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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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2
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Xu Y, Zhang C, Pan B, Yuan Q, Zhang X. A portable and efficient dementia screening tool using eye tracking machine learning and virtual reality. NPJ Digit Med 2024; 7:219. [PMID: 39174736 PMCID: PMC11341897 DOI: 10.1038/s41746-024-01206-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 07/29/2024] [Indexed: 08/24/2024] Open
Abstract
Dementia represents a significant global health challenge, with early screening during the preclinical stage being crucial for effective management. Traditional diagnostic biomarkers for Alzheimer's Disease, the most common form of dementia, are limited by cost and invasiveness. Mild cognitive impairment (MCI), a precursor to dementia, is currently identified through neuropsychological tests like the Montreal Cognitive Assessment (MoCA), which are not suitable for large-scale screening. Eye-tracking technology, capturing and quantifying eye movements related to cognitive behavior, has emerged as a promising tool for cognitive assessment. Subtle changes in eye movements could serve as early indicators of MCI. However, the interpretation of eye-tracking data is challenging. This study introduced a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality (VR) to offer a non-invasive, efficient alternative capable of large-scale deployment. VECA was conducted with 201 participants from Shenzhen Baoan Chronic Hospital, utilizing eye-tracking data captured via VR headsets to predict MoCA scores and classify cognitive impairment across different educational backgrounds. The support vector regression model employed demonstrated a high correlation (0.9) with MoCA scores, significantly outperforming baseline models. Furthermore, it established optimal cut-off scores for identifying cognitive impairment with notable sensitivity (88.5%) and specificity (83%). This study underscores VECA's potential as a portable, efficient tool for early dementia screening, highlighting the benefits of integrating eye-tracking technology, machine learning, and VR in cognitive health assessments.
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Affiliation(s)
- Ying Xu
- Shenzhen Bao'an Centre for Chronic Disease Control, Shenzhen, PR China
| | - Chi Zhang
- Shenzhen Yiwei Technology, Shenzhen, PR China
| | - Baobao Pan
- Shenzhen Yiwei Technology, Shenzhen, PR China
| | - Qing Yuan
- Shenzhen Bao'an Centre for Chronic Disease Control, Shenzhen, PR China.
| | - Xu Zhang
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, PR China.
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3
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Murray-Smith H, Barker S, Barkhof F, Barnes J, Brown TM, Captur G, R E Cartlidge M, Cash DM, Coath W, Davis D, Dickson JC, Groves J, Hughes AD, James SN, Keshavan A, Keuss SE, King-Robson J, Lu K, Malone IB, Nicholas JM, Rapala A, Scott CJ, Street R, Sudre CH, Thomas DL, Wong A, Wray S, Zetterberg H, Chaturvedi N, Fox NC, Crutch SJ, Richards M, Schott JM. Updating the study protocol: Insight 46 - a longitudinal neuroscience sub-study of the MRC National Survey of Health and Development - phases 2 and 3. BMC Neurol 2024; 24:40. [PMID: 38263061 PMCID: PMC10804658 DOI: 10.1186/s12883-023-03465-3] [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: 08/30/2023] [Accepted: 11/13/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Although age is the biggest known risk factor for dementia, there remains uncertainty about other factors over the life course that contribute to a person's risk for cognitive decline later in life. Furthermore, the pathological processes leading to dementia are not fully understood. The main goals of Insight 46-a multi-phase longitudinal observational study-are to collect detailed cognitive, neurological, physical, cardiovascular, and sensory data; to combine those data with genetic and life-course information collected from the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort); and thereby contribute to a better understanding of healthy ageing and dementia. METHODS/DESIGN Phase 1 of Insight 46 (2015-2018) involved the recruitment of 502 members of the NSHD (median age = 70.7 years; 49% female) and has been described in detail by Lane and Parker et al. 2017. The present paper describes phase 2 (2018-2021) and phase 3 (2021-ongoing). Of the 502 phase 1 study members who were invited to a phase 2 research visit, 413 were willing to return for a clinic visit in London and 29 participated in a remote research assessment due to COVID-19 restrictions. Phase 3 aims to recruit 250 study members who previously participated in both phases 1 and 2 of Insight 46 (providing a third data time point) and 500 additional members of the NSHD who have not previously participated in Insight 46. DISCUSSION The NSHD is the oldest and longest continuously running British birth cohort. Members of the NSHD are now at a critical point in their lives for us to investigate successful ageing and key age-related brain morbidities. Data collected from Insight 46 have the potential to greatly contribute to and impact the field of healthy ageing and dementia by combining unique life course data with longitudinal multiparametric clinical, imaging, and biomarker measurements. Further protocol enhancements are planned, including in-home sleep measurements and the engagement of participants through remote online cognitive testing. Data collected are and will continue to be made available to the scientific community.
