1
|
M VR, GNK G, D R, T VP, Rao GN. Neuro Receptor Signal Detecting and Monitoring Smart Devices for Biological Changes in Cognitive Health Conditions. Ann Neurosci 2024; 31:225-233. [PMID: 39156625 PMCID: PMC11325689 DOI: 10.1177/09727531231206888] [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: 07/20/2023] [Accepted: 09/19/2023] [Indexed: 08/20/2024] Open
Abstract
Background Currently, wearable sensors significantly impact health care through continuous monitoring and event prediction. The types and clinical applications of wearable technology for the prevention of mental illnesses, as well as associated health authority rules, are covered in the current review. Summary The technologies behind wearable ECG monitors, biosensors, electronic skin patches, neural interfaces, retinal prosthesis, and smart contact lenses were discussed. We described how sensors will examine neuronal impulses using verified machine-learning algorithms running in real-time. These sensors will closely monitor body signals and demonstrate continuous sensing with wireless functionality. The wearable applications in the following medical fields were covered in our review: sleep, neurology, mental health, anxiety, depression, Parkinson's disease, epilepsy, seizures, and schizophrenia. These mental health conditions can cause serious issues, even death. Inflammation brought on by mental health problems can worsen hypothalamic-pituitary-adrenal axis dysfunction and interfere with certain neuroregulatory systems such as the neural peptide Y, serotonergic, and cholinergic systems. Severe depressive disorder symptoms are correlated with elevated Interleukin (IL-6) levels. On the basis of previous and present data collected utilizing a variety of sensory modalities, researchers are currently investigating ways to identify or detect the current mental state. Key message This review explores the potential of various mental health monitoring technologies. The types and clinical uses of wearable technology, such as ECG monitors, biosensors, electronic skin patches, brain interfaces, retinal prostheses, and smart contact lenses, were covered in the current review will be beneficial for patients with mental health problems like Alzheimer, epilepsy, dementia. The sensors will closely monitor bodily signals with wireless functionality while using machine learning algorithms to analyse neural impulses in real time.
Collapse
Affiliation(s)
- Vivek Reddy M
- Department of Regulatory Affairs, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Ganesh GNK
- Department of Regulatory Affairs, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Rudhresh D
- Department of Regulatory Affairs, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Vaishnavi Parimala T
- Department of Regulatory Affairs, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Gaddam Narasimha Rao
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| |
Collapse
|
2
|
Khalid UB, Naeem M, Stasolla F, Syed MH, Abbas M, Coronato A. Impact of AI-Powered Solutions in Rehabilitation Process: Recent Improvements and Future Trends. Int J Gen Med 2024; 17:943-969. [PMID: 38495919 PMCID: PMC10944308 DOI: 10.2147/ijgm.s453903] [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: 12/15/2023] [Accepted: 03/05/2024] [Indexed: 03/19/2024] Open
Abstract
Rehabilitation is an important and necessary part of local and global healthcare services along with treatment and palliative care, prevention of disease, and promotion of good health. The rehabilitation process helps older and young adults even children to become as independent as possible in activities of daily life and enables participation in useful living activities, recreation, work, and education. The technology of Artificial Intelligence (AI) has evolved significantly in recent years. Many activities related to rehabilitation have been getting benefits from using AI techniques. The objective of this review study is to explore the advantages of AI for rehabilitation and how AI is impacting the rehabilitation process. This study aims at the most critical aspects of the rehabilitation process that could potentially take advantage of AI techniques including personalized rehabilitation apps, rehabilitation through assistance, rehabilitation for neurological disorders, rehabilitation for developmental disorders, virtual reality rehabilitation, rehabilitation of neurodegenerative diseases and Telerehabilitation of Cardiovascular. We presented a survey on the newest empirical studies available in the literature including the AI-based technology helpful in the Rehabilitation process. The novelty feature included but was not limited to an overview of the technological solutions useful in rehabilitation. Seven different categories were identified. Illustrative examples of practical applications were detailed. Implications of the findings for both research and practice were critically discussed. Most of the AI applications in these rehabilitation types are in their infancy and continue to grow while exploring new opportunities. Therefore, we investigate the role of AI technology in rehabilitation processes. In addition, we do statistical analysis of the selected studies to highlight the significance of this review work. In the end, we also present a discussion on some challenges, and future research directions.
