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Sun YM, Wang ZY, Liang YY, Hao CW, Shi CH. Digital biomarkers for precision diagnosis and monitoring in Parkinson's disease. NPJ Digit Med 2024; 7:218. [PMID: 39169258 PMCID: PMC11339454 DOI: 10.1038/s41746-024-01217-2] [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: 04/02/2024] [Accepted: 08/07/2024] [Indexed: 08/23/2024] Open
Abstract
Parkinson's disease (PD) is a multifactorial neurodegenerative disorder with high prevalence among the elderly, primarily manifested by progressive decline in motor function. The aging global demographic and increased life expectancy have led to a rapid surge in PD cases, imposing a significant societal burden. PD along with other neurodegenerative diseases has garnered increasing attention from the scientific community. In PD, motor symptoms are recognized when approximately 60% of dopaminergic neurons have been damaged. The irreversible feature of PD and benefits of early intervention underscore the importance of disease onset prediction and prompt diagnosis. The advent of digital health technology in recent years has elevated the role of digital biomarkers in precisely and sensitively detecting early PD clinical symptoms, evaluating treatment effectiveness, and guiding clinical medication, focusing especially on motor function, responsiveness and sleep quality assessments. This review examines prevalent digital biomarkers for PD and highlights the latest advancements.
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Affiliation(s)
- Yue-Meng Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Zhi-Yun Wang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Yuan-Yuan Liang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Chen-Wei Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Chang-He Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China.
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China.
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China.
- NHC Key Laboratory of Prevention and treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China.
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Lee H, Kim HF, Hikosaka O. Implication of regional selectivity of dopamine deficits in impaired suppressing of involuntary movements in Parkinson's disease. Neurosci Biobehav Rev 2024; 162:105719. [PMID: 38759470 PMCID: PMC11167649 DOI: 10.1016/j.neubiorev.2024.105719] [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: 12/06/2023] [Revised: 04/26/2024] [Accepted: 05/09/2024] [Indexed: 05/19/2024]
Abstract
To improve the initiation and speed of intended action, one of the crucial mechanisms is suppressing unwanted movements that interfere with goal-directed behavior, which is observed relatively aberrant in Parkinson's disease patients. Recent research has highlighted that dopamine deficits in Parkinson's disease predominantly occur in the caudal lateral part of the substantia nigra pars compacta (SNc) in human patients. We previously found two parallel circuits within the basal ganglia, primarily divided into circuits mediated by the rostral medial part and caudal lateral part of the SNc dopamine neurons. We have further discovered that the indirect pathway in caudal basal ganglia circuits, facilitated by the caudal lateral part of the SNc dopamine neurons, plays a critical role in suppressing unnecessary involuntary movements when animals perform voluntary goal-directed actions. We thus explored recent research in humans and non-human primates focusing on the distinct functions and networks of the caudal lateral part of the SNc dopamine neurons to elucidate the mechanisms involved in the impairment of suppressing involuntary movements in Parkinson's disease patients.
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Affiliation(s)
- Hyunchan Lee
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD 20892-4435, USA.
| | - Hyoung F Kim
- School of Biological Sciences, College of Natural Sciences, Seoul National University (SNU), Seoul 08826, Republic of Korea
| | - Okihide Hikosaka
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD 20892-4435, USA
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Vaish A. A Machine Learning Approach for Early Identification of Prodromal Parkinson's Disease. Cureus 2024; 16:e63240. [PMID: 39070398 PMCID: PMC11281860 DOI: 10.7759/cureus.63240] [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] [Accepted: 06/22/2024] [Indexed: 07/30/2024] Open
Abstract
Parkinson's disease (PD) affects approximately 6 million people worldwide. Data analysis of early PD symptoms using machine learning (ML) models may provide an inexpensive, non-invasive, and simple method for the remote diagnosis of early PD. The aim of this project was to analyze voice, computer keystrokes, spiral drawings, and gait data involving PD patients and controls available in public databases using ML models and identify early PD characteristics that are more pronounced than others. An ML model was developed using Random Forest to analyze existing clinical data for PD patients, prodromal PD patients with REM (rapid eye movement) sleep behavior disorder (RBD) symptoms, and non-PD healthy controls. We reviewed and collected data from the UCI (University of California Irvine) Machine Learning Repository, PPMI (Parkinson's Progression Markers Initiative), and Kaggle databases. ML analysis was carried out on voice samples in PD and RBD patients, computer keystroke data, spiral drawings, and gait datasets. The ML prediction model developed may be helpful in improving risk prediction for PD, enabling early intervention and resource prioritization. The ML study suggests that voice analysis is the most robust test, followed by computer keystroke data, spiral drawings, and gait analysis, in that order. Voice is affected even in RBD patients, revealing that it is a sensitive and early measure of prodromal PD. The low accuracy of the analysis indicates that several PD-positive samples may remain undetected and unclassified. Combining all four features, that is, voice analysis, computer keystroke data, spiral drawings, and gait analysis, may improve the overall accuracy.
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Affiliation(s)
- Anisha Vaish
- Neurology, Tesla STEM (Science, Technology, Engineering & Math) High School, Redmond, USA
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Bhidayasiri R, Sringean J, Phumphid S, Anan C, Thanawattano C, Deoisres S, Panyakaew P, Phokaewvarangkul O, Maytharakcheep S, Buranasrikul V, Prasertpan T, Khontong R, Jagota P, Chaisongkram A, Jankate W, Meesri J, Chantadunga A, Rattanajun P, Sutaphan P, Jitpugdee W, Chokpatcharavate M, Avihingsanon Y, Sittipunt C, Sittitrai W, Boonrach G, Phonsrithong A, Suvanprakorn P, Vichitcholchai J, Bunnag T. The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand. Front Neurol 2024; 15:1386608. [PMID: 38803644 PMCID: PMC11129688 DOI: 10.3389/fneur.2024.1386608] [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: 02/15/2024] [Accepted: 04/19/2024] [Indexed: 05/29/2024] Open
Abstract
The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.
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Affiliation(s)
- Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand
| | - Jirada Sringean
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Saisamorn Phumphid
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Chanawat Anan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | | | - Suwijak Deoisres
- National Electronics and Computer Technology Centre, Pathum Thani, Thailand
| | - Pattamon Panyakaew
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Onanong Phokaewvarangkul
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Suppata Maytharakcheep
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Vijittra Buranasrikul
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Tittaya Prasertpan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Sawanpracharak Hospital, Nakhon Sawan, Thailand
| | | | - Priya Jagota
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Araya Chaisongkram
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Worawit Jankate
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Jeeranun Meesri
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Araya Chantadunga
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Piyaporn Rattanajun
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Phantakarn Sutaphan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Weerachai Jitpugdee
- Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Marisa Chokpatcharavate
- Chulalongkorn Parkinson's Disease Support Group, Department of Medicine, Faculty of Medicine, Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Yingyos Avihingsanon
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Society, Bangkok, Thailand
| | - Chanchai Sittipunt
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Society, Bangkok, Thailand
| | | | | | | | | | | | - Tej Bunnag
- Thai Red Cross Society, Bangkok, Thailand
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Hong CS, Smith TR. Aerobic exercise interventions to address impaired quality of life in patients with pituitary tumors. PLoS One 2023; 18:e0295907. [PMID: 38100429 PMCID: PMC10723697 DOI: 10.1371/journal.pone.0295907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Patients with pituitary tumors may experience persistent fatigue and reduced physical activity, based on subjective measures after treatment. These symptoms may persist despite gross total resection of their tumors and biochemical normalization of pituitary function. While reduced quality of life has been commonly acknowledged in pituitary tumor patients, there is a lack of studies on what interventions may be best implemented to ameliorate these issues, particularly when hormonal levels have otherwise normalized. Aerobic exercise programs have been previously described to ameliorate symptoms of chronic fatigue and reduced physical capacity across a variety of pathologies in the literature. As such, a prescribed aerobic exercise program may be an underrecognized but potentially impactful intervention to address quality of life in pituitary tumor patients. This review seeks to summarize the existing literature on aerobic exercise interventions in patients with pituitary tumors. In addition, future areas of study are discussed, including tailoring exercise programs to the hormonal status of the patient and incorporating more objective measures in monitoring response to interventions.