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Affiliation(s)
- Heidi Murray-Smith
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK.
| | - Suzie Barker
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
- Centre for Medical Image Computing, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Josephine Barnes
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Thomas M Brown
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Gabriella Captur
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Molly R E Cartlidge
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - David M Cash
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - William Coath
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Daniel Davis
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - John C Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - James Groves
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Josh King-Robson
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Kirsty Lu
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Ian B Malone
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Jennifer M Nicholas
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Alicja Rapala
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Catherine J Scott
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Rebecca Street
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Carole H Sudre
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
- Centre for Medical Image Computing, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - David L Thomas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Selina Wray
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute, University College London, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Hong, Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
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Gunawardane PDSH, MacNeil RR, Zhao L, Enns JT, de Silva CW, Chiao M. A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography. SENSORS (BASEL, SWITZERLAND) 2024; 24:540. [PMID: 38257633 PMCID: PMC11154520 DOI: 10.3390/s24020540] [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: 11/17/2023] [Revised: 01/03/2024] [Accepted: 01/11/2024] [Indexed: 01/24/2024]
Abstract
Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human-computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.
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Affiliation(s)
| | - Raymond Robert MacNeil
- Department of Psychology, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (R.R.M.); (M.C.)
| | - Leo Zhao
- Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (P.D.S.H.G.); (L.Z.); (C.W.d.S.)
| | - James Theodore Enns
- Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (P.D.S.H.G.); (L.Z.); (C.W.d.S.)
| | - Clarence Wilfred de Silva
- Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (P.D.S.H.G.); (L.Z.); (C.W.d.S.)
| | - Mu Chiao
- Department of Psychology, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (R.R.M.); (M.C.)
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5
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Newport RA, Liu S, Di Ieva A. Analyzing Eye Paths Using Fractals. ADVANCES IN NEUROBIOLOGY 2024; 36:827-848. [PMID: 38468066 DOI: 10.1007/978-3-031-47606-8_42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Visual patterns reflect the anatomical and cognitive background underlying process governing how we perceive information, influenced by stimulus characteristics and our own visual perception. These patterns are both spatially complex and display self-similarity seen in fractal geometry at different scales, making them challenging to measure using the traditional topological dimensions used in Euclidean geometry.However, methods for measuring eye gaze patterns using fractals have shown success in quantifying geometric complexity, matchability, and implementation into machine learning methods. This success is due to the inherent capabilities that fractals possess when reducing dimensionality using Hilbert curves, measuring temporal complexity using the Higuchi fractal dimension (HFD), and determining geometric complexity using the Minkowski-Bouligand dimension.Understanding the many applications of fractals when measuring and analyzing eye gaze patterns can extend the current growing body of knowledge by identifying markers tied to neurological pathology. Additionally, in future work, fractals can facilitate defining imaging modalities in eye tracking diagnostics by exploiting their capability to acquire multiscale information, including complementary functions, structures, and dynamics.
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Affiliation(s)
- Robert Ahadizad Newport
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
| | - Sidong Liu
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia
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Rane D, Dash DP, Dutt A, Dutta A, Das A, Lahiri U. Distinctive visual tasks for characterizing mild cognitive impairment and dementia using oculomotor behavior. Front Aging Neurosci 2023; 15:1125651. [PMID: 37547742 PMCID: PMC10397802 DOI: 10.3389/fnagi.2023.1125651] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/04/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction One's eye movement (in response to visual tasks) provides a unique window into the cognitive processes and higher-order cognitive functions that become adversely affected in cases with cognitive decline, such as those mild cognitive impairment (MCI) and dementia. MCI is a transitional stage between normal aging and dementia. Methods In the current work, we have focused on identifying visual tasks (such as horizontal and vertical Pro-saccade, Anti-saccade and Memory Guided Fixation tasks) that can differentiate individuals with MCI and dementia from their cognitively unimpaired healthy aging counterparts based on oculomotor Performance indices. In an attempt to identify the optimal combination of visual tasks that can be used to differentiate the participant groups, clustering was performed using the oculomotor Performance indices. Results Results of our study with a group of 60 cognitively unimpaired healthy aging individuals, a group with 60 individuals with MCI and a group with 60 individuals with dementia indicate that the horizontal and vertical Anti-saccade tasks provided the optimal combination that could differentiate individuals with MCI and dementia from their cognitively unimpaired healthy aging counterparts with clustering accuracy of ∼92% based on the saccade latencies. Also, the saccade latencies during both of these Anti-saccade tasks were found to strongly correlate with the Neuropsychological test scores. Discussion This suggests that the Anti-saccade tasks can hold promise in clinical practice for professionals working with individuals with MCI and dementia.
