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Zahavi I, Ben Shitrit I, Einav S. Using augmented intelligence to improve long term outcomes. Curr Opin Crit Care 2024; 30:523-531. [PMID: 39150034 DOI: 10.1097/mcc.0000000000001185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
PURPOSE OF REVIEW For augmented intelligence (AI) tools to realize their potential, critical care clinicians must ensure they are designed to improve long-term outcomes. This overview is intended to align professionals with the state-of-the art of AI. RECENT FINDINGS Many AI tools are undergoing preliminary assessment of their ability to support the care of survivors and their caregivers at multiple time points after intensive care unit (ICU) discharge. The domains being studied include early identification of deterioration (physiological, mental), management of impaired physical functioning, pain, sleep and sexual dysfunction, improving nutrition and communication, and screening and treatment of cognitive impairment and mental health disorders.Several technologies are already being marketed and many more are in various stages of development. These technologies mostly still require clinical trials outcome testing. However, lacking a formal regulatory approval process, some are already in use. SUMMARY Plans for long-term management of ICU survivors must account for the development of a holistic follow-up system that incorporates AI across multiple platforms. A tiered post-ICU screening program may be established wherein AI tools managed by ICU follow-up clinics provide appropriate assistance without human intervention in cases with less pathology and refer severe cases to expert treatment.
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Affiliation(s)
- Itay Zahavi
- Bruce and Ruth Rappaport Faculty of Medicine, Technion - Israel Institute of Technology Haifa
| | - Itamar Ben Shitrit
- Joyce and Irving Goldman Medical School and Clinical Research Center, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva
| | - Sharon Einav
- Maccabi Healthcare System, Sharon Region, and Hebrew University Faculty of Medicine, Jerusalem, Israel
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di Biase L, Pecoraro PM, Pecoraro G, Shah SA, Di Lazzaro V. Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review. J Neurol 2024; 271:6452-6470. [PMID: 39143345 DOI: 10.1007/s00415-024-12611-x] [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: 06/12/2024] [Revised: 07/22/2024] [Accepted: 07/24/2024] [Indexed: 08/16/2024]
Abstract
BACKGROUND The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson's disease diagnosis. METHODS Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between December, 2007 and July, 2023, using a search string combining "Parkinson's disease" AND ("healthy" or "control") AND "diagnosis", within the Groups and Outcome domains. Additional search terms included "Algorithm", "Technology" and "Performance". RESULTS From 89 identified studies, 47 met the inclusion criteria based on the search string and four additional studies were included based on the Authors' expertise. Gait emerged as the most common parameter analysed by machine learning models, with Support Vector Machines as the prevalent algorithm. The results suggest promising accuracy with complex algorithms like Random Forest, Support Vector Machines, and K-Nearest Neighbours. DISCUSSION Despite the promise shown by machine learning algorithms, real-world applications may still face limitations. This review suggests that integrating machine learning with wearable sensors has the potential to improve Parkinson's disease diagnosis. These tools could provide clinicians with objective data, potentially aiding in earlier detection.
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Affiliation(s)
- Lazzaro di Biase
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy.
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy.
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128, Rome, Italy.
| | - Pasquale Maria Pecoraro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | | | | | - Vincenzo Di Lazzaro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy
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Bonato P, Feipel V, Corniani G, Arin-Bal G, Leardini A. Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience. Gait Posture 2024; 113:191-203. [PMID: 38917666 DOI: 10.1016/j.gaitpost.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/30/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA). RESEARCH QUESTION How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled? METHODS The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects. RESULTS FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA. SIGNIFICANCE We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.
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Affiliation(s)
- Paolo Bonato
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Véronique Feipel
- Laboratory of Functional Anatomy, Faculty of Motor Sciences, Laboratory of Anatomy, Biomechanics and Organogenesis, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium
| | - Giulia Corniani
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Gamze Arin-Bal
- Faculty of Physical Therapy and Rehabilitation, Hacettepe University, Ankara, Turkey; Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | - Alberto Leardini
- Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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Dong Y, Noh HY. Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations. SENSORS (BASEL, SWITZERLAND) 2024; 24:2496. [PMID: 38676114 PMCID: PMC11053483 DOI: 10.3390/s24082496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/05/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024]
Abstract
Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.