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Affiliation(s)
- Christopher S. Hong
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Computational Neuroscience Outcomes Center (CNOC), Boston, Masachusettts, United States of America
| | - Timothy R. Smith
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Computational Neuroscience Outcomes Center (CNOC), Boston, Masachusettts, United States of America
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Demir B, Ulukaya S, Erdem O. Detection of Parkinson's disease with keystroke data. Comput Methods Biomech Biomed Engin 2023; 26:1653-1667. [PMID: 37599616 DOI: 10.1080/10255842.2023.2245516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/21/2023] [Accepted: 07/16/2023] [Indexed: 08/22/2023]
Abstract
Parkinson's disease (PD) is one of the most widespread neurological disorders associated with nerve damage without definitive treatment. Impairments, such as trembling and slowing down in hand movements are among the first symptoms. For this purpose, in this study, machine learning (ML)-based models were developed by using keyboard keystroke dynamics. According to patients' drug use status, disease severity, and gender, we created 14 different sub-datasets and extracted 378 features using raw keystroke data. We developed alternative models with Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) algorithms. We further used Minimum Redundancy Maximum Relevance (mRmR), RELIEF, sequential forward selection (SFS), and RF feature selection methods to investigate prominent features in distinguishing PD. We developed ML models that jointly use the most popular features of selection algorithms (feature ensemble [FE]) and an ensemble classifier by combining multiple ML algorithms utilizing majority vote (model ensemble [ME]). With 14 different sets, FE and ME models provided accuracy (Acc.) in the range of 91.73 - 100% and 81.08 - 100%, respectively.
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Affiliation(s)
- Bahar Demir
- Department of Computational Science, Trakya University, Edirne, Turkey
| | - Sezer Ulukaya
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey
| | - Oğuzhan Erdem
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey
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Parab S, Boster J, Washington P. Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study. JMIR Form Res 2023; 7:e49898. [PMID: 37773607 PMCID: PMC10576230 DOI: 10.2196/49898] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/16/2023] [Accepted: 09/04/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND Parkinson disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad or touchscreen interactions, which serve as reliable indicators of PD. Previous works explore keyboard tapping and unstructured device monitoring; we attempt to further these works with structured tests taking into account 2D hand movement in addition to finger tapping. Our feasibility study uses keystroke and mouse movement data from a remotely conducted, structured, web-based test combined with self-reported PD status to create a predictive model for detecting the presence of PD. OBJECTIVE Analysis of finger tapping speed and accuracy through keyboard input and analysis of 2D hand movement through mouse input allowed differentiation between participants with and without PD. This comparative analysis enables us to establish clear distinctions between the two groups and explore the feasibility of using motor behavior to predict the presence of the disease. METHODS Participants were recruited via email by the Hawaii Parkinson Association (HPA) and directed to a web application for the tests. The 2023 HPA symposium was also used as a forum to recruit participants and spread information about our study. The application recorded participant demographics, including age, gender, and race, as well as PD status. We conducted a series of tests to assess finger tapping, using on-screen prompts to request key presses of constant and random keys. Response times, accuracy, and unintended movements resulting in accidental presses were recorded. Participants performed a hand movement test consisting of tracing straight and curved on-screen ribbons using a trackpad or mouse, allowing us to evaluate stability and precision of 2D hand movement. From this tracing, the test collected and stored insights concerning lower arm motor movement. RESULTS Our formative study included 31 participants, 18 without PD and 13 with PD, and analyzed their lower limb movement data collected from keyboards and computer mice. From the data set, we extracted 28 features and evaluated their significances using an extra tree classifier predictor. A random forest model was trained using the 6 most important features identified by the predictor. These selected features provided insights into precision and movement speed derived from keyboard tapping and mouse tracing tests. This final model achieved an average F1-score of 0.7311 (SD 0.1663) and an average accuracy of 0.7429 (SD 0.1400) over 20 runs for predicting the presence of PD. CONCLUSIONS This preliminary feasibility study suggests the possibility of using technology-based limb movement data to predict the presence of PD, demonstrating the practicality of implementing this approach in a cost-effective and accessible manner. In addition, this study demonstrates that structured mouse movement tests can be used in combination with finger tapping to detect PD.
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Affiliation(s)
- Shubham Parab
- University of Hawaii at Manoa, Honolulu, HI, United States
| | - Jerry Boster
- Hawaii Parkinson Association, Honolulu, HI, United States
| | - Peter Washington
- Department of Information & Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
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Liu WM, Yeh CL, Chen PW, Lin CW, Liu AB. Keystroke Biometrics as a Tool for the Early Diagnosis and Clinical Assessment of Parkinson's Disease. Diagnostics (Basel) 2023; 13:3061. [PMID: 37835803 PMCID: PMC10572112 DOI: 10.3390/diagnostics13193061] [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: 08/02/2023] [Revised: 09/22/2023] [Accepted: 09/23/2023] [Indexed: 10/15/2023] Open
Abstract
(1) Background: Parkinson's disease (PD) is the second most common neurodegenerative disease. Early diagnosis and reliable clinical assessments are essential for appropriate therapy and improving patients' quality of life. Keystroke biometrics, which capture unique typing behavior, have shown potential for early PD diagnosis. This study aimed to evaluate keystroke biometric parameters from two datasets to identify indicators that can effectively distinguish de novo PD patients from healthy controls. (2) Methods: Data from natural typing tasks in Physionet were analyzed to estimate keystroke biometric parameters. The parameters investigated included alternating-finger tapping (afTap) and standard deviations of interkey latencies (ILSD) and release latencies (RLSD). Sensitivity rates were calculated to assess the discriminatory ability of these parameters. (3) Results: Significant differences were observed in three parameters, namely afTap, ILSD, and RLSD, between de novo PD patients and healthy controls. The sensitivity rates were high, with values of 83%, 88%, and 96% for afTap, ILSD, and RLSD, respectively. Correlation analysis revealed a significantly negative correlation between typing speed and number of words typed with the standard motor assessment for PD, UPDRS-III, in patients with early PD. (4) Conclusions: Simple algorithms utilizing keystroke biometric parameters can serve as effective screening tests in distinguishing de novo PD patients from healthy controls. Moreover, typing speed and number of words typed were identified as reliable tools for assessing clinical statuses in PD patients. These findings underscore the potential of keystroke biometrics for early PD diagnosis and clinical severity assessment.
