1
|
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 2024:10.1007/s00415-024-12611-x. [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] [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.
Collapse
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
| |
Collapse
|
2
|
Khalil RM, Shulman LM, Gruber-Baldini AL, Shakya S, Fenderson R, Van Hoven M, Hausdorff JM, von Coelln R, Cummings MP. Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2024; 24:4983. [PMID: 39124030 PMCID: PMC11314738 DOI: 10.3390/s24154983] [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: 06/19/2024] [Revised: 07/21/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.
Collapse
Affiliation(s)
- Rana M. Khalil
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
| | - Lisa M. Shulman
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Ann L. Gruber-Baldini
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Sunita Shakya
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Rebecca Fenderson
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Maxwell Van Hoven
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6492416, Israel;
- Department of Physical Therapy, Faculty of Medicine & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
| | - Rainer von Coelln
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Michael P. Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
| |
Collapse
|
3
|
Reddy A, Reddy RP, Roghani AK, Garcia RI, Khemka S, Pattoor V, Jacob M, Reddy PH, Sehar U. Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements. Ageing Res Rev 2024; 99:102410. [PMID: 38972602 DOI: 10.1016/j.arr.2024.102410] [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: 10/02/2023] [Accepted: 07/04/2024] [Indexed: 07/09/2024]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder, globally affecting men and women at an exponentially growing rate, with currently no cure. Disease progression starts when dopaminergic neurons begin to die. In PD, the loss of neurotransmitter, dopamine is responsible for the overall communication of neural cells throughout the body. Clinical symptoms of PD are slowness of movement, involuntary muscular contractions, speech & writing changes, lessened automatic movement, and chronic tremors in the body. PD occurs in both familial and sporadic forms and modifiable and non-modifiable risk factors and socioeconomic conditions cause PD. Early detectable diagnostics and treatments have been developed in the last several decades. However, we still do not have precise early detectable biomarkers and therapeutic agents/drugs that prevent and/or delay the disease process. Recently, artificial intelligence (AI) science and machine learning tools have been promising in identifying early detectable markers with a greater rate of accuracy compared to past forms of treatment and diagnostic processes. Artificial intelligence refers to the intelligence exhibited by machines or software, distinct from the intelligence observed in humans that is based on neural networks in a form and can be used to diagnose the longevity and disease severity of disease. The term Machine Learning or Neural Networks is a blanket term used to identify an emerging technology that is created to work in the way of a "human brain" using many intertwined neurons to achieve the same level of raw intelligence as that of a brain. These processes have been used for neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, to assess the severity of the patient's condition. In the current article, we discuss the prevalence and incidence of PD, and currently available diagnostic biomarkers and therapeutic strategies. We also highlighted currently available artificial intelligence science and machine learning tools and their applications to detect disease and develop therapeutic interventions.
Collapse
Affiliation(s)
- Aananya Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Lubbock High School, Lubbock, TX 79401, USA.
| | - Ruhananhad P Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Lubbock High School, Lubbock, