.5, 0.5/1.0, and 2.5/3.0, respectively. The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) values of the test groups were 0.85, 0.90, and 0.92 for discriminating NC from SCD, SCD from MCI, and MCI from dementia, respectively. Compared with the Cognitive Abilities Screening Instrument or the Montreal Cognitive Assessment, the use of CDR-SB is less influenced by age and education.Conclusion: Our study showed that the operational determination of SCD, MCI, and dementia using the CDR-SB is practical and can be applied in clinical settings and research on CI or dementia.

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“…Machine learning and deep learning are commonly used in other fields. Such as in [29], machine learning algorithms are used for the preliminary diagnosis of Dementia. In [30], target recognition is made in SAR images based on multiresolution representations with 2D canonical correlation analysis.…”

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confidence: 99%

A Novel Deep Convolutional Neural Network Model to Monitor People following Guidelines to Avoid COVID-19

2020

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COVID-19, a deadly disease that originated in Wuhan, China, has resulted in a global outbreak. Patients infected with the causative virus SARS-CoV-2 are placed in quarantine, so the virus does not spread. The medical community has not discovered any vaccine that can be immediately used on patients infected with SARS-CoV-2. The only method discovered so far to protect people from this virus is keeping a distance from other people, wearing masks and gloves, as well as regularly washing and sanitizing hands. Government and law enforcement agencies are involved in banning the movement of people in different cities, to control the spread and monitor people following the guidelines of the CDC. But it is not possible for the government to monitor all places, such as shopping malls, hospitals, government offices, and banks, and guide people to follow the safety guidelines. In this paper, a novel technique is developed that can guide people to protect themselves from someone who has high exposure to the virus or has symptoms of COVID-19, such as having fever and coughing. Different deep Convolutional Neural Networks (CNN) models are implemented to test the proposed technique. The proposed intelligent monitoring system can be used as a complementary tool to be installed at different places and automatically monitor people adopting the safety guidelines. With these precautionary measurements, humans will be able to win this fight against COVID-19.

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Machine Learning for the Preliminary Diagnosis of Dementia

Cited by 39 publications

References 20 publications

Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning

Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning

Operational Determination of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using Sum of Boxes of the Clinical Dementia Rating Scale

A Novel Deep Convolutional Neural Network Model to Monitor People following Guidelines to Avoid COVID-19

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