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Affiliation(s)
- Dharma Rane
- Indian Institute of Technology Gandhinagar, Electrical Engineering, Palaj, Gujarat, India
| | - Deba Prasad Dash
- Indian Institute of Technology Gandhinagar, Electrical Engineering, Palaj, Gujarat, India
| | | | - Anirban Dutta
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo SUNY, Buffalo, NY, United States
| | - Abhijit Das
- Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Uttama Lahiri
- Indian Institute of Technology Gandhinagar, Electrical Engineering, Palaj, Gujarat, India
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7
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Iao WC, Zhang W, Wang X, Wu Y, Lin D, Lin H. Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13050900. [PMID: 36900043 PMCID: PMC10001234 DOI: 10.3390/diagnostics13050900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/16/2023] [Accepted: 02/18/2023] [Indexed: 03/06/2023] Open
Abstract
Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios.
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Affiliation(s)
- Wai Cheng Iao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Weixing Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Xun Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Yuxuan Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou 570311, China
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510060, China
- Correspondence:
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Puterman-Salzman L, Katz J, Bergman H, Grad R, Khanassov V, Gore G, Vedel I, Wilchesky M, Armanfard N, Ghourchian N, Abbasgholizadeh Rahimi S. Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review. Dement Geriatr Cogn Dis Extra 2023; 13:28-38. [PMID: 37927529 PMCID: PMC10624450 DOI: 10.1159/000533693] [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: 04/10/2023] [Accepted: 08/07/2023] [Indexed: 11/07/2023] Open
Abstract
Background Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence (AI) may help in detection and screening of dementia; however, little is known in this area. Objectives The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method The review followed the framework proposed by O'Malley's and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson's or Huntington's disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.
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Affiliation(s)
| | - Jory Katz
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Howard Bergman
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Roland Grad
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | | | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences and Engineering, McGill University, Montreal, QC, Canada
| | - Isabelle Vedel
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | - Machelle Wilchesky
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, QC, Canada
- Division of Geriatric Medicine, McGill University, Montreal, QC, Canada
- Donald Berman Maimonides Centre for Research in Aging, Montreal, QC, Canada
| | - Narges Armanfard
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada
| | | | - Samira Abbasgholizadeh Rahimi
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Faculty of Dentistry Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
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9
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Deng L, Liu D, Li Y, Wang R, Liu J, Zhang J, Liu H. MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network. BMC Bioinformatics 2022; 23:427. [PMID: 36241972 PMCID: PMC9569055 DOI: 10.1186/s12859-022-04976-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 09/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Increasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs. Identifying circRNA-disease associations is of great significance to precise diagnosis and treatment of diseases. However, the traditional biological experiment is usually time-consuming and expensive. Hence, it is necessary to develop a computational framework to infer unknown associations between circRNA and disease. RESULTS In this work, we propose an efficient framework called MSPCD to infer unknown circRNA-disease associations. To obtain circRNA similarity and disease similarity accurately, MSPCD first integrates more biological information such as circRNA-miRNA associations, circRNA-gene ontology associations, then extracts circRNA and disease high-order features by the neural network. Finally, MSPCD employs DNN to predict unknown circRNA-disease associations. CONCLUSIONS Experiment results show that MSPCD achieves a significantly more accurate performance compared with previous state-of-the-art methods on the circFunBase dataset. The case study also demonstrates that MSPCD is a promising tool that can effectively infer unknown circRNA-disease associations.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Dayun Liu
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Yizhan Li
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Runqi Wang
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Junyi Liu
- Viterbi School of Engineering, University of Southern California, Los Angeles, 90089, USA
| | - Jiaxuan Zhang
- Department of Cognitive Science, University of California San Diego, La Jolla, 92093, USA
| | - Hui Liu
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, 211816, China.
