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Piendel L, Vališ M, Hort J. An update on mobile applications collecting data among subjects with or at risk of Alzheimer's disease. Front Aging Neurosci 2023; 15:1134096. [PMID: 37323138 PMCID: PMC10267974 DOI: 10.3389/fnagi.2023.1134096] [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: 12/29/2022] [Accepted: 05/02/2023] [Indexed: 06/17/2023] Open
Abstract
Smart mobile phone use is increasing worldwide, as is the ability of mobile devices to monitor daily routines, behaviors, and even cognitive changes. There is a growing opportunity for users to share the data collected with their medical providers which may serve as an accessible cognitive impairment screening tool. Data logged or tracked in an app and analyzed with machine learning (ML) could identify subtle cognitive changes and lead to more timely diagnoses on an individual and population level. This review comments on existing evidence of mobile device applications designed to passively and/or actively collect data on cognition relevant for early detection and diagnosis of Alzheimer's disease (AD). The PubMed database was searched to identify existing literature on apps related to dementia and cognitive health data collection. The initial search deadline was December 1, 2022. Additional literature published in 2023 was accounted for with a follow-up search prior to publication. Criteria for inclusion was limited to articles in English which referenced data collection via mobile app from adults 50+ concerned, at risk of, or diagnosed with AD dementia. We identified relevant literature (n = 25) which fit our criteria. Many publications were excluded because they focused on apps which fail to collect data and simply provide users with cognitive health information. We found that although data collecting cognition-related apps have existed for years, the use of these apps as screening tools remains underdeveloped; however, it may serve as proof of concept and feasibility as there is much supporting evidence on their predictive utility. Concerns about the validity of mobile apps for cognitive screening and privacy issues remain prevalent. Mobile applications and use of ML is widely considered a financially and socially viable method of compiling symptomatic data but currently this large potential dataset, screening tool, and research resource is still largely untapped.
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Affiliation(s)
- Lydia Piendel
- Augusta University/University of Georgia Medical Partnership, Medical College of Georgia, Athens, GA, United States
- Memory Clinic, Department of Neurology, Charles University, 2nd Faculty of Medicine and Motol University Hospital, Prague, Czechia
| | - Martin Vališ
- Department of Neurology, University Hospital Hradec Králové, Faculty of Medicine, Charles University, Hradec Králové, Czechia
| | - Jakub Hort
- Memory Clinic, Department of Neurology, Charles University, 2nd Faculty of Medicine and Motol University Hospital, Prague, Czechia
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Ford E, Milne R, Curlewis K. Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2023; 13:e1492. [PMID: 38439952 PMCID: PMC10909482 DOI: 10.1002/widm.1492] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 03/06/2024]
Abstract
Dementia poses a growing challenge for health services but remains stigmatized and under-recognized. Digital technologies to aid the earlier detection of dementia are approaching market. These include traditional cognitive screening tools presented on mobile devices, smartphone native applications, passive data collection from wearable, in-home and in-car sensors, as well as machine learning techniques applied to clinic and imaging data. It has been suggested that earlier detection and diagnosis may help patients plan for their future, achieve a better quality of life, and access clinical trials and possible future disease modifying treatments. In this review, we explore whether digital tools for the early detection of dementia can or should be deployed, by assessing them against the principles of ethical screening programs. We conclude that while the importance of dementia as a health problem is unquestionable, significant challenges remain. There is no available treatment which improves the prognosis of diagnosed disease. Progression from early-stage disease to dementia is neither given nor currently predictable. Available technologies are generally not both minimally invasive and highly accurate. Digital deployment risks exacerbating health inequalities due to biased training data and inequity in digital access. Finally, the acceptability of early dementia detection is not established, and resources would be needed to ensure follow-up and support for those flagged by any new system. We conclude that early dementia detection deployed at scale via digital technologies does not meet standards for a screening program and we offer recommendations for moving toward an ethical mode of implementation. This article is categorized under:Application Areas > Health CareCommercial, Legal, and Ethical Issues > Ethical ConsiderationsTechnologies > Artificial Intelligence.