Collapse
Affiliation(s)
- Umamah bint Khalid
- Department of Electronics, Quaid-I-Azam University, Islamabad, 44000, Pakistan
| | - Muddasar Naeem
- Research Center on ICT Technologies for Healthcare and Wellbeing, Università Telematica “Giustino Fortunato”, Benevento, 82100, Italy
| | - Fabrizio Stasolla
- Research Center on ICT Technologies for Healthcare and Wellbeing, Università Telematica “Giustino Fortunato”, Benevento, 82100, Italy
| | - Madiha Haider Syed
- Department of Electronics, Quaid-I-Azam University, Islamabad, 44000, Pakistan
- Institute of Information Technology, Quaid-i-Azam University, Islamabad, 44000, Pakistan
| | - Musarat Abbas
- Department of Electronics, Quaid-I-Azam University, Islamabad, 44000, Pakistan
| | - Antonio Coronato
- Research Center on ICT Technologies for Healthcare and Wellbeing, Università Telematica “Giustino Fortunato”, Benevento, 82100, Italy
| |
Collapse
|
3
|
Hill JR, Min EE, Abebe E, Holden RJ. Telecaregiving for Dementia: A Mapping Review of Technological and Nontechnological Interventions. THE GERONTOLOGIST 2024; 64:gnad026. [PMID: 36919597 PMCID: PMC10733214 DOI: 10.1093/geront/gnad026] [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: 08/29/2022] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Informal (or family) caregivers to older adults with Alzheimer's disease or other related dementias (ADRD) could greatly benefit from innovative telecaregiving systems that support caregiving from a distance. The objective of this review is to better understand (a) who is involved in telecaregiving and their experiences; (b) the interventions currently available to support ADRD telecaregiving; and (c) the outcomes measured to assess the effects of ADRD telecaregiving interventions. RESEARCH DESIGN AND METHODS A mapping review was conducted by systematically searching MEDLINE, CINAHL, Embase, and PsycINFO for all works published in English from 2002 to 2022. References of included publications were searched to identify additional empirical publications for inclusion. RESULTS Sixty-one publications (describing 48 studies and 5 nonstudy sources) were included in the review. Currently available information on the demographics, experiences, challenges, and benefits of ADRD telecaregivers is summarized. We found that interventions to support telecaregiving could be classified into 7 categories of technological interventions and 3 categories of nontechnological interventions. Empirical studies on ADRD telecaregiving interventions investigated a variety of outcomes, the most prevalent being user experience. DISCUSSION AND IMPLICATIONS We conclude that (a) the paucity of literature on telecaregiving does not allow for a comprehensive understanding of the needs and day-to-day activities of ADRD telecaregivers; (b) interventions developed to support ADRD telecaregiving may not fully meet the needs of caregivers or care recipients; and (c) there is insufficient rigorous research establishing the effects of telecaregiving interventions on key ADRD-related outcomes.