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Affiliation(s)
- Wei-Min Liu
- Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi 621301, Taiwan; (W.-M.L.); (C.-L.Y.)
| | - Che-Lun Yeh
- Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi 621301, Taiwan; (W.-M.L.); (C.-L.Y.)
| | - Po-Wei Chen
- Department of Physical Medicine and Rehabilitation, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan;
| | - Che-Wei Lin
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701401, Taiwan;
| | - An-Bang Liu
- Department of Medicine, School of Medicine, Tzu Chi University, Hualien 970374, Taiwan
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, Hualien 970473, Taiwan
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Belić M, Radivojević Z, Bobić V, Kostić V, Đurić-Jovičić M. Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms. Heliyon 2023; 9:e14824. [PMID: 37077676 PMCID: PMC10107087 DOI: 10.1016/j.heliyon.2023.e14824] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 03/06/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
Background Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear responses to medication, correct percentages of early diagnoses go as low as 26%. Technology has been used in various forms to facilitate discerning between persons with PD and healthy individuals, but much less work has been dedicated to separating PD and atypical parkinsonisms. Methods A wearable system was developed based on inertial sensors that capture the movements of fingers during repetitive finger tapping. A k-nearest-neighbor classifier was used on features extracted from gyroscope recordings for quick aid in differential diagnostics, discerning patients with PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and healthy controls (HC). Results The overall classification accuracy achieved was 85.18% in the multiclass setup. MSA and HC groups were the easiest to discern (100%), while PSP was the most elusive diagnosis, as some patients were incorrectly assigned to MSA and HC groups. Conclusions The system shows potential for use as a tool for quick diagnostic aid, and in the era of big data, offers a means of standardization of data collection that could allow scientists to aggregate multi-center data for further research.
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Chawla P, Rana SB, Kaur H, Singh K, Yuvaraj R, Murugappan M. A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Khanna NN, Maindarkar MA, Viswanathan V, Puvvula A, Paul S, Bhagawati M, Ahluwalia P, Ruzsa Z, Sharma A, Kolluri R, Krishnan PR, Singh IM, Laird JR, Fatemi M, Alizad A, Dhanjil SK, Saba L, Balestrieri A, Faa G, Paraskevas KI, Misra DP, Agarwal V, Sharma A, Teji JS, Al-Maini M, Nicolaides A, Rathore V, Naidu S, Liblik K, Johri AM, Turk M, Sobel DW, Miner M, Viskovic K, Tsoulfas G, Protogerou AD, Mavrogeni S, Kitas GD, Fouda MM, Kalra MK, Suri JS. Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study. J Clin Med 2022; 11:6844. [PMID: 36431321 PMCID: PMC9693632 DOI: 10.3390/jcm11226844] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 11/22/2022] Open
Abstract
A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Mahesh A. Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | | | - Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Annu’s Hospitals for Skin and Diabetes, Nellore 524101, India
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
| | - Zoltan Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Raghu Kolluri
- Ohio Health Heart and Vascular, Columbus, OH 43214, USA
| | | | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Surinder K. Dhanjil
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
| | - Antonella Balestrieri
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124 Cagliari, Italy
| | | | | | - Vikas Agarwal
- Department of Immunology, SGPGIMS, Lucknow 226014, India
| | - Aman Sharma
- Department of Immunology, SGPGIMS, Lucknow 226014, India
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Egkomi 2408, Cyprus
| | | | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
| | - Kiera Liblik
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
| | - David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 17674 Athens, Greece
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | | | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
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12
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Lam KH, Twose J, Lissenberg-Witte B, Licitra G, Meijer K, Uitdehaag B, De Groot V, Killestein J. The Use of Smartphone Keystroke Dynamics to Passively Monitor Upper Limb and Cognitive Function in Multiple Sclerosis: Longitudinal Analysis. J Med Internet Res 2022; 24:e37614. [PMID: 36342763 PMCID: PMC9679948 DOI: 10.2196/37614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/31/2022] [Accepted: 08/22/2022] [Indexed: 11/09/2022] Open
Abstract
Background Typing on smartphones, which has become a near daily activity, requires both upper limb and cognitive function. Analysis of keyboard interactions during regular typing, that is, keystroke dynamics, could therefore potentially be utilized for passive and continuous monitoring of function in patients with multiple sclerosis. Objective To determine whether passively acquired smartphone keystroke dynamics correspond to multiple sclerosis outcomes, we investigated the association between keystroke dynamics and clinical outcomes (upper limb and cognitive function). This association was investigated longitudinally in order to study within-patient changes independently of between-patient differences. Methods During a 1-year follow-up, arm function and information processing speed were assessed every 3 months in 102 patients with multiple sclerosis with the Nine-Hole Peg Test and Symbol Digit Modalities Test, respectively. Keystroke-dynamics data were continuously obtained from regular typing on the participants’ own smartphones. Press-and-release latency of the alphanumeric keys constituted the fine motor score cluster, while latency of the punctuation and backspace keys constituted the cognition score cluster. The association over time between keystroke clusters and the corresponding clinical outcomes was assessed with linear mixed models with subjects as random intercepts. By centering around the mean and calculating deviation scores within subjects, between-subject and within-subject effects were distinguished. Results Mean (SD) scores for the fine motor score cluster and cognition score cluster were 0.43 (0.16) and 0.94 (0.41) seconds, respectively. The fine motor score cluster was significantly associated with the Nine-Hole Peg Test: between-subject β was 15.9 (95% CI 12.2-19.6) and within-subject β was 6.9 (95% CI 2.0-11.9). The cognition score cluster was significantly associated with the Symbol Digit Modalities Test between subjects (between-subject β –11.2, 95% CI –17.3 to –5.2) but not within subjects (within-subject β –0.4, 95% CI –5.6 to 4.9). Conclusions Smartphone keystroke dynamics were longitudinally associated with multiple sclerosis outcomes. Worse arm function corresponded with longer latency in typing both across and within patients. Worse processing speed corresponded with higher latency in using punctuation and backspace keys across subjects. Hence, keystroke dynamics are a potential digital biomarker for remote monitoring and predicting clinical outcomes in patients with multiple sclerosis. Trial Registration Netherlands Trial Register NTR7268; https://trialsearch.who.int/Trial2.aspx?TrialID=NTR7268
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Affiliation(s)
- Ka-Hoo Lam
- Department of Neurology, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | | | - Birgit Lissenberg-Witte
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | | | | | - Bernard Uitdehaag
- Department of Neurology, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | - Vincent De Groot
- Department of Rehabilitation Medicine, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | - Joep Killestein
- Department of Neurology, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
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13
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Rana A, Dumka A, Singh R, Panda MK, Priyadarshi N. A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives. Diagnostics (Basel) 2022; 12:2708. [PMID: 36359550 PMCID: PMC9689408 DOI: 10.3390/diagnostics12112708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 08/03/2023] Open
Abstract
According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.