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Affiliation(s)
- Yiwen Dong
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA;
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Alhazmi AK, Alanazi MA, Alshehry AH, Alshahry SM, Jaszek J, Djukic C, Brown A, Jackson K, Chodavarapu VP. Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine. SENSORS (BASEL, SWITZERLAND) 2024; 24:268. [PMID: 38203130 PMCID: PMC10781319 DOI: 10.3390/s24010268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/13/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024]
Abstract
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients' privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR.
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Affiliation(s)
- Abdullah K. Alhazmi
- Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (A.K.A.); (A.H.A.); (S.M.A.)
| | - Mubarak A. Alanazi
- Electrical Engineering Department, Jubail Industrial College, Royal Commission for Jubail and Yanbu, Jubail Industrial City 31961, Saudi Arabia;
| | - Awwad H. Alshehry
- Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (A.K.A.); (A.H.A.); (S.M.A.)
| | - Saleh M. Alshahry
- Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (A.K.A.); (A.H.A.); (S.M.A.)
| | - Jennifer Jaszek
- Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (J.J.); (C.D.); (A.B.); (K.J.)
| | - Cameron Djukic
- Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (J.J.); (C.D.); (A.B.); (K.J.)
| | - Anna Brown
- Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (J.J.); (C.D.); (A.B.); (K.J.)
| | - Kurt Jackson
- Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (J.J.); (C.D.); (A.B.); (K.J.)
| | - Vamsy P. Chodavarapu
- Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (A.K.A.); (A.H.A.); (S.M.A.)
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di Biase L, Pecoraro PM, Carbone SP, Caminiti ML, Di Lazzaro V. Levodopa-Induced Dyskinesias in Parkinson's Disease: An Overview on Pathophysiology, Clinical Manifestations, Therapy Management Strategies and Future Directions. J Clin Med 2023; 12:4427. [PMID: 37445461 DOI: 10.3390/jcm12134427] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/18/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023] Open
Abstract
Since its first introduction, levodopa has become the cornerstone for the treatment of Parkinson's disease and remains the leading therapeutic choice for motor control therapy so far. Unfortunately, the subsequent appearance of abnormal involuntary movements, known as dyskinesias, is a frequent drawback. Despite the deep knowledge of this complication, in terms of clinical phenomenology and the temporal relationship during a levodopa regimen, less is clear about the pathophysiological mechanisms underpinning it. As the disease progresses, specific oscillatory activities of both motor cortical and basal ganglia neurons and variation in levodopa metabolism, in terms of the dopamine receptor stimulation pattern and turnover rate, underlie dyskinesia onset. This review aims to provide a global overview on levodopa-induced dyskinesias, focusing on pathophysiology, clinical manifestations, therapy management strategies and future directions.
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Affiliation(s)
- Lazzaro di Biase
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Rome, Italy
| | - Pasquale Maria Pecoraro
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Simona Paola Carbone
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Maria Letizia Caminiti
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Vincenzo Di Lazzaro
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
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Quantitative High Density EEG Brain Connectivity Evaluation in Parkinson's Disease: The Phase Locking Value (PLV). J Clin Med 2023; 12:jcm12041450. [PMID: 36835985 PMCID: PMC9967371 DOI: 10.3390/jcm12041450] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
INTRODUCTION The present study explores brain connectivity in Parkinson's disease (PD) and in age matched healthy controls (HC), using quantitative EEG analysis, at rest and during a motor tasks. We also evaluated the diagnostic performance of the phase locking value (PLV), a measure of functional connectivity, in differentiating PD patients from HCs. METHODS High-density, 64-channels, EEG data from 26 PD patients and 13 HC were analyzed. EEG signals were recorded at rest and during a motor task. Phase locking value (PLV), as a measure of functional connectivity, was evaluated for each group in a resting state and during a motor task for the following frequency bands: (i) delta: 2-4 Hz; (ii) theta: 5-7 Hz; (iii) alpha: 8-12 Hz; beta: 13-29 Hz; and gamma: 30-60 Hz. The diagnostic performance in PD vs. HC discrimination was evaluated. RESULTS Results showed no significant differences in PLV connectivity between the two groups during the resting state, but a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. Comparing the resting state versus the motor task for each group, only HCs showed a higher PLV connectivity in the delta band during motor task. A ROC curve analysis for HC vs. PD discrimination, showed an area under the ROC curve (AUC) of 0.75, a sensitivity of 100%, and a negative predictive value (NPV) of 100%. CONCLUSIONS The present study evaluated the brain connectivity through quantitative EEG analysis in Parkinson's disease versus healthy controls, showing a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. This neurophysiology biomarkers showed the potentiality to be explored in future studies as a potential screening biomarker for PD patients.
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