Collapse
Affiliation(s)
- Jordan R Hill
- Department of Health & Wellness Design, Indiana University School of Public Health—Bloomington, Bloomington, Indiana, USA
- Indiana University Center for Aging Research, Regenstrief Institute Inc., Indianapolis, Indiana, USA
| | - Elissa E Min
- Department of Pharmacy Practice, Purdue University, West Lafayette, Indiana, USA
| | - Ephrem Abebe
- Department of Pharmacy Practice, Purdue University, West Lafayette, Indiana, USA
- Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Richard J Holden
- Department of Health & Wellness Design, Indiana University School of Public Health—Bloomington, Bloomington, Indiana, USA
- Indiana University Center for Aging Research, Regenstrief Institute Inc., Indianapolis, Indiana, USA
| |
Collapse
|
4
|
Manchanda N, Aggarwal A, Setya S, Talegaonkar S. Digital Intervention For The Management Of Alzheimer's Disease. Curr Alzheimer Res 2023; 19:CAR-EPUB-129308. [PMID: 36744687 DOI: 10.2174/1567205020666230206124155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/08/2023] [Accepted: 01/12/2023] [Indexed: 02/07/2023]
Abstract
Alzheimer's disease (AD) is a progressive, multifactorial, chronic, neurodegenerative disease with high prevalence and limited therapeutic options, making it a global health crisis. Being the most common cause of dementia, AD erodes the cognitive, functional, and social abilities of the individual and causes escalating medical and psychosocial needs. As yet, this disorder has no cure and current treatment options are palliative in nature. There is an urgent need for novel therapy to address this pressing challenge. Digital therapeutics (Dtx) is one such novel therapy that is gaining popularity globally. Dtx provides evidence based therapeutic interventions driven by internet and software, employing tools such as mobile devices, computers, videogames, apps, sensors, virtual reality aiding in the prevention, management, and treatment of ailments like neurological abnormalities and chronic diseases. Dtx acts as a supportive tool for the optimization of patient care, individualized treatment and improved health outcomes. Dtx uses visual, sound and other non-invasive approaches for instance-consistent therapy, reminiscence therapy, computerised cognitive training, semantic and phonological assistance devices, wearables and computer-assisted rehabilitation environment to find applications in Alzheimer's disease for improving memory, cognition, functional abilities and managing motor symptom. A few of the Dtx-based tools employed in AD include "Memory Matters", "AlzSense", "Alzheimer Assistant", "smart robotic dog", "Immersive virtual reality (iVR)" and the most current gamma stimulation. The purpose of this review is to summarize the current trends in digital health in AD and explore the benefits, challenges, and impediments of using Dtx as an adjunctive therapy for the management of AD.
Collapse
Affiliation(s)
- Namish Manchanda
- School of Pharmaceutical Sciences, Delhi Pharmaceutical Sciences & Research University, Govt. of NCT of Delhi, New Delhi-110017, India
| | - Akanksha Aggarwal
- Delhi Institute of Pharmaceutical Sciences And Research, Delhi Pharmaceutical Sciences & Research University, Govt. of NCT of Delhi, New Delhi-110017, India
| | - Sonal Setya
- Department of Pharmacy Practice, SGT College of Pharmacy, SGT University, Gurugram, Haryana-122505, India
| | - Sushama Talegaonkar
- School of Pharmaceutical Sciences, Delhi Pharmaceutical Sciences & Research University, Govt. of NCT of Delhi, New Delhi-110017, India
| |
Collapse
|
5
|
Prieto-Avalos G, Sánchez-Morales LN, Alor-Hernández G, Sánchez-Cervantes JL. A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases. BIOSENSORS 2022; 13:72. [PMID: 36671907 PMCID: PMC9856141 DOI: 10.3390/bios13010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.
Collapse
Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Laura Nely Sánchez-Morales
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| |
Collapse
|
6
|
Ullah R, Asghar I, Griffiths MG. An Integrated Methodology for Bibliometric Analysis: A Case Study of Internet of Things in Healthcare Applications. SENSORS (BASEL, SWITZERLAND) 2022; 23:67. [PMID: 36616665 PMCID: PMC9824791 DOI: 10.3390/s23010067] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/14/2022] [Accepted: 12/18/2022] [Indexed: 06/17/2023]
Abstract
This paper presents an integrated and easy methodology for bibliometric analysis. The proposed methodology is evaluated on recent research activities to highlight the role of the Internet of Things in healthcare applications. Different tools are used for bibliometric studies to explore the breadth and depth of different research areas. However, these Methods consider only the Web of Science or Scopus data for bibliometric analysis. Furthermore, bibliometric analysis has not been fully utilised to examine the capabilities of the Internet of Things for medical devices and their applications. There is a need for an easy methodology to use for a single integrated analysis of data from many sources rather than just the Web of Science or Scopus. A few bibliometric studies merge the Web of Science and Scopus to conduct a single integrated piece of research. This paper presents a methodology that could be used for a single bibliometric analysis across multiple databases. Three freely available tools, Excel, Perish or Publish and the R package Bibliometrix, are used for the purpose. The proposed bibliometric methodology is evaluated for studies related to the Internet of Medical Things (IoMT) and its applications in healthcare settings. An inclusion/exclusion criterion is developed to explore relevant studies from the seven largest databases, including Scopus, Web of Science, IEEE, ACM digital library, PubMed, Science Direct and Google Scholar. The study focuses on factors such as the number of publications, citations per paper, collaborative research output, h-Index, primary research and healthcare application areas. Data for this study are collected from the seven largest academic databases for 2012 to 2022 related to IoMT and their applications in healthcare. The bibliometric data analysis generated different research themes within IoMT technologies and their applications in healthcare research. The study has also identified significant research areas in this field. The leading research countries and their contributions are another output from the data analysis. Finally, future research directions are proposed for researchers to explore this area in further detail.