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Mukaetova-Ladinska EB, De Lillo C, Arshad Q, Subramaniam HE, Maltby JJ. DEMENTIA COGNITIVE ASSESSMENT: NEED FOR AN INCLUSIVE TOOL DESIGN. Curr Alzheimer Res 2022; 19:265-273. [PMID: 35293294 DOI: 10.2174/1567205019666220315092008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 01/17/2022] [Accepted: 01/27/2022] [Indexed: 11/22/2022]
Affiliation(s)
- Elizabeta B Mukaetova-Ladinska
- Department of Neuroscience, Psychology and Behavour, Unievrsity of Leicester
- The Evington Center, Leicesterhire Partnership NHS Trust, Leicester
| | - Carlo De Lillo
- Department of Neuroscience, Psychology and Behavour, Unievrsity of Leicester
| | - Qadeer Arshad
- Department of Neuroscience, Psychology and Behavour, Unievrsity of Leicester
| | | | - John J Maltby
- Department of Neuroscience, Psychology and Behavour, Unievrsity of Leicester
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11
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Fan K, Cao J, Meng Z, Zhu J, Ma H, Ng ACM, Ng T, Qian W, Qi S. Predicting the Reader's English Level from Reading Fixation Patterns Using the Siamese Convolutional Neural Network. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1071-1080. [PMID: 35259110 DOI: 10.1109/tnsre.2022.3157768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Abstract-Among numerous functions performed by the human eye, reading is a common task that best reflects an individual's understanding and cognitive patterns. Previous studies showed that text comprehension may be determined by comprehension monitoring, a metacognitive process that evaluates and regulates the pattern of comprehension. Herein, we propose a hypothesis: an individual's cognitive pattern during reading is predictive of the level of reading comprehension. According to the criteria of the College English Test Band Six (CET-6), 80 participants (sophomore and junior) were divided into a pass group (n = 40) and a non-pass group (n = 40). Heatmaps of eye fixation counts were collected by an eye-tracker while each participant executed four reading comprehension tests. Using these heatmaps as inputs, we proposed the Siamese convolutional neural network models to predict the English level of participants. Both strategies of "Trained from scratch" and "Pretrained with fine-tuning" were employed. "Soft Voting" was applied to integrate the predictions from four tests. Results showed that the Siamese network model trained by the datasets with the cluster radius of fixation equal to 25 pixels and connection layer by L1 norm distance has a satisfactory or superior performance to other comparative experiments. The AUC values of Siamese networks trained by the two strategies reach 0.941 and 0.956, respectively. This indicates that the individual reading cognitive pattern captured by the eye-tracker can predict the level of reading comprehension through advanced deep learning models.
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Swain S, Bhushan B, Dhiman G, Viriyasitavat W. Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:3981-4003. [PMID: 35342282 PMCID: PMC8939887 DOI: 10.1007/s11831-022-09733-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 02/09/2022] [Indexed: 05/04/2023]
Abstract
Machine Learning (ML) has been categorized as a branch of Artificial Intelligence (AI) under the Computer Science domain wherein programmable machines imitate human learning behavior with the help of statistical methods and data. The Healthcare industry is one of the largest and busiest sectors in the world, functioning with an extensive amount of manual moderation at every stage. Most of the clinical documents concerning patient care are hand-written by experts, selective reports are machine-generated. This process elevates the chances of misdiagnosis thereby, imposing a risk to a patient's life. Recent technological adoptions for automating manual operations have witnessed extensive use of ML in its applications. The paper surveys the applicability of ML approaches in automating medical systems. The paper discusses most of the optimized statistical ML frameworks that encourage better service delivery in clinical aspects. The universal adoption of various Deep Learning (DL) and ML techniques as the underlying systems for a variety of wellness applications, is delineated by challenges and elevated by myriads of security. This work tries to recognize a variety of vulnerabilities occurring in medical procurement, admitting the concerns over its predictive performance from a privacy point of view. Finally providing possible risk delimiting facts and directions for active challenges in the future.