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Affiliation(s)
- Arti Rana
- Computer Science & Engineering, Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India
| | - Ankur Dumka
- Department of Computer Science and Engineering, Women Institute of Technology, Dehradun 248007, Uttarakhand, India
- Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun 248001, Uttarakhand, India
| | - Rajesh Singh
- Division of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
| | - Manoj Kumar Panda
- Department of Electrical Engineering, G.B. Pant Institute of Engineering and Technology, Pauri 246194, Uttarakhand, India
| | - Neeraj Priyadarshi
- Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, West Bengal, India
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14
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Bernardo LS, Damaševičius R, Ling SH, de Albuquerque VHC, Tavares JMRS. Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data. Biomedicines 2022; 10:2746. [PMID: 36359266 PMCID: PMC9687688 DOI: 10.3390/biomedicines10112746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/14/2022] [Accepted: 10/21/2022] [Indexed: 08/22/2023] Open
Abstract
Parkinson's disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject's key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches.
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Affiliation(s)
- Lucas Salvador Bernardo
- Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
| | - Sai Ho Ling
- Department of Electrical and Data Engineering, University of Technology Sydney, Sydney 2007, Australia
| | | | - João Manuel R. S. Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
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15
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Suri JS, Maindarkar MA, Paul S, Ahluwalia P, Bhagawati M, Saba L, Faa G, Saxena S, Singh IM, Chadha PS, Turk M, Johri A, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou AD, Misra DP, Agarwal V, Kitas GD, Kolluri R, Teji JS, Al-Maini M, Dhanjil SK, Sockalingam M, Saxena A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Omerzu T, Naidu S, Nicolaides A, Paraskevas KI, Kalra M, Ruzsa Z, Fouda MM. Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review. Diagnostics (Basel) 2022; 12:1543. [PMID: 35885449 PMCID: PMC9324237 DOI: 10.3390/diagnostics12071543] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/14/2022] [Accepted: 06/16/2022] [Indexed: 11/16/2022] Open
Abstract
Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (M.A.M.); (I.M.S.); (P.S.C.); (S.K.D.)
| | - Mahesh A. Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (M.A.M.); (I.M.S.); (P.S.C.); (S.K.D.)
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (S.P.); (M.B.)
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (S.P.); (M.B.)
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India;
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (S.P.); (M.B.)
| | - Luca Saba
- Department of Radiology, and Pathology, Azienda Ospedaliero Universitaria, 09123 Cagliari, Italy; (L.S.); (G.F.)
| | - Gavino Faa
- Department of Radiology, and Pathology, Azienda Ospedaliero Universitaria, 09123 Cagliari, Italy; (L.S.); (G.F.)
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhuneshwar 751029, India;
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (M.A.M.); (I.M.S.); (P.S.C.); (S.K.D.)
| | - Paramjit S. Chadha
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (M.A.M.); (I.M.S.); (P.S.C.); (S.K.D.)
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (M.T.); (T.O.)
| | - Amer Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India; (N.N.K.); (A.S.)
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Sofia Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 176 74 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 157 72 Athens, Greece; (D.W.S.); (P.P.S.)
| | | | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 157 72 Athens, Greece; (D.W.S.); (P.P.S.)
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 541 24 Thessaloniki, Greece;
| | - Athanase D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 157 72 Athens, Greece;
| | - Durga Prasanna Misra
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Raghu Kolluri
- OhioHealth Heart and Vascular, Mansfield, OH 44905, USA;
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology, and Rheumatology Institute, Toronto, ON M5G 1N8, Canada;
| | - Surinder K. Dhanjil
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (M.A.M.); (I.M.S.); (P.S.C.); (S.K.D.)
| | | | - Ajit Saxena
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India; (N.N.K.); (A.S.)
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA;
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA 95823, USA;
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Tomaz Omerzu
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (M.T.); (T.O.)
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Engomi 2408, Cyprus;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, 106 80 Athens, Greece;
| | - Mannudeep Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA;
| | - Zoltán Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
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16
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Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, Hadjileontiadis LJ. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep 2022; 12:7690. [PMID: 35546606 PMCID: PMC9095860 DOI: 10.1038/s41598-022-11865-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I2 = 79.49%) and 0.83 (CI 0.79-0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65-1.00, I2 = 79.10%) and 0.87 (95% CI 0.80-0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74-0.96, I2 = 50.39%) and 0.82 (95% CI 0.70-0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a "co-creation" approach that stems from mechanistic explanations of patients' characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Nayeefa Chowdhury
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz Quebrada, 1499-002, Lisbon, Portugal
| | - K Ray Chaudhuri
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, United Kingdom
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
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17
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Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07256-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractNon-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.
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18
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Gopal A, Hsu WY, Allen DD, Bove R. Remote Assessments of Hand Function in Neurological Disorders: Systematic Review. JMIR Rehabil Assist Technol 2022; 9:e33157. [PMID: 35262502 PMCID: PMC8943610 DOI: 10.2196/33157] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/17/2022] [Accepted: 01/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Loss of fine motor skills is observed in many neurological diseases, and remote monitoring assessments can aid in early diagnosis and intervention. Hand function can be regularly assessed to monitor loss of fine motor skills in people with central nervous system disorders; however, there are challenges to in-clinic assessments. Remotely assessing hand function could facilitate monitoring and supporting of early diagnosis and intervention when warranted. OBJECTIVE Remote assessments can facilitate the tracking of limitations, aiding in early diagnosis and intervention. This study aims to systematically review existing evidence regarding the remote assessment of hand function in populations with chronic neurological dysfunction. METHODS PubMed and MEDLINE, CINAHL, Web of Science, and Embase were searched for studies that reported remote assessment of hand function (ie, outside of traditional in-person clinical settings) in adults with chronic central nervous system disorders. We excluded studies that included participants with orthopedic upper limb dysfunction or used tools for intervention and treatment. We extracted data on the evaluated hand function domains, validity and reliability, feasibility, and stage of development. RESULTS In total, 74 studies met the inclusion criteria for Parkinson disease (n=57, 77% studies), stroke (n=9, 12%), multiple sclerosis (n=6, 8%), spinal cord injury (n=1, 1%), and amyotrophic lateral sclerosis (n=1, 1%). Three assessment modalities were identified: external device (eg, wrist-worn accelerometer), smartphone or tablet, and telerehabilitation. The feasibility and overall participant acceptability were high. The most common hand function domains assessed included finger tapping speed (fine motor control and rigidity), hand tremor (pharmacological and rehabilitation efficacy), and finger dexterity (manipulation of small objects required for daily tasks) and handwriting (coordination). Although validity and reliability data were heterogeneous across studies, statistically significant correlations with traditional in-clinic metrics were most commonly reported for telerehabilitation and smartphone or tablet apps. The most readily implementable assessments were smartphone or tablet-based. CONCLUSIONS The findings show that remote assessment of hand function is feasible in neurological disorders. Although varied, the assessments allow clinicians to objectively record performance in multiple hand function domains, improving the reliability of traditional in-clinic assessments. Remote assessments, particularly via telerehabilitation and smartphone- or tablet-based apps that align with in-clinic metrics, facilitate clinic to home transitions, have few barriers to implementation, and prompt remote identification and treatment of hand function impairments.
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Affiliation(s)
- Arpita Gopal
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Wan-Yu Hsu
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Diane D Allen
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco/San Francisco State University, San Francisco, CA, United States
| | - Riley Bove
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
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19
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Pereira MCL, Boese AC, Murad R, Yin J, Hamblin MH, Lee JP. Reduced dopaminergic neuron degeneration and global transcriptional changes in Parkinson's disease mouse brains engrafted with human neural stems during the early disease stage. Exp Neurol 2022; 352:114042. [PMID: 35271839 DOI: 10.1016/j.expneurol.2022.114042] [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: 12/15/2021] [Revised: 02/16/2022] [Accepted: 03/03/2022] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Current stem cell therapies for Parkinson's disease (PD) focus on a neurorestorative approach that aims to repair the CNS during the symptomatic phase. However, the pleiotropic and supportive effects of human neural stem cells (hNSCs) may make them effective for PD treatment during the disease's earlier stages. In the current study, we investigated the therapeutic effects of transplanting hNSCs during the early stages of PD development when most dopaminergic neurons are still present and before symptoms appear. Previous studies on hNSCs in Parkinson's disease focus on the substantia nigra and its immediate surroundings, but other brain structures are affected in PD as well. Here, we investigated the therapeutic effects of hNSCs on the entire PD-afflicted brain transcriptome using RNA sequencing (RNA-seq). METHODS PD was induced with a single intranasal infusion of 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) and hNSCs were transplanted unilaterally into the striatum one week later. The timepoint for hNSC transplantation coincided with upregulation of endogenous proinflammatory cytokines in the CNS, which play a role in stem cell migration. At 3 weeks post-transplantation (4 weeks post-MPTP), we assessed motor symptoms through behavioral tests, quantified dopaminergic neurons in the substantia nigra, and performed global transcriptional profiling to understand the mechanism underlying the effect of hNSCs on dopaminergic neuron degeneration. RESULTS We found that early hNSC engraftment mitigated motor symptoms induced by MPTP, and also reduced MPTP-induced loss of dopaminergic neurons. In this study, we uniquely presented the first comprehensive analysis of the effect of hNSC transplantation on the transcriptional profiling of PD mouse brains showing decreased expression of 249 and increased expression of 200 genes. These include genes implicated in mitochondrial bioenergetics, proteostasis, and other signaling pathways associated with improved PD outcome following hNSC transplantation. CONCLUSION These findings indicate that NSC transplantation during the asymptomatic phase of PD may limit or halt the progression of this neurodegenerative disorder. Transcriptional profiling of hNSC-engrafted PD mouse brains provides mechanistic insight that could lead to novel approaches to ameliorating degeneration of dopaminergic neurons and improving behavioral dysfunction in PD.
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Affiliation(s)
- Marcia C L Pereira
- Department of Physiology, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Austin C Boese
- Department of Physiology, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Rabi Murad
- Bioinformatics, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Jun Yin
- Bioinformatics, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Milton H Hamblin
- Tulane University Health Sciences Center, Tulane University, New Orleans, LA 70112, USA
| | - Jean-Pyo Lee
- Department of Physiology, Tulane University School of Medicine, New Orleans, LA 70112, USA.
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20
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Dhar J. An adaptive intelligent diagnostic system to predict early stage of parkinson's disease using two-stage dimension reduction with genetically optimized lightgbm algorithm. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06612-4] [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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21
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Paul S, Maindarkar M, Saxena S, Saba L, Turk M, Kalra M, Krishnan PR, Suri JS. Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review. Diagnostics (Basel) 2022; 12:166. [PMID: 35054333 PMCID: PMC8774851 DOI: 10.3390/diagnostics12010166] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/27/2021] [Accepted: 01/01/2022] [Indexed: 12/13/2022] Open
Abstract
Background and Motivation: Diagnosis of Parkinson's disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. METHOD The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. RESULT The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were "deep learning with sketches as outcomes" and "machine learning with Electroencephalography," respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. CONCLUSION The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.
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Affiliation(s)
- Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Maheshrao Maindarkar
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhuneshwar 751003, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09121 Cagliari, Italy
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, 1262 Maribor, Slovenia
| | - Manudeep Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
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22
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Lunt C, Denny JC. I can drive in Iceland: Enabling international joint analyses. CELL GENOMICS 2021; 1:100034. [PMID: 36778587 PMCID: PMC9903678 DOI: 10.1016/j.xgen.2021.100034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
In this issue of Cell Genomics, GA4GH reports key efforts to help share data across enclaves, including a framework for responsible data sharing, a data use ontology, and approaches for data use oversight. While there remains work in establishing reciprocity between data providers, we envision a future where joint analysis across enclaves is as easy as driving in different countries.
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Affiliation(s)
- Chris Lunt
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Joshua C. Denny
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
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23
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Zoppi T, Gharib M, Atif M, Bondavalli A. Meta-Learning to Improve Unsupervised Intrusion Detection in Cyber-Physical Systems. ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS 2021. [DOI: 10.1145/3467470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Artificial Intelligence (AI)-
based classifiers rely on
Machine Learning (ML)
algorithms to provide functionalities that system architects are often willing to integrate into critical
Cyber-Physical Systems (CPSs)
. However, such algorithms may misclassify observations, with potential detrimental effects on the system itself or on the health of people and of the environment. In addition, CPSs may be subject to threats that were not previously known, motivating the need for building
Intrusion Detectors (IDs)
that can effectively deal with zero-day attacks. Different studies were directed to compare misclassifications of various algorithms to identify the most suitable one for a given system. Unfortunately, even the most suitable algorithm may still show an unsatisfactory number of misclassifications when system requirements are strict. A possible solution may rely on the adoption of meta-learners, which build ensembles of base-learners to reduce misclassifications and that are widely used for supervised learning. Meta-learners have the potential to reduce misclassifications with respect to non-meta learners: however, misleading base-learners may let the meta-learner leaning towards misclassifications and therefore their behavior needs to be carefully assessed through empirical evaluation. To such extent, in this paper we investigate, expand, empirically evaluate, and discuss meta-learning approaches that rely on ensembles of unsupervised algorithms to detect (zero-day) intrusions in CPSs. Our experimental comparison is conducted by means of public datasets belonging to network intrusion detection and biometric authentication systems, which are common IDSs for CPSs. Overall, we selected 21 datasets, 15 unsupervised algorithms and 9 different meta-learning approaches. Results allow discussing the applicability and suitability of meta-learning for unsupervised anomaly detection, comparing metric scores achieved by base algorithms and meta-learners. Analyses and discussion end up showing how the adoption of meta-learners significantly reduces misclassifications when detecting (zero-day) intrusions in CPSs.
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Affiliation(s)
- Tommaso Zoppi
- Dept. of Mathematics and Informatics, University of Florence, Florence - Italy
| | - Mohamad Gharib
- Dept. of Mathematics and Informatics, University of Florence, Florence - Italy
| | - Muhammad Atif
- Dept. of Mathematics and Informatics, University of Florence, Florence - Italy
| | - Andrea Bondavalli
- Dept. of Mathematics and Informatics, University of Florence, Florence - Italy
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24
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Mei J, Desrosiers C, Frasnelli J. Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature. Front Aging Neurosci 2021; 13:633752. [PMID: 34025389 PMCID: PMC8134676 DOI: 10.3389/fnagi.2021.633752] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 03/22/2021] [Indexed: 12/26/2022] Open
Abstract
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.
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Affiliation(s)
- Jie Mei
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
| | - Christian Desrosiers
- Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle (LIVIA), Department of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Johannes Frasnelli
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
- Centre de Recherche de l'Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal (CIUSSS du Nord-de-l'Île-de-Montréal), Montreal, QC, Canada
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25
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Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
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Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
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26
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Distinguishing normal and aggregated alpha-synuclein interaction on gold nanorod incorporated zinc oxide nanocomposite by electrochemical technique. Int J Biol Macromol 2021; 171:217-224. [PMID: 33418041 DOI: 10.1016/j.ijbiomac.2021.01.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/26/2020] [Accepted: 01/03/2021] [Indexed: 11/21/2022]
Abstract
Misfolding and accumulation of the protein alpha synuclein in the brain cells characterize Parkinson's disease (PD). Electrochemical based aluminum interdigitated electrodes (ALIDEs) was fabricated by using conventional photolithography method and modified the surfaces with zinc oxide and gold nanorod by using spin coating method for the analysis of PD protein biomarker. The device surface modified with gold nanorod of 25 nm diameter was used. The bare devices and the surface modified devices were characterized by Scanning Electron Microscope, 3D-Profilometer, Atomic Force Microscope and high-power microscope. The above measurement was also performed to measure the interaction of antibody with aggregated alpha-synuclein for normal, aggregated and aggregated alpha synuclein in human serum and distinguished against 3 control proteins (PARK1, DJ-1 and Factor IX). The detection limit for normal alpha synuclein was 1 f. with the sensitivity of 1 f. on a linear regression (R2 = 0.9759). The detection limit for aggregated alpha synuclein was 10 aM with the sensitivity of 1 aM on a linear regression (R2 = 0.9797). Also, the detection limit of aggregated alpha synuclein in serum was 10 aM with the sensitivity of 1 aM on a linear regression (R2 = 0.9739). These results however indicate that, serum has only minimal amount of alpha synuclein.
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27
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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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28
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Smith E, Storch EA, Vahia I, Wong STC, Lavretsky H, Cummings JL, Eyre HA. Affective Computing for Late-Life Mood and Cognitive Disorders. Front Psychiatry 2021; 12:782183. [PMID: 35002802 PMCID: PMC8732874 DOI: 10.3389/fpsyt.2021.782183] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 11/29/2021] [Indexed: 12/20/2022] Open
Abstract
Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.
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Affiliation(s)
- Erin Smith
- The PRODEO Institute, San Francisco, CA, United States.,Organisation for Economic Co-operation and Development (OECD), Paris, France.,Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, United States.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Eric A Storch
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Ipsit Vahia
- Division of Geriatric Psychiatry, McLean Hospital, Boston, MA, United States.,Division of Geriatric Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Stephen T C Wong
- Systems Medicine and Biomedical Engineering Houston Methodist, Houston, TX, United States
| | - Helen Lavretsky
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jeffrey L Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, United States
| | - Harris A Eyre
- The PRODEO Institute, San Francisco, CA, United States.,Organisation for Economic Co-operation and Development (OECD), Paris, France.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.,Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States.,IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Deakin University, Geelong, VIC, Australia
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29
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Kamikubo R, Dwivedi U, Kacorri H. Sharing Practices for Datasets Related to Accessibility and Aging. ASSETS. ANNUAL ACM CONFERENCE ON ASSISTIVE TECHNOLOGIES 2021; 1:10.1145/3441852.3471208. [PMID: 35187541 PMCID: PMC8855358 DOI: 10.1145/3441852.3471208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.
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Affiliation(s)
- Rie Kamikubo
- College of Information Studies University of Maryland, College Park
| | - Utkarsh Dwivedi
- College of Information Studies University of Maryland, College Park
| | - Hernisa Kacorri
- College of Information Studies University of Maryland, College Park
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30
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Detection of early Parkinson’s disease with wavelet features using finger typing movements on a keyboard. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-03473-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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31
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Li X, Li J, Li P, Jiang Y, Wu Y, Li B. Injury to dopaminergic neurons development via the Lmx1a/Wnt1 autoregulatory loop induced by simazine. Toxicol Lett 2020; 333:279-289. [PMID: 32822773 DOI: 10.1016/j.toxlet.2020.07.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/17/2020] [Accepted: 07/26/2020] [Indexed: 10/23/2022]
Abstract
Simazine is a kind of persistent organic pollutant that is detected in both ground and water and has several routes of exposure. Here, we explored the mechanisms underlying simazine-related effects on dopaminergic neurons via development-related factors using mouse embryos and embryonic mesencephalic hybrid cell line (MN9D cells). We treated pregnant mice with 50 μg/kg bw, 200 μg/kg bw simazine from the 0.5 day to the 10.5 day of embryonic phase and MN9D cells with 600 μM simazine for 24 h to research the mechanism of dopaminergic neurons acute respond to simazine through preliminary experiments. Protein expressions of LIM homeobox transcription factor 1-alpha (Lmx1a) and LIM homeobox transcription factor 1-beta (Lmx1b) displayed a dose- and time-dependent increase after the exposure to simazine. In the 200 μg/kg bw of embryos and the 24h-600 μM of MN9D cells, protein levels of dopaminergic developmental factors were significantly upregulated, and dopaminergic function was significantly damaged for the abnormal expression of Dyt5b. We demonstrated simazine induced the injury to dopaminergic neurons via the Lmx1a/wingless-related integration site 1 (Wnt1) and Lmx1b pathways. In the transfection experiments, we knocked down Lmx1a and Lmx1b of cells to verify the potential target of simazine-induced injury to dopaminergic neurons, respectively. We detected the protein and mRNA levels of development-related genes of dopaminergic neurons and intracellular dopamine levels in different treatment groups. Based on our experiments' results, we demonstrated an acute response to 24 h-600 μM simazine treatment, the simazine-induced injury to dopaminergic neuronal which leads to abnormal dopamine levels and dopaminergic impairment is via the activation of the Lmx1a/Wnt1 autoregulatory loop. Lmx1a is a promising target in the search for the mechanisms underlying simazine-induced dopaminergic injury.
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Affiliation(s)
- Xueting Li
- Department of Toxicology, College of Public Health, Harbin Medical University, 157 Baojian Street, Harbin, Heilongjiang Province, 150081, PR China
| | - Jianan Li
- Department of Toxicology, College of Public Health, Harbin Medical University, 157 Baojian Street, Harbin, Heilongjiang Province, 150081, PR China
| | - Peng Li
- Department of Toxicology, College of Public Health, Harbin Medical University, 157 Baojian Street, Harbin, Heilongjiang Province, 150081, PR China
| | - Yujia Jiang
- Department of Toxicology, College of Public Health, Harbin Medical University, 157 Baojian Street, Harbin, Heilongjiang Province, 150081, PR China
| | - Yanping Wu
- Department of Toxicology, College of Public Health, Harbin Medical University, 157 Baojian Street, Harbin, Heilongjiang Province, 150081, PR China
| | - Baixiang Li
- Department of Toxicology, College of Public Health, Harbin Medical University, 157 Baojian Street, Harbin, Heilongjiang Province, 150081, PR China.