Collapse
|
7
|
Kumpik DP, Santos-Rodriguez R, Selwood J, Coulthard E, Twomey N, Craddock I, Ben-Shlomo Y. A longitudinal observational study of home-based conversations for detecting early dementia: protocol for the CUBOId TV task. BMJ Open 2022; 12:e065033. [PMID: 36418120 PMCID: PMC9684963 DOI: 10.1136/bmjopen-2022-065033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 11/07/2022] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Limitations in effective dementia therapies mean that early diagnosis and monitoring are critical for disease management, but current clinical tools are impractical and/or unreliable, and disregard short-term symptom variability. Behavioural biomarkers of cognitive decline, such as speech, sleep and activity patterns, can manifest prodromal pathological changes. They can be continuously measured at home with smart sensing technologies, and permit leveraging of interpersonal interactions for optimising diagnostic and prognostic performance. Here we describe the ContinUous behavioural Biomarkers Of cognitive Impairment (CUBOId) study, which explores the feasibility of multimodal data fusion for in-home monitoring of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The report focuses on a subset of CUBOId participants who perform a novel speech task, the 'TV task', designed to track changes in ecologically valid conversations with disease progression. METHODS AND ANALYSIS CUBOId is a longitudinal observational study. Participants have diagnoses of MCI or AD, and controls are their live-in partners with no such diagnosis. Multimodal activity data were passively acquired from wearables and in-home fixed sensors over timespans of 8-25 months. At two time points participants completed the TV task over 5 days by recording audio of their conversations as they watched a favourite TV programme, with further testing to be completed after removal of the sensor installations. Behavioural testing is supported by neuropsychological assessment for deriving ground truths on cognitive status. Deep learning will be used to generate fused multimodal activity-speech embeddings for optimisation of diagnostic and predictive performance from speech alone. ETHICS AND DISSEMINATION CUBOId was approved by an NHS Research Ethics Committee (Wales REC; ref: 18/WA/0158) and is sponsored by University of Bristol. It is supported by the National Institute for Health Research Clinical Research Network West of England. Results will be reported at conferences and in peer-reviewed scientific journals.
Collapse
Affiliation(s)
- Daniel Paul Kumpik
- Department of Engineering Mathematics, University of Bristol, Bristol, UK
| | | | - James Selwood
- Bristol Medical School, University of Bristol, Bristol, UK
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Elizabeth Coulthard
- Bristol Medical School, University of Bristol, Bristol, UK
- Department of Translational Health Sciences, University of Bristol, Bristol, UK
| | - Niall Twomey
- Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK
| | - Ian Craddock
- Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK
| | - Yoav Ben-Shlomo
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| |
Collapse
|
8
|
Khaliq F, Oberhauser J, Wakhloo D, Mahajani S. Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders. Neural Regen Res 2022; 18:1235-1242. [PMID: 36453399 PMCID: PMC9838151 DOI: 10.4103/1673-5374.355982] [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] [Indexed: 11/27/2022] Open
Abstract
Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis, treatment, and tracking of complex conditions, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. While no definitive methods of diagnosis or treatment exist for either disease, researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers. Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment. However, such techniques require further development aimed at improving transparency, adaptability, and reproducibility. In this review, we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer's and Parkinson's diseases.