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Affiliation(s)
- Subhasmita Swain
- Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India
| | - Bharat Bhushan
- Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India
| | - Gaurav Dhiman
- Department of Computer Science, Government Bikram College of Commerce, Patiala, India
- University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, India
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India
| | - Wattana Viriyasitavat
- Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn Business School, Bangkok, Thailand
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13
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Liu Z, Yang Z, Gu Y, Liu H, Wang P. The effectiveness of eye tracking in the diagnosis of cognitive disorders: A systematic review and meta-analysis. PLoS One 2021; 16:e0254059. [PMID: 34252113 PMCID: PMC8274929 DOI: 10.1371/journal.pone.0254059] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/18/2021] [Indexed: 02/05/2023] Open
Abstract
Background Eye tracking (ET) is a viable marker for the recognition of cognitive disorders. We assessed the accuracy and clinical value of ET for the diagnosis of cognitive disorders in patients. Methods We searched the Medline, Embase, Web of Science, Cochrane Library, and Pubmed databases from inception to March 2, 2021, as well as the reference lists of identified primary studies. We included articles written in English that investigated ET for cognitive disorder patients—Mild cognitive impairment (MCI), Alzheimer’s disease (AD), Amyotrophic lateral sclerosis (ALS), and dementia. Two independent researchers extracted the data and the characteristics of each study; We calculated pooled sensitivities and specificities. A hierarchical summary of receiver performance characteristics (HSROC) model was used to test the diagnostic accuracy of ET for cognitive impairment (CI). Findings 11 studies met the inclusion criteria and were included in qualitative comprehensive analysis. Meta-analysis was performed on 9 trials using Neuropsychological Cognitive Testing (NCT) as the reference standard. The comprehensive sensitivity and specificity of ET for detecting cognitive disorders were 0.75 (95% CI 0.72–0.79) and 0.73 (95% CI 0.70 to 0.76), respectively. The combined positive likelihood ratio (LR+) was 2.74 (95%CI 2.32–3.24) and the negative likelihood ratio (LR−) was 0.27 (95%CI 0.18–0.42). Conclusions This review showed that ET technology could be used to detect the decline in CI, clinical use of ET techniques in combination with other tools to assess CI can be encouraged.
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Affiliation(s)
- Zicai Liu
- Department of Rehabilitation Medicine, Yue Bei People's Hospital, Shaoguan, Guangdong, China
| | - Zhen Yang
- Histology and Imaging platform, Core Facilities of West China Hospital, Sichuan University, China
| | - Yueming Gu
- Rehabilitation College of Gannan Medical University, Ganzhou, Jiangxi, China
| | - Huiyu Liu
- Department of Rehabilitation Medicine, Yue Bei People's Hospital, Shaoguan, Guangdong, China
| | - Pu Wang
- Department of Rehabilitation Medicine, The 7th Affiliated Hospital of Sun Yat-Sen University (Shenzhen), Shenzhen, Guangdong, China.,Guangdong Engineering and Technology Research Center for Rehabilitation Medicine and Translation, Guangzhou, China
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14
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Bellio M, Oxtoby NP, Walker Z, Henley S, Ribbens A, Blandford A, Alexander DC, Yong KXX. Analyzing large Alzheimer's disease cognitive datasets: Considerations and challenges. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12135. [PMID: 33313379 PMCID: PMC7720865 DOI: 10.1002/dad2.12135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/13/2020] [Accepted: 10/13/2020] [Indexed: 11/17/2022]
Abstract
Recent data-sharing initiatives of clinical and preclinical Alzheimer's disease (AD) have led to a growing number of non-clinical researchers analyzing these datasets using modern data-driven computational methods. Cognitive tests are key components of such datasets, representing the principal clinical tool to establish phenotypes and monitor symptomatic progression. Despite the potential of computational analyses in complementing the clinical understanding of AD, the characteristics and multifactorial nature of cognitive tests are often unfamiliar to computational researchers and other non-specialist audiences. This perspective paper outlines core features, idiosyncrasies, and applications of cognitive test data. We report tests commonly featured in data-sharing initiatives, highlight key considerations in their selection and analysis, and provide suggestions to avoid risks of misinterpretation. Ultimately, the greater transparency of cognitive measures will maximize insights offered in AD, particularly regarding understanding the extent and basis of AD phenotypic heterogeneity.
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Affiliation(s)
- Maura Bellio
- UCL Centre for Medical Image Computing (CMIC)Department of Computer ScienceUniversity College LondonLondonUK
- UCL Interaction Centre (UCLIC)Department of Computer ScienceUniversity College LondonLondonUK
| | - Neil P. Oxtoby
- UCL Centre for Medical Image Computing (CMIC)Department of Computer ScienceUniversity College LondonLondonUK
| | - Zuzana Walker
- Division of PsychiatryUniversity College LondonLondonUK
| | - Susie Henley
- Dementia Research CentreDepartment of Neurodegeneration, National Hospital for Neurology and NeurosurgeryUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | | | - Ann Blandford
- UCL Interaction Centre (UCLIC)Department of Computer ScienceUniversity College LondonLondonUK
| | - Daniel C. Alexander
- UCL Centre for Medical Image Computing (CMIC)Department of Computer ScienceUniversity College LondonLondonUK
| | - Keir X. X. Yong
- Dementia Research CentreDepartment of Neurodegeneration, National Hospital for Neurology and NeurosurgeryUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
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