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Affiliation(s)
- Elizabeth Ford
- Department of Primary Care and Public HealthBrighton and Sussex Medical SchoolBrightonUK
| | - Richard Milne
- Kavli Centre for Ethics, Science and the PublicUniversity of CambridgeCambridgeUK
- Engagement and SocietyWellcome Connecting ScienceCambridgeUK
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Sirilertmekasakul C, Rattanawong W, Gongvatana A, Srikiatkhachorn A. The current state of artificial intelligence-augmented digitized neurocognitive screening test. Front Hum Neurosci 2023; 17:1133632. [PMID: 37063100 PMCID: PMC10098088 DOI: 10.3389/fnhum.2023.1133632] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/20/2023] [Indexed: 04/18/2023] Open
Abstract
The cognitive screening test is a brief cognitive examination that could be easily performed in a clinical setting. However, one of the main drawbacks of this test was that only a paper-based version was available, which restricts the test to be manually administered and graded by medical personnel at the health centers. The main solution to these problems was to develop a potential remote assessment for screening individuals with cognitive impairment. Currently, multiple studies have been adopting artificial intelligence (AI) technology into these tests, evolving the conventional paper-based neurocognitive test into a digitized AI-assisted neurocognitive test. These studies provided credible evidence of the potential of AI-augmented cognitive screening tests to be better and provided the framework for future studies to further improve the implementation of AI technology in the cognitive screening test. The objective of this review article is to discuss different types of AI used in digitized cognitive screening tests and their advantages and disadvantages.
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Sohn M, Yang J, Sohn J, Lee JH. Digital healthcare for dementia and cognitive impairment: A scoping review. Int J Nurs Stud 2022; 140:104413. [PMID: 36821951 DOI: 10.1016/j.ijnurstu.2022.104413] [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: 04/29/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Cognitive disorders, such as Alzheimer's disease, are a global health problem. Digital healthcare technology is an innovative management tool for delaying the progression of dementia and mild cognitive impairment. Thanks to digital technology, the possibility of safe and effective care for patients at home and in the community is increasing, even in situations that threaten the continuity of care, such as the COVID-19 pandemic. However, it is difficult to select appropriate technology and alternatives due to the lack of comprehensive reviews on the types and characteristics of digital technology for cognitive impairment, including their effects and limitations. OBJECTIVE This study aims to identify the types of digital healthcare technology for dementia and mild cognitive impairment and comprehensively examine how its outcome measures were constructed in line with each technology's purpose. METHODS According to the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews guidelines, a literature search was conducted in August 2021 using Medline (Ovid), EMBASE, and Cochrane library. The search terms were constructed based on Population-Concept-Context mnemonic: 'dementia', 'cognitive impairment', and 'cognitive decline'; digital healthcare technology, such as big data, artificial intelligence, virtual reality, robots, applications, and so on; and the outcomes of digital technology, such as accuracy of diagnosis and physical, mental, and social health. After grasping overall research trends, the literature was classified and analysed in terms of the type of service users and technology. RESULTS In total, 135 articles were selected. Since 2015, an increase in literature has been observed, and various digital healthcare technologies were identified. For people with mild cognitive impairment, technology for predicting and diagnosing the onset of dementia was studied, and for people with dementia, intervention technology to prevent the deterioration of health and induce significant improvement was considered. Regarding caregivers, many studies were conducted on monitoring and daily living assistive technologies that reduce the burden of care. However, problems such as data collection, storage, safety, and the digital divide persisted at different intensities for each technology type. CONCLUSIONS This study revealed that appropriate technology options and considerations may differ depending on the characteristics of users. It also emphasises the role of humans in designing and managing technology to apply digital healthcare technology more effectively.
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Affiliation(s)
- Minsung Sohn
- Division of Health and Medical Sciences, The Cyber University of Korea, Seoul, Republic of Korea
| | - JungYeon Yang
- Transdisciplinary Major in Learning Health Systems, Department of Public Health Science, Graduate School, Korea University, Republic of Korea
| | - Junyoung Sohn
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| | - Jun-Hyup Lee
- Department of Health Policy and Management, College of Health Sciences, Korea University, Republic of Korea.
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Automated method for real-time AMD screening of fundus images dedicated for mobile devices. Med Biol Eng Comput 2022; 60:1449-1479. [DOI: 10.1007/s11517-022-02546-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 03/06/2022] [Indexed: 01/01/2023]
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Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of Artificial Intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform 2022; 127:104030. [DOI: 10.1016/j.jbi.2022.104030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 12/17/2022]
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AIM in Neurodegenerative Diseases: Parkinson and Alzheimer. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Mujeeb Rahman KK, Monica Subashini M. A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT). J Autism Dev Disord 2021; 52:2732-2746. [PMID: 34191261 DOI: 10.1007/s10803-021-05141-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2021] [Indexed: 01/15/2023]
Abstract
Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance.
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Affiliation(s)
- K K Mujeeb Rahman
- Department of Biomedical Engineering, Ajman University, Ajman, United Arab Emirates
| | - M Monica Subashini
- School of Electrical Engineering, Vellore Institute of Technology, Vellore, India.
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Herzog NJ, Magoulas GD. Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia. SENSORS 2021; 21:s21030778. [PMID: 33498908 PMCID: PMC7865614 DOI: 10.3390/s21030778] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 01/20/2021] [Accepted: 01/21/2021] [Indexed: 11/30/2022]
Abstract
Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.
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Affiliation(s)
- Nitsa J. Herzog
- Department of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UK;
| | - George D. Magoulas
- Department of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UK;
- Birkbeck Knowledge Lab, University of London, London WC1E 7HZ, UK
- Correspondence:
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Davids J, Ashrafian H. AIM in Neurodegenerative Diseases: Parkinson and Alzheimer. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_190-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Thabtah F, Peebles D, Retzler J, Hathurusingha C. Dementia medical screening using mobile applications: A systematic review with a new mapping model. J Biomed Inform 2020; 111:103573. [PMID: 32961306 DOI: 10.1016/j.jbi.2020.103573] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 09/13/2020] [Accepted: 09/14/2020] [Indexed: 12/19/2022]
Abstract
Early detection is the key to successfully tackling dementia, a neurocognitive condition common among the elderly. Therefore, screening using technological platforms such as mobile applications (apps) may provide an important opportunity to speed up the diagnosis process and improve accessibility. Due to the lack of research into dementia diagnosis and screening tools based on mobile apps, this systematic review aims to identify the available mobile-based dementia and mild cognitive impairment (MCI) apps using specific inclusion and exclusion criteria. More importantly, we critically analyse these tools in terms of their comprehensiveness, validity, performance, and the use of artificial intelligence (AI) techniques. The research findings suggest diagnosticians in a clinical setting use dementia screening apps such as ALZ and CognitiveExams since they cover most of the domains for the diagnosis of neurocognitive disorders. Further, apps such as Cognity and ACE-Mobile have great potential as they use machine learning (ML) and AI techniques, thus improving the accuracy of the outcome and the efficiency of the screening process. Lastly, there was overlapping among the dementia screening apps in terms of activities and questions they contain therefore mapping these apps to the designated cognitive domains is a challenging task, which has been done in this research.
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Affiliation(s)
- Fadi Thabtah
- Digital Technologies, Manukau Institute of Technology, Auckland, New Zealand.
| | - David Peebles
- Department of Psychology, University of Huddersfield, Huddersfield, UK.
| | - Jenny Retzler
- Department of Psychology, University of Huddersfield, Huddersfield, UK.
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Thabtah F, Peebles D, Retzler J, Hathurusingha C. A review of dementia screening tools based on Mobile application. HEALTH AND TECHNOLOGY 2020. [DOI: 10.1007/s12553-020-00426-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Abdelhamid N, Padmavathy A, Peebles D, Thabtah F, Goulder-Horobin D. Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2020. [DOI: 10.1142/s0219649220400146] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Machine learning (ML) is a branch of computer science that is rapidly gaining popularity within the healthcare arena due to its ability to explore large datasets to discover useful patterns that can be interepreted for decision-making and prediction. ML techniques are used for the analysis of clinical parameters and their combinations for prognosis, therapy planning and support and patient management and wellbeing. In this research, we investigate a crucial problem associated with medical applications such as autism spectrum disorder (ASD) data imbalances in which cases are far more than just controls in the dataset. In autism diagnosis data, the number of possible instances is linked with one class, i.e. the no ASD is larger than the ASD, and this may cause performance issues such as models favouring the majority class and undermining the minority class. This research experimentally measures the impact of class imbalance issue on the performance of different classifiers on real autism datasets when various data imbalance approaches are utilised in the pre-processing phase. We employ oversampling techniques, such as Synthetic Minority Oversampling (SMOTE), and undersampling with different classifiers including Naive Bayes, RIPPER, C4.5 and Random Forest to measure the impact of these on the performance of the models derived in terms of area under curve and other metrics. Results pinpoint that oversampling techniques are superior to undersampling techniques, at least for the toddlers’ autism dataset that we consider, and suggest that further work should look at incorporating sampling techniques with feature selection to generate models that do not overfit the dataset.
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Affiliation(s)
- Neda Abdelhamid
- IT Programme, Auckland Institute of Studies, Auckland, New Zealand
| | - Arun Padmavathy
- Digital Technologies, Manukau Institute of Technology, Auckland, New Zealand
| | - David Peebles
- Department of Psychology, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
| | - Fadi Thabtah
- Digital Technologies, Manukau Institute of Technology, Auckland, New Zealand
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