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Miles G, Smith M, Zook N, Zhang W. EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning. Comput Struct Biotechnol J 2024; 24:264-280. [PMID: 38638116 PMCID: PMC11024913 DOI: 10.1016/j.csbj.2024.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 04/20/2024] Open
Abstract
Alzheimer's Disease is the most prevalent neurodegenerative disease, and is a leading cause of disability among the elderly. Eye movement behaviour demonstrates potential as a non-invasive biomarker for Alzheimer's Disease, with changes detectable at an early stage after initial onset. This paper introduces a new publicly available dataset: EM-COGLOAD (available at https://osf.io/zjtdq/, DOI: 10.17605/OSF.IO/ZJTDQ). A dual-task paradigm was used to create effects of declined cognitive performance in 75 healthy adults as they carried out visual tracking tasks. Their eye movement was recorded, and time series classification of the extracted eye movement traces was explored using a range of deep learning techniques. The results of this showed that convolutional neural networks were able to achieve an accuracy of 87.5% when distinguishing between eye movement under low and high cognitive load, and 76% when distinguishing between the oldest and youngest age groups.
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Affiliation(s)
- Gabriella Miles
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T Block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Melvyn Smith
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T Block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Nancy Zook
- Faculty of Health and Applied Sciences, University of the West of England, Bristol BS16 1QY, UK
| | - Wenhao Zhang
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T Block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
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Gaire BP, Koronyo Y, Fuchs DT, Shi H, Rentsendorj A, Danziger R, Vit JP, Mirzaei N, Doustar J, Sheyn J, Hampel H, Vergallo A, Davis MR, Jallow O, Baldacci F, Verdooner SR, Barron E, Mirzaei M, Gupta VK, Graham SL, Tayebi M, Carare RO, Sadun AA, Miller CA, Dumitrascu OM, Lahiri S, Gao L, Black KL, Koronyo-Hamaoui M. Alzheimer's disease pathophysiology in the Retina. Prog Retin Eye Res 2024; 101:101273. [PMID: 38759947 DOI: 10.1016/j.preteyeres.2024.101273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 04/23/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024]
Abstract
The retina is an emerging CNS target for potential noninvasive diagnosis and tracking of Alzheimer's disease (AD). Studies have identified the pathological hallmarks of AD, including amyloid β-protein (Aβ) deposits and abnormal tau protein isoforms, in the retinas of AD patients and animal models. Moreover, structural and functional vascular abnormalities such as reduced blood flow, vascular Aβ deposition, and blood-retinal barrier damage, along with inflammation and neurodegeneration, have been described in retinas of patients with mild cognitive impairment and AD dementia. Histological, biochemical, and clinical studies have demonstrated that the nature and severity of AD pathologies in the retina and brain correspond. Proteomics analysis revealed a similar pattern of dysregulated proteins and biological pathways in the retina and brain of AD patients, with enhanced inflammatory and neurodegenerative processes, impaired oxidative-phosphorylation, and mitochondrial dysfunction. Notably, investigational imaging technologies can now detect AD-specific amyloid deposits, as well as vasculopathy and neurodegeneration in the retina of living AD patients, suggesting alterations at different disease stages and links to brain pathology. Current and exploratory ophthalmic imaging modalities, such as optical coherence tomography (OCT), OCT-angiography, confocal scanning laser ophthalmoscopy, and hyperspectral imaging, may offer promise in the clinical assessment of AD. However, further research is needed to deepen our understanding of AD's impact on the retina and its progression. To advance this field, future studies require replication in larger and diverse cohorts with confirmed AD biomarkers and standardized retinal imaging techniques. This will validate potential retinal biomarkers for AD, aiding in early screening and monitoring.
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Affiliation(s)
- Bhakta Prasad Gaire
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dieu-Trang Fuchs
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Haoshen Shi
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Altan Rentsendorj
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ron Danziger
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jean-Philippe Vit
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nazanin Mirzaei
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jonah Doustar
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Julia Sheyn
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harald Hampel
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Andrea Vergallo
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Miyah R Davis
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ousman Jallow
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Filippo Baldacci
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France; Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | | | - Ernesto Barron
- Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA; Doheny Eye Institute, Los Angeles, CA, USA
| | - Mehdi Mirzaei
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Vivek K Gupta
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Stuart L Graham
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia; Department of Clinical Medicine, Macquarie University, Sydney, NSW, Australia
| | - Mourad Tayebi
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia
| | - Roxana O Carare
- Department of Clinical Neuroanatomy, University of Southampton, Southampton, UK
| | - Alfredo A Sadun
- Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA; Doheny Eye Institute, Los Angeles, CA, USA
| | - Carol A Miller
- Department of Pathology Program in Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Shouri Lahiri
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Liang Gao
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Keith L Black
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences, Division of Applied Cell Biology and Physiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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El-Assy AM, Amer HM, Ibrahim HM, Mohamed MA. A novel CNN architecture for accurate early detection and classification of Alzheimer's disease using MRI data. Sci Rep 2024; 14:3463. [PMID: 38342924 PMCID: PMC10859371 DOI: 10.1038/s41598-024-53733-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/04/2024] [Indexed: 02/13/2024] Open
Abstract
Alzheimer's disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to categorize AD. The network employs two separate CNN models, each with distinct filter sizes and pooling layers, which are concatenated in a classification layer. The multi-class problem is addressed across three, four, and five categories. The proposed CNN architecture achieves exceptional accuracies of 99.43%, 99.57%, and 99.13%, respectively. These high accuracies demonstrate the efficacy of the network in capturing and discerning relevant features from MRI images, enabling precise classification of AD subtypes and stages. The network architecture leverages the hierarchical nature of convolutional layers, pooling layers, and fully connected layers to extract both local and global patterns from the data, facilitating accurate discrimination between different AD categories. Accurate classification of AD carries significant clinical implications, including early detection, personalized treatment planning, disease monitoring, and prognostic assessment. The reported accuracy underscores the potential of the proposed CNN architecture to assist medical professionals and researchers in making precise and informed judgments regarding AD patients.
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Affiliation(s)
- A M El-Assy
- Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
| | - Hanan M Amer
- Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - H M Ibrahim
- Communication and Electronics Engineering Department, Nile Higher Institute for Engineering and Technology-IEEE Com Society Member, Mansoura, Egypt
| | - M A Mohamed
- Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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Lin J, Xu T, Yang X, Yang Q, Zhu Y, Wan M, Xiao X, Zhang S, Ouyang Z, Fan X, Sun W, Yang F, Yuan L, Bei Y, Wang J, Guo J, Tang B, Shen L, Jiao B. A detection model of cognitive impairment via the integrated gait and eye movement analysis from a large Chinese community cohort. Alzheimers Dement 2024; 20:1089-1101. [PMID: 37876113 PMCID: PMC10916936 DOI: 10.1002/alz.13517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/26/2023]
Abstract
INTRODUCTION Whether the integration of eye-tracking, gait, and corresponding dual-task analysis can distinguish cognitive impairment (CI) patients from controls remains unclear. METHODS One thousand four hundred eighty-one participants, including 724 CI and 757 controls, were enrolled in this study. Eye movement and gait, combined with dual-task patterns, were measured. The LightGBM machine learning models were constructed. RESULTS A total of 105 gait and eye-tracking features were extracted. Forty-six parameters, including 32 gait and 14 eye-tracking features, showed significant differences between two groups (P < 0.05). Of these, the Gait_3Back-TurnTime and Dual-task cost-TurnTime patterns were significantly correlated with plasma phosphorylated tau 181 (p-tau181) level. A model based on dual-task gait, dual-task smooth pursuit, prosaccade, and anti-saccade achieved the best area under the receiver operating characteristics curve (AUC) of 0.987 for CI detection, while combined with p-tau181, the model discriminated mild cognitive impairment from controls with an AUC of 0.824. DISCUSSION Combining dual-task gait and dual-task eye-tracking analysis is feasible for the detection of CI. HIGHLIGHTS This is the first study to report the efficiency of integrated parameters of dual-task gait and eye-tracking for cognitive impairment (CI) detection in a large cohort. We identified 46 gait and eye-tracking features associated with CI, and two were correlated to plasma phosphorylated tau 181. We constructed the model based on dual-task gait, smooth pursuit, prosaccade, and anti-saccade, achieving the best area under the curve of 0.987 for CI detection.
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Affiliation(s)
- Jingyi Lin
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalCentral South UniversityChangshaChina
- Department of BiologyEmory UniversityAtlantaGeorgiaUSA
| | - Tianyan Xu
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Xuan Yang
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Qijie Yang
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Yuan Zhu
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Meidan Wan
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Xuewen Xiao
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalCentral South UniversityChangshaChina
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaChina
- Engineering Research Center of Hunan Province in Cognitive Impairment DisordersCentral South UniversityChangshaChina
| | - Sizhe Zhang
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Ziyu Ouyang
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
| | - Xiangmin Fan
- Institute of SoftwareChinese Academy of SciencesBeijingChina
| | - Wei Sun
- Institute of SoftwareChinese Academy of SciencesBeijingChina
| | - Fan Yang
- Institute of SoftwareChinese Academy of SciencesBeijingChina
- School of Computer Science and TechnologyUniversity of Chinese Academy of SciencesBeijingChina
| | - Li Yuan
- Department of NeurologyLiuyang Jili HospitalChangshaChina
| | - Yuzhang Bei
- Department of NeurologyLiuyang Jili HospitalChangshaChina
| | - Junling Wang
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalCentral South UniversityChangshaChina
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaChina
- Engineering Research Center of Hunan Province in Cognitive Impairment DisordersCentral South UniversityChangshaChina
| | - Jifeng Guo
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalCentral South UniversityChangshaChina
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaChina
- Engineering Research Center of Hunan Province in Cognitive Impairment DisordersCentral South UniversityChangshaChina
| | - Beisha Tang
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalCentral South UniversityChangshaChina
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaChina
- Engineering Research Center of Hunan Province in Cognitive Impairment DisordersCentral South UniversityChangshaChina
| | - Lu Shen
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalCentral South UniversityChangshaChina
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaChina
- Engineering Research Center of Hunan Province in Cognitive Impairment DisordersCentral South UniversityChangshaChina
| | - Bin Jiao
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesXiangya HospitalCentral South UniversityChangshaChina
- Department of NeurologyXiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaChina
- Engineering Research Center of Hunan Province in Cognitive Impairment DisordersCentral South UniversityChangshaChina
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Kim SY, Park J, Choi H, Loeser M, Ryu H, Seo K. Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study. J Med Internet Res 2023; 25:e48093. [PMID: 37862101 PMCID: PMC10625097 DOI: 10.2196/48093] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/07/2023] [Accepted: 09/22/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a "virtual kiosk test" for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment. OBJECTIVE We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features. METHODS A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test-developed by our group-where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls. RESULTS In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=-3.44; P=.004), and a greater number of errors (t49=-3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score. CONCLUSIONS Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.
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Affiliation(s)
- Se Young Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Jinseok Park
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Hojin Choi
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Martin Loeser
- Department of Computer Science, Electrical Engineering and Mechatronics, ZHAW Zurich University of Applied Sciences, Winterthur, Switzerland
| | - Hokyoung Ryu
- Graduate School of Technology and Innovation Management, Hanyang University, Seoul, Republic of Korea
| | - Kyoungwon Seo
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
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Mohamed AA, Marques O. Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline. Cureus 2023; 15:e47004. [PMID: 37965412 PMCID: PMC10641267 DOI: 10.7759/cureus.47004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Cognitive impairment is an age-associated disorder of increasing prevalence as the aging population continues to grow. Classified based on the level of cognitive decline, memory, function, and capacity to conduct activities of daily living, cognitive impairment ranges from mild cognitive impairment to dementia. When considering the insidious nature of the etiologies responsible for varying degrees of cognitive impairment, early diagnosis may provide a clinical benefit through the facilitation of early treatment. Typical diagnosis relies heavily on evaluation in a primary care setting. However, there is evidence that other diagnostic tools may aid in an earlier diagnosis of the different underlying pathologies responsible for cognitive impairment. Artificial intelligence represents a new intersecting field with healthcare that may aid in the early detection of neurodegenerative disorders. When assessing the role of AI in detecting cognitive decline, it is important to consider both the diagnostic efficacy of AI algorithms and the clinical relevance and impact of early interventions as a result of early detection. Thus, this review highlights promising investigations and developments in the space of artificial intelligence and healthcare and their potential to impact patient outcomes.