Collapse
Affiliation(s)
- Fariha Khaliq
- Department of Biomedical Engineering and Sciences (BMES), National University of Science and Technology, Islamabad, Pakistan,Correspondence to: Fariha Khaliq, ; Sameehan Mahajani, .
| | - Jane Oberhauser
- Department of Neuropathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Debia Wakhloo
- Department of Neuropathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sameehan Mahajani
- Department of Neuropathology, School of Medicine, Stanford University, Stanford, CA, USA,Correspondence to: Fariha Khaliq, ; Sameehan Mahajani, .
| |
Collapse
|
9
|
Cui D, Huang H, Peng Z, Li Q, He J, Qiu J, Luo X, Ou J, Fan C. Next-generation 5G fusion-based intelligent health-monitoring platform for ethylene cracking furnace tube. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:9168-9199. [PMID: 35942754 DOI: 10.3934/mbe.2022426] [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: 06/15/2023]
Abstract
This study aimed to develop a 5G + "mixed computing" + deep learning-based next-generation intelligent health-monitoring platform for an ethylene cracking furnace tube based on 5G communication technology, with the goal of improving the health management level of the key component of ethylene production, that is, the cracking furnace tube, and focusing on the key common technical difficulties of ethylene production of tube outer-surface temperature sensing and tube slagging diagnosis. It also integrated the edge-fog-cloud "mixed computing" technology and deep learning technology in artificial intelligence, which had a higher degree in the research and development of automation and intelligence, and was more versatile in an industrial environment. The platform included a 5G-based tube intelligent temperature-measuring device, a 5G-based intelligent peep door gearing, a 5G-based edge-fog-cloud collaboration mechanism, and a mixed deep learning-related application. The platform enhanced the automation and intelligence of the enterprise, which could not only promote the quality and efficiency of the enterprise but also protect the safe operation of the cracking furnace device and lead the technological progress and transformation and upgrading of the industry through the application.
Collapse
Affiliation(s)
- Delong Cui
- College of Electronic Information Engineer, Guangdong University of Petrochemical Technology, Maoming, China
| | - Hong Huang
- College of Electronic Information Engineer, Guangdong University of Petrochemical Technology, Maoming, China
| | - Zhiping Peng
- College of Electronic Information Engineer, Guangdong University of Petrochemical Technology, Maoming, China
- Jiangmen Polytechnic, China
| | - Qirui Li
- College of Electronic Information Engineer, Guangdong University of Petrochemical Technology, Maoming, China
| | - Jieguang He
- College of Electronic Information Engineer, Guangdong University of Petrochemical Technology, Maoming, China
| | - Jinbo Qiu
- College of Electronic Information Engineer, Guangdong University of Petrochemical Technology, Maoming, China
| | - Xinlong Luo
- College of Electronic Information Engineer, Guangdong University of Petrochemical Technology, Maoming, China
| | - Jiangtao Ou
- AI Sensing Technology, Chancheng District, Foshan, China
| | - Chengyuan Fan
- AI Sensing Technology, Chancheng District, Foshan, China
| |
Collapse
|
10
|
Turnbull A, Kaplan R, Adeli E, Lin FV. A Novel Explainability Approach for Technology-Driven Translational Research on Brain Aging. J Alzheimers Dis 2022; 88:1229-1239. [PMID: 35754280 PMCID: PMC9399001 DOI: 10.3233/jad-220441] [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] [Indexed: 01/03/2023]
Abstract
Brain aging leads to difficulties in functional independence. Mitigating these difficulties can benefit from technology that predicts, monitors, and modifies brain aging. Translational research prioritizes solutions that can be causally linked to specific pathophysiologies at the same time as demonstrating improvements in impactful real-world outcome measures. This poses a challenge for brain aging technology that needs to address the tension between mechanism-driven precision and clinical relevance. In the current opinion, by synthesizing emerging mechanistic, translational, and clinical research-related frameworks, and our own development of technology-driven brain aging research, we suggest incorporating the appreciation of four desiderata (causality, informativeness, transferability, and fairness) of explainability into early-stage research that designs and tests brain aging technology. We apply a series of work on electrocardiography-based "peripheral" neuroplasticity markers from our work as an illustration of our proposed approach. We believe this novel approach will promote the development and adoption of brain aging technology that links and addresses brain pathophysiology and functional independence in the field of translational research.
Collapse
Affiliation(s)
- Adam Turnbull
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA
- School of Nursing, University of Rochester Medical Center, NY, USA
| | - Robert Kaplan
- Clinical Excellence Research Center (CERC), Stanford University, CA, USA
| | - Ehsan Adeli
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, CA, USA
| | - Feng V. Lin
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, CA, USA
| |
Collapse
|