Early detection of cognitive decline in mild cognitive impairment and Alzheimer's disease with a novel eye tracking test

Tadokoro, Koh; Yamashita, Toru; Fukui, Yoshio; Nomura, Emi; Ohta, Yasuyuki; Ueno, Setsuko; Nishina, Saya; Tsunoda, Keiichiro; Wakutani, Yosuke; Takao, Yoshiki; Miyoshi, Tetsuya; Higashi, Yasuto; Osakada, Yosuke; Matsumoto, Namiko; Kawahara, Yuko; Omote, Yoshio; Hishikawa, Nozomi; Morihara, Ryuta

doi:10.1016/j.jns.2021.117529

Cited by 26 publications

(37 citation statements)

References 20 publications

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“…Each layer's weights are created using a normal distribution. The resulting tensor using the Kaiming weight will have values sampled from N (−bound, bound), where bound is described in Equation (2).…”

Section: Weight Randomization and Classificationmentioning

confidence: 99%

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An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Odusami

Maskeliūnas

Damaševičius

2022

Sensors

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Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD.

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Section: Weight Randomization and Classificationmentioning

confidence: 99%

“…Memory loss and cognitive decline are the most common signs of AD, and are caused by damage to and death of nerve cells related to memory in the brain area [1]. Mild cognitive impairment (MCI) is a stage that occurs between normal and AD [2]. AD progresses gradually through the prodromal stage of MCI, and finally, to AD dementia.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Odusami

Maskeliūnas

Damaševičius

2022

Sensors

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“…Participants are asked to perform this 3-min image task displayed on a computer monitor with an audio explanation, the data recorded is used to analyze eye tracking scores and classify NC (Normal Control), MCI (Mild Cognitive Impairment), and AD subjects. As a result, it shows that eye tracking scores are correlative with mini-mental state examination (MMSE) scores and it is an effective and rapid method to detect early cognitive impairment ( Tadokoro et al, 2021 ). In another work by Oyama et al, the very same eye-tracking device is used to perform a rapid cognitive assessment and show good diagnostic performance ( Figure 2II ) ( Oyama et al, 2019 ).…”

Section: Applications Of Biosensors Toward Behavior Detection Of Alzh...mentioning

confidence: 99%

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

Sun

Sun²,

Wang³

et al. 2022

Front. Bioeng. Biotechnol.

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In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer’s disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.

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“…This would act as a supporting element in tracking the evolution of the sickness and, ultimately, assessing the severity as well as effectiveness of the therapeutic interventions. Eye tracking technology is extremely important as an earlier diagnosis of Alzheimer's disease would enable effective treatments, when available, to be administered before pathological changes to the brain can spread and be permanent (Wilcockson et al, 2019;Tadokoro et al, 2021).…”

Section: Eye-tracking Technologymentioning

confidence: 99%

A Review on the Techniques for Early Diagnosis of Alzheimer’s Disease

Shahzadi

Saeed

Khan

et al. 2022

LGUJLS

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Alzheimer’s disease is a neurological condition in which there is rapid deterioration of the brain and it affects around 50 million people globally. The most obvious sign of Alzheimer’s is dementia which is primarily an affliction of old age. Majority of the people presenting with dementia in old age are Alzheimer’s patients. The symptoms of Alzheimer’s disease are debilitating and have the ability to utterly disrupt a person's normal life. It is only discovered after this terrible disease has destroyed all neurons, thus there is little chance to cure it or reverse the adverse effects. There are two types of techniques for detecting Alzheimer's disease: invasive and non-invasive techniques. Invasive method obtains data from the patient bydrawing a small amount of blood or performing a lumbar puncture, whereas noninvasive method collects data using imaging techniques like MRI and CT scan. Invasive technique, on the other hand, is thought to be a more accurate indicator of Alzheimer's disease than non-invasive technique since it provides strong biomarkers. Once Alzheimer's disease has progressed to its final stage, it is incurable. Treatment is only viable when the disease is in its initial stages. Future treatments for Alzheimer's disease will focus on the causative maladies of neurofibrillary tangles (ptau) and senile plaques (A). The pathological traits connected to debilitating disease, special protein, b proteins, are critical for future therapeutics

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Early detection of cognitive decline in mild cognitive impairment and Alzheimer's disease with a novel eye tracking test

Cited by 26 publications

References 20 publications

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

A Review on the Techniques for Early Diagnosis of Alzheimer’s Disease

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