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Affiliation(s)
- Ali A Mohamed
- Neurological Surgery, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA
- Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA
| | - Oge Marques
- Biomedical Sciences, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA
- Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA
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Calvo Córdoba A, García Cena CE, Montoliu C. Automatic Video-Oculography System for Detection of Minimal Hepatic Encephalopathy Using Machine Learning Tools. SENSORS (BASEL, SWITZERLAND) 2023; 23:8073. [PMID: 37836903 PMCID: PMC10575013 DOI: 10.3390/s23198073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023]
Abstract
This article presents an automatic gaze-tracker system to assist in the detection of minimal hepatic encephalopathy by analyzing eye movements with machine learning tools. To record eye movements, we used video-oculography technology and developed automatic feature-extraction software as well as a machine learning algorithm to assist clinicians in the diagnosis. In order to validate the procedure, we selected a sample (n=47) of cirrhotic patients. Approximately half of them were diagnosed with minimal hepatic encephalopathy (MHE), a common neurological impairment in patients with liver disease. By using the actual gold standard, the Psychometric Hepatic Encephalopathy Score battery, PHES, patients were classified into two groups: cirrhotic patients with MHE and those without MHE. Eye movement tests were carried out on all participants. Using classical statistical concepts, we analyzed the significance of 150 eye movement features, and the most relevant (p-values ≤ 0.05) were selected for training machine learning algorithms. To summarize, while the PHES battery is a time-consuming exploration (between 25-40 min per patient), requiring expert training and not amenable to longitudinal analysis, the automatic video oculography is a simple test that takes between 7 and 10 min per patient and has a sensitivity and a specificity of 93%.
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Affiliation(s)
- Alberto Calvo Córdoba
- Escuela Técnica Superior de Ingenieros Industriales, Center for Automation and Robotics, UPM-CSIC, Universidad Politécnica de Madrid, José Gutiérrez Abascal St., 2, 28006 Madrid, Spain
| | - Cecilia E. García Cena
- Escuela Técnica Superior de Ingeniería y Diseño Industrial, Center for Automation and Robotics, UPM-CSIC, Universidad Politécnica de Madrid, Ronda de Valencia, 3, 28012 Madrid, Spain;
| | - Carmina Montoliu
- Instituto de Investigación Sanitaria-INCLIVA, 46010 Valencia, Spain;
- Servicio de Medicina Digestiva, Hospital Clínico de Valencia, 46010 Valencia, Spain
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Chandrasekharan J, Joseph A, Ram A, Nollo G. ETMT: A Tool for Eye-Tracking-Based Trail-Making Test to Detect Cognitive Impairment. SENSORS (BASEL, SWITZERLAND) 2023; 23:6848. [PMID: 37571630 PMCID: PMC10422410 DOI: 10.3390/s23156848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/19/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023]
Abstract
The growing number of people with cognitive impairment will significantly increase healthcare demand. Screening tools are crucial for detecting cognitive impairment due to a shortage of mental health experts aiming to improve the quality of life for those living with this condition. Eye tracking is a powerful tool that can provide deeper insights into human behavior and inner cognitive processes. The proposed Eye-Tracking-Based Trail-Making Test, ETMT, is a screening tool for monitoring a person's cognitive function. The proposed system utilizes a fuzzy-inference system as an integral part of its framework to calculate comprehensive scores assessing visual search speed and focused attention. By employing an adaptive neuro-fuzzy-inference system, the tool provides an overall cognitive-impairment score, allowing psychologists to assess and quantify the extent of cognitive decline or impairment in their patients. The ETMT model offers a comprehensive understanding of cognitive abilities and identifies potential deficits in various domains. The results indicate that the ETMT model is a potential tool for evaluating cognitive impairment and can capture significant changes in eye movement behavior associated with cognitive impairment. It provides a convenient and affordable diagnosis, prioritizing healthcare resources for severe conditions while enhancing feedback to practitioners.
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Affiliation(s)
- Jyotsna Chandrasekharan
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India;
- Department of Industrial Engineering, University of Trento, 38123 Trento, Italy;
| | - Amudha Joseph
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India;
| | | | - Giandomenico Nollo
- Department of Industrial Engineering, University of Trento, 38123 Trento, Italy;
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Wolf A, Tripanpitak K, Umeda S, Otake-Matsuura M. Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review. Front Psychol 2023; 14:1197567. [PMID: 37546488 PMCID: PMC10399700 DOI: 10.3389/fpsyg.2023.1197567] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/19/2023] [Indexed: 08/08/2023] Open
Abstract
Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.
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Affiliation(s)
- Alexandra Wolf
- Cognitive Behavioral Assistive Technology (CBAT), Goal-Oriented Technology Group, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kornkanok Tripanpitak
- Cognitive Behavioral Assistive Technology (CBAT), Goal-Oriented Technology Group, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
| | - Satoshi Umeda
- Department of Psychology, Keio University, Tokyo, Japan
| | - Mihoko Otake-Matsuura
- Cognitive Behavioral Assistive Technology (CBAT), Goal-Oriented Technology Group, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
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10
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Ionescu A, Ştefănescu E, Strilciuc Ş, Grad DA, Mureşanu D. Eyes on dementia: an overview of the interplay between eye movements and cognitive decline. J Med Life 2023; 16:642-662. [PMID: 37520470 PMCID: PMC10375353 DOI: 10.25122/jml-2023-0217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 04/30/2023] [Indexed: 08/01/2023] Open
Abstract
The economic and disease burden of dementia is forecasted to continue increasing. Considering its cognitive effects, timely diagnosis is important in developing a stage-based treatment plan and gathering data to support advocacy efforts and plan healthcare and social services. Eye-tracking technology has emerged as an efficient diagnostic tool in clinical practice and experimental studies. This review aimed to comprehensively analyze various aspects of eye-tracking technology, including pupillometry parameters, eye movements, eye-tracking devices, and neuropsychological tools. We conducted a systematic review retrieving articles published in the last ten years from six databases. Our results provide a complex overview for each included form of dementia/cognitive decline in terms of patient characteristics (age, sex-disaggregated by included pathologies), inclusion and exclusion criteria, devices, and neuropsychological tools. We also summarized findings on fixation stability tasks, saccadic evaluation, pupillometry, scene perception, object recognition, spatial memory, eye-tracking video tasks, and visual search. The eye-tracking method has become more common in cognitive assessments.
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Affiliation(s)
- Alec Ionescu
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Emanuel Ştefănescu
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Ştefan Strilciuc
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Diana Alecsandra Grad
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
- Department of Public Health, Faculty of Political, Administrative and Communication Sciences, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Dafin Mureşanu
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
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11
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Vrahatis AG, Skolariki K, Krokidis MG, Lazaros K, Exarchos TP, Vlamos P. Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094184. [PMID: 37177386 PMCID: PMC10180573 DOI: 10.3390/s23094184] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023]
Abstract
Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.
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Affiliation(s)
- Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Konstantina Skolariki
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Marios G Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Konstantinos Lazaros
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
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12
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Wang Y, Chen T, Wang C, Ogihara A, Ma X, Huang S, Zhou S, Li S, Liu J, Li K. A New Smart 2-Min Mobile Alerting Method for Mild Cognitive Impairment Due to Alzheimer's Disease in the Community. Brain Sci 2023; 13:brainsci13020244. [PMID: 36831787 PMCID: PMC9954272 DOI: 10.3390/brainsci13020244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/29/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
The early identification of mild cognitive impairment (MCI) due to Alzheimer's disease (AD), in an early stage of AD can expand the AD warning window. We propose a new capability index evaluating the spatial execution process (SEP), which can dynamically evaluate the execution process in the space navigation task. The hypothesis is proposed that there are neurobehavioral differences between normal cognitive (NC) elderly and AD patients with MCI reflected in digital biomarkers captured during SEP. According to this, we designed a new smart 2-min mobile alerting method for MCI due to AD, for community screening. Two digital biomarkers, total mission execution distance (METRtotal) and execution distance above the transverse obstacle (EDabove), were selected by step-up regression analysis. For the participants with more than 9 years of education, the alerting efficiency of the combination of the two digital biomarkers for MCI due to AD could reach 0.83. This method has the advantages of fast speed, high alerting efficiency, low cost and high intelligence and thus has a high application value for community screening in developing countries. It also provides a new intelligent alerting approach based on the human-computer interaction (HCI) paradigm for MCI due to AD in community screening.
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Affiliation(s)
- Yujia Wang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Tong Chen
- Department of Neurology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China
| | - Chen Wang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Atsushi Ogihara
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Department of Health Sciences and Social Welfare, Faculty of Human Sciences, Waseda University, Tokorozawa 359-1162, Japan
| | - Xiaowen Ma
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Shouqiang Huang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Siyu Zhou
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
- School of Public Health, Hangzhou Normal University, Hangzhou 311121, China
| | - Shuwu Li
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Jiakang Liu
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Kai Li
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Correspondence:
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13
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Sun X, Sun X, Wang Q, Wang X, Feng L, Yang Y, Jing Y, Yang C, Zhang S. Biosensors toward behavior detection in diagnosis of alzheimer’s disease. Front Bioeng Biotechnol 2022; 10:1031833. [PMID: 36338126 PMCID: PMC9626796 DOI: 10.3389/fbioe.2022.1031833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/03/2022] [Indexed: 11/30/2022] Open
Abstract
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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Affiliation(s)
- Xiaotong Sun
- Ningbo Innovation Center, School of Mechanical Engineering, Zhejiang University, Ningbo, China
- Faculty of Science and Engineering, University of Nottingham Ningbo, Ningbo, China
| | - Xu Sun
- Faculty of Science and Engineering, University of Nottingham Ningbo, Ningbo, China
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo, Ningbo, China
- *Correspondence: Sheng Zhang, ; Xu Sun,
| | - Qingfeng Wang
- Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo, Zhejiang, China
| | - Xiang Wang
- Ningbo Innovation Center, School of Mechanical Engineering, Zhejiang University, Ningbo, China
- Faculty of Science and Engineering, University of Nottingham Ningbo, Ningbo, China
| | - Luying Feng
- Ningbo Innovation Center, School of Mechanical Engineering, Zhejiang University, Ningbo, China
| | - Yifan Yang
- Ningbo Innovation Center, School of Mechanical Engineering, Zhejiang University, Ningbo, China
- Faculty of Science and Engineering, University of Nottingham Ningbo, Ningbo, China
| | - Ying Jing
- Business School, NingboTech University, Ningbo, China
| | - Canjun Yang
- Ningbo Innovation Center, School of Mechanical Engineering, Zhejiang University, Ningbo, China
| | - Sheng Zhang
- Ningbo Innovation Center, School of Mechanical Engineering, Zhejiang University, Ningbo, China
- Faculty of Science and Engineering, University of Nottingham Ningbo, Ningbo, China
- *Correspondence: Sheng Zhang, ; Xu Sun,
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14
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Sun J, Liu Y, Wu H, Jing P, Ji Y. A novel deep learning approach for diagnosing Alzheimer's disease based on eye-tracking data. Front Hum Neurosci 2022; 16:972773. [PMID: 36158627 PMCID: PMC9500464 DOI: 10.3389/fnhum.2022.972773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks.
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Affiliation(s)
- Jinglin Sun
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Yu Liu
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Hao Wu
- Tianjin Key Laboratory of Cerebrovascular and Neurodegenerative Diseases, Department of Neurology, Tianjin Dementia Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Peiguang Jing
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
- *Correspondence: Peiguang Jing
| | - Yong Ji
- Tianjin Key Laboratory of Cerebrovascular and Neurodegenerative Diseases, Department of Neurology, Tianjin Dementia Institute, Tianjin Huanhu Hospital, Tianjin, China
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15
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WANG XIAOWEI, ZHOU LUBIAO, WANG JINKE, TAMURA SHINICHI. NATURAL SEMANTIC INFORMATION-BASED EYE TRACKING USING IMPROVED NEEDLEMAN–WUNSCH AND SUBSMATCH METHODS. J MECH MED BIOL 2022. [DOI: 10.1142/s0219519422400140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper proposed an improved natural semantic information-based eye-tracking method combing Needleman–Wunsch and SubsMatch technologies in psychological assessments. The natural semantic information of the self-assessment scale in psychological measurement was combined with the participants’ eye-tracking data, and a time-space similarity method was proposed to calculate the eye-tracking differences between different participants in the questionnaire response and carry out visual pattern recognition to achieve the screening of the target population in psychological measurement. The proposed method was evaluated by screening a sample at high risk of depression. The comparative results showed that the average screening accuracy of the sample under the stimulation of a single topic was 80.13% and increased to 97.37% after a dimensionality reduction. This paper verified the objectivity and effectiveness of the semantic information-based eye-tracking time-space similarity method and proved it to be a promising objective tool to assist psychological measurement.