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32
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Asil SM, Ahlawat J, Barroso GG, Narayan M. Nanomaterial based drug delivery systems for the treatment of neurodegenerative diseases. Biomater Sci 2020; 8:4109-4128. [PMID: 32638706 PMCID: PMC7439575 DOI: 10.1039/d0bm00809e] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
With an aging population that has been increasing in recent years, the need for the development of therapeutic approaches for treatment of neurodegenerative disorders (ND) has increased. ND, which are characterized by the progressive loss of the structure or function of neurons, are often associated with neuronal death. In spite of screening numerous drugs, currently there is no specific treatment that can cure these diseases or slow down their progression. Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Huntington's disease, and prion diseases belong to ND which affect enormous numbers of people globally. There are some main possible reasons for failure in the treatment of neurodegenerative diseases such as limitations introduced by the Blood-Brain Barrier (BBB), the Blood-Cerebrospinal Fluid Barrier (BCFB) and P-glycoproteins. Current advances in nanotechnology present opportunities to overcome the mentioned limitations by using nanotechnology and designing nanomaterials improving the delivery of active drug candidates. Some of the basic and developing strategies to overcome drug delivery impediments are the local delivery of drugs, receptor-mediated transcytosis, physicochemical disruption of the BBB, cell-penetrating peptides and magnetic disruption. Recently, the application of nanoparticles has been developed to improve the efficiency of drug delivery. Nanoengineered particles as nanodrugs possess the capacity to cross the BBB and also show decreased invasiveness. Examples include inorganic, magnetic, polymeric and carbonic nanoparticles that have been developed to improve drug delivery efficiency. Despite numerous papers published in this filed, there are some unsolved issues that need to be addressed for successful treatment of neurodegenerative diseases. These are discussed herein.
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Affiliation(s)
- Shima Masoudi Asil
- The Department of Environmental Science & Engineering, The University of Texas at El Paso, USA
| | - Jyoti Ahlawat
- Department of Chemistry & Biochemistry, The University of Texas at El Paso, USA
| | | | - Mahesh Narayan
- Department of Chemistry & Biochemistry, The University of Texas at El Paso, USA
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Morgan C, Rolinski M, McNaney R, Jones B, Rochester L, Maetzler W, Craddock I, Whone AL. Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment. JOURNAL OF PARKINSON'S DISEASE 2020; 10:429-454. [PMID: 32250314 PMCID: PMC7242826 DOI: 10.3233/jpd-191781] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/31/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND The emergence of new technologies measuring outcomes in Parkinson's disease (PD) to complement the existing clinical rating scales has introduced the possibility of measurement occurring in patients' own homes whilst they freely live and carry out normal day-to-day activities. OBJECTIVE This systematic review seeks to provide an overview of what technology is being used to test which outcomes in PD from free-living participant activity in the setting of the home environment. Additionally, this review seeks to form an impression of the nature of validation and clinimetric testing carried out on the technological device(s) being used. METHODS Five databases (Medline, Embase, PsycInfo, Cochrane and Web of Science) were systematically searched for papers dating from 2000. Study eligibility criteria included: adults with a PD diagnosis; the use of technology; the setting of a home or home-like environment; outcomes measuring any motor and non-motor aspect relevant to PD, as well as activities of daily living; unrestricted/unscripted activities undertaken by participants. RESULTS 65 studies were selected for data extraction. There were wide varieties of participant sample sizes (<10 up to hundreds) and study durations (<2 weeks up to a year). The metrics evaluated by technology, largely using inertial measurement units in wearable devices, included gait, tremor, physical activity, bradykinesia, dyskinesia and motor fluctuations, posture, falls, typing, sleep and activities of daily living. CONCLUSIONS Home-based free-living testing in PD is being conducted by multiple groups with diverse approaches, focussing mainly on motor symptoms and sleep.
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Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Michal Rolinski
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Roisin McNaney
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Bennet Jones
- Library and Knowledge Service, Learning and Research, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Lynn Rochester
- Institute of Neuroscience, Newcastle University, Newcastle Upon Tyne, UK
- Newcastle Upon Tyne Hospitals National Health Service Foundation Trust, Newcastle Upon Tyne, UK
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts University, Kiel, Germany
| | - Ian Craddock
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Alan L. Whone
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
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Youngmann B, Allerhand L, Paltiel O, Yom-Tov E, Arkadir D. A machine learning algorithm successfully screens for Parkinson's in web users. Ann Clin Transl Neurol 2019; 6:2503-2509. [PMID: 31714022 PMCID: PMC6917308 DOI: 10.1002/acn3.50945] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 10/21/2019] [Indexed: 12/17/2022] Open
Abstract
Objective To develop, apply, and evaluate, a novel web‐based classifier for screening for Parkinson disease among a large cohort of search engine users. Methods A supervised machine learning classifier learned to distinguish web users with self‐reported Parkinson's disease from controls based on their interactions with a search engine (Bing, Microsoft). It was then applied to groups of web users with low or high risk for actual Parkinson's disease. Textual content of web queries was used to sort surfers into the different risk groups, but not for classifying users as negative or positive for Parkinson's disease. Disease detection was unsolicited. Researchers did not have access to any identifying data on users. Results Applying the classifier (with an estimated positive predictive value of 25%) resulted in 17,843/1,490,987 (1.2%) web users over the age of 40 years screened positive for Parkinson's disease. This percentile was higher in at‐risk groups (Fisher exact P < 0.00001), including users who searched for information regarding the disease (518/804, 64.4%), and users with non‐motor Parkinson's symptom or with an affected relative (57/1064, 5.3%). Longitudinal follow‐up revealed that in all studied groups individuals classified as having the disease showed a higher mean rate of progression in disease‐related features (t‐test P < 0.05). Interpretation An automatic classifier, based on mouse and keyboard interactions with a search engine, is able to reliably trace individuals at high risk for actual Parkinson's disease as well as to demonstrate more rapid progression of disease‐related signs in those who screened positive. This ability raises novel ethical issues.
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Affiliation(s)
| | | | - Ora Paltiel
- Braun School of Public Health and Community Medicine, Hadassah-Hebrew University, Jerusalem, Israel
| | - Elad Yom-Tov
- Microsoft Research, Herzliya, Israel.,Faculty of Industrial Engineering and Management, Technion, Haifa, Israel
| | - David Arkadir
- Department of Neurology, Hadassah Hebrew University, Jerusalem, Israel
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Adam H, Gopinath SC, Arshad MM, Adam T, Hashim U. Perspectives of nanobiotechnology and biomacromolecules in parkinson’s disease. Process Biochem 2019. [DOI: 10.1016/j.procbio.2019.07.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. NPJ Digit Med 2019; 2:88. [PMID: 31508498 PMCID: PMC6731256 DOI: 10.1038/s41746-019-0166-1] [Citation(s) in RCA: 141] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 08/09/2019] [Indexed: 02/07/2023] Open
Abstract
The use of data generated passively by personal electronic devices, such as smartphones, to measure human function in health and disease has generated significant research interest. Particularly in psychiatry, objective, continuous quantitation using patients' own devices may result in clinically useful markers that can be used to refine diagnostic processes, tailor treatment choices, improve condition monitoring for actionable outcomes, such as early signs of relapse, and develop new intervention models. If a principal goal for digital phenotyping is clinical improvement, research needs to attend now to factors that will help or hinder future clinical adoption. We identify four opportunities for research directed toward this goal: exploring intermediate outcomes and underlying disease mechanisms; focusing on purposes that are likely to be used in clinical practice; anticipating quality and safety barriers to adoption; and exploring the potential for digital personalized medicine arising from the integration of digital phenotyping and digital interventions. Clinical relevance also means explicitly addressing consumer needs, preferences, and acceptability as the ultimate users of digital phenotyping interventions. There is a risk that, without such considerations, the potential benefits of digital phenotyping are delayed or not realized because approaches that are feasible for application in healthcare, and the evidence required to support clinical commissioning, are not developed. Practical steps to accelerate this research agenda include the further development of digital phenotyping technology platforms focusing on scalability and equity, establishing shared data repositories and common data standards, and fostering multidisciplinary collaborations between clinical stakeholders (including patients), computer scientists, and researchers.