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Affiliation(s)
- XIAOWEI WANG
- Rongcheng College, Harbin University of Science and Technology, Rongcheng 264300, P. R. China
| | - LUBIAO ZHOU
- School of Automation, Harbin University of Science and Technology, Harbin 150080, P. R. China
| | - JINKE WANG
- Rongcheng College, Harbin University of Science and Technology, Rongcheng 264300, P. R. China
| | - SHINICHI TAMURA
- Center for Advanced Medical Engineering and Informatics, Osaka University, Suita, Japan
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16
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Odusami M, Maskeliūnas R, Damaševičius R. An Intelligent System for Early Recognition of Alzheimer's Disease Using Neuroimaging. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22030740. [PMID: 35161486 PMCID: PMC8839926 DOI: 10.3390/s22030740] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 05/08/2023]
Abstract
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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Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
- Correspondence:
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17
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Abstract
PURPOSE OF REVIEW Dementia is a term for loss of memory, language, problem-solving, and other thinking abilities, which significantly interferes with daily life. Certain dementing conditions may also affect visual function. The eye is an accessible window to the brain that can provide valuable information for the early diagnosis of people who suffer from Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies as well as from more rare causes of dementias, such as Creutzfeldt-Jacob and Huntington's diseases. Herein, we present the ocular manifestations of neurocognitive disorders focusing on the neuro-ophthalmic ones and further discuss potential ocular biomarkers that could help in early detection of these disorders. RECENT FINDINGS Ophthalmic examination along with the recent developments in in-vivo testing have provided a strong foundation of useful knowledge about brain disorder in neurodegenerative diseases without the need for invasive studies. Currently, a number of visual measures, such as visual acuity, contrast sensitivity, pupil response, and saccades in addition to various ophthalmic tests, such as electroretinogram, visual evoked potential, optical coherence tomography (OCT), and OCT-angiography have been widely used and evaluated as potential biomarkers for different stages of dementia. SUMMARY Ophthalmologic and neuro-ophthalmic evaluation is evolving as an important part of the early diagnosis and management of people with dementia. A particular focus on ocular biomarkers in dementing illnesses has arisen over the past few years and there are several promising measures and imaging tools that have been proposed as potential biomarkers for these diseases.
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Affiliation(s)
- Vivian Paraskevi Douglas
- Division of Neuro-ophthalmology, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA
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18
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Zhu G, Wang J, Xiao L, Yang K, Huang K, Li B, Huang S, Hu B, Xiao B, Liu D, Feng L, Wang Q. Memory Deficit in Patients With Temporal Lobe Epilepsy: Evidence From Eye Tracking Technology. Front Neurosci 2021; 15:716476. [PMID: 34557066 PMCID: PMC8453169 DOI: 10.3389/fnins.2021.716476] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/16/2021] [Indexed: 11/24/2022] Open
Abstract
Objective: To explore quantitative measurements of the visual attention and neuroelectrophysiological relevance of memory deficits in temporal lobe epilepsy (TLE) by eye tracking and electroencephalography (EEG). Methods: Thirty-four TLE patients and twenty-eight healthy controls were invited to complete neurobehavioral assessments, cognitive oculomotor tasks, and 24-h video EEG (VEEG) recordings using an automated computer-based memory assessment platform with an eye tracker. Visit counts, visit time, and time of first fixation on areas of interest (AOIs) were recorded and analyzed in combination with interictal epileptic discharge (IED) characteristics from the bilateral temporal lobes. Results: The TLE patients had significantly worse Wechsler Digit Span scores [F(1, 58) = 7.49, p = 0.008]. In the Short-Term Memory Game with eye tracking, TLE patients took a longer time to find the memorized items [F(1, 57) = 17.30, p < 0.001]. They had longer first fixation [F(1, 57) = 4.06, p = 0.049] and more visit counts [F(1, 57) = 7.58, p = 0.008] on the target during the recall. Furthermore, the performance of the patients in the Digit Span task was negatively correlated with the total number of IEDs [r(28) = −0.463, p = 0.013] and the number of spikes per sleep cycle [r(28) = −0.420, p = 0.026]. Conclusion: Eye tracking appears to be a quantitative, objective measure of memory evaluation, demonstrating memory retrieval deficits but preserved visual attention in TLE patients. Nocturnal temporal lobe IEDs are closely associated with memory performance, which might be the electrophysiological mechanism for memory impairment in TLE.
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Affiliation(s)
- Guangpu Zhu
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Xi'an, China.,University of Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Xi'an, China
| | - Jing Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ling Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ke Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Kailing Huang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Beibin Li
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
| | - Sha Huang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Bingliang Hu
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Xi'an, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ding Liu
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Xi'an, China.,Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Xi'an, China
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