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Affiliation(s)
- Kit Huckvale
- Black Dog Institute, UNSW Sydney, Sydney, NSW Australia
| | | | - Helen Christensen
- Black Dog Institute, UNSW Sydney, Sydney, NSW Australia
- Mindgardens Neuroscience Network, Sydney, NSW Australia
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Tang P, Hou C, Liu Y, Liu P, Zhang X, Zhang L, Chong L, Li R. Quantitative Assessment of Finger Movement Profile in a Visual-Motor Task Based on a Tablet Computer: The Application in Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2019; 9:811-819. [PMID: 31450513 DOI: 10.3233/jpd-191695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND/OBJECTIVE Easily applicable, quantitative assessment of movement is widely needed in various clinical settings, especially in the evaluation of Parkinson's disease (PD). METHODS We developed a highly repeatable tablet computer-based finger movement assessment system (FMAS) to record finger movement profile in a visual-motor task both in PD (n = 217) and healthy participants (n = 221). RESULTS We found age-related declines in finger movement performance among the healthy participants but not in PD patients with the FMAS. Significant differences in movement time (MT) and latency/MT ratio but not in latency were observed in PD patients as compared with healthy subjects (P < 0.000). Meanwhile, we identified the latency/MT ratio as the optimal parameter to differentiate PD from age-matched healthy subjects in an age-independent manner (cut-off 1.08 with corresponding AUC = 0.861). In addition, a significant correlation was found between finger movement parameters and the Hoehn and Yahr scale (H-Y scale), UPDRS III score and the duration of the disease in PD patients (P < 0.01). CONCLUSION It was suggested that the tablet computer-based evaluation of finger movement provided an easily applicable quantitative method to assess the conditions of PD patients.
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Affiliation(s)
- Peng Tang
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Chen Hou
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Yue Liu
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Peng Liu
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xin Zhang
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Lina Zhang
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Li Chong
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Rui Li
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
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Belić M, Bobić V, Badža M, Šolaja N, Đurić-Jovičić M, Kostić VS. Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review. Clin Neurol Neurosurg 2019; 184:105442. [PMID: 31351213 DOI: 10.1016/j.clineuro.2019.105442] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 05/31/2019] [Accepted: 07/11/2019] [Indexed: 01/30/2023]
Abstract
Artificial intelligence, specifically machine learning, has found numerous applications in computer-aided diagnostics, monitoring and management of neurodegenerative movement disorders of parkinsonian type. These tasks are not trivial due to high inter-subject variability and similarity of clinical presentations of different neurodegenerative disorders in the early stages. This paper aims to give a comprehensive, high-level overview of applications of artificial intelligence through machine learning algorithms in kinematic analysis of movement disorders, specifically Parkinson's disease (PD). We surveyed papers published between January 2007 and January 2019, within online databases, including PubMed and Science Direct, with a focus on the most recently published studies. The search encompassed papers dealing with the implementation of machine learning algorithms for diagnosis and assessment of PD using data describing motion of upper and lower extremities. This systematic review presents an overview of 48 relevant studies published in the abovementioned period, which investigate the use of artificial intelligence for diagnostics, therapy assessment and progress prediction in PD based on body kinematics. Different machine learning algorithms showed promising results, particularly for early PD diagnostics. The investigated publications demonstrated the potentials of collecting data from affordable and globally available devices. However, to fully exploit artificial intelligence technologies in the future, more widespread collaboration is advised among medical institutions, clinicians and researchers, to facilitate aligning of data collection protocols, sharing and merging of data sets.
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Affiliation(s)
- Minja Belić
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Vladislava Bobić
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Milica Badža
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Nikola Šolaja
- School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Milica Đurić-Jovičić
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Vladimir S Kostić
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia.
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Lan BL, Yeo JHW. Comparison of computer-key-hold-time and alternating-finger-tapping tests for early-stage Parkinson's disease. PLoS One 2019; 14:e0219114. [PMID: 31247037 PMCID: PMC6597101 DOI: 10.1371/journal.pone.0219114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 06/14/2019] [Indexed: 11/19/2022] Open
Abstract
Giancardo et al. recently introduced the neuroQWERTY index (nQi), which is a novel motor index derived from computer-key-hold-time data using an ensemble regression algorithm, to detect early-stage Parkinson's disease. Here, we derive a much simpler motor index from their hold-time data, which is the standard deviation (SD) of the hold-time fluctuations, where fluctuation is defined as the difference between successive natural-log of hold time. Our results show the performance of the SD and nQi tests in discriminating early-stage subjects from controls do not differ, although the SD index is much simpler. There is also no difference in performance between the SD and alternating-finger-tapping tests.
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Affiliation(s)
- Boon Leong Lan
- Electrical and Computer Systems Engineering & Advanced Engineering Platform, School of Engineering, Monash University, Bandar Sunway, Malaysia
- * E-mail:
| | - Jacob Hsiao Wen Yeo
- Electrical and Computer Systems Engineering & Advanced Engineering Platform, School of Engineering, Monash University, Bandar Sunway, Malaysia
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White RW, Doraiswamy PM, Horvitz E. Detecting neurodegenerative disorders from web search signals. NPJ Digit Med 2018; 1:8. [PMID: 31304293 PMCID: PMC6550228 DOI: 10.1038/s41746-018-0016-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 01/02/2018] [Accepted: 01/10/2018] [Indexed: 12/31/2022] Open
Abstract
Neurodegenerative disorders, such as Parkinson’s disease (PD) and Alzheimer’s disease (AD), are important public health problems warranting early detection. We trained machine-learned classifiers on the longitudinal search logs of 31,321,773 search engine users to automatically detect neurodegenerative disorders. Several digital phenotypes with high discriminatory weights for detecting these disorders are identified. Classifier sensitivities for PD detection are 94.2/83.1/42.0/34.6% at false positive rates (FPRs) of 20/10/1/0.1%, respectively. Preliminary analysis shows similar performance for AD detection. Subject to further refinement of accuracy and reproducibility, these findings show the promise of web search digital phenotypes as adjunctive screening tools for neurodegenerative disorders.
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Affiliation(s)
| | - P Murali Doraiswamy
- 2Duke University Health System and Duke Institute for Brain Sciences, Durham, NC 27710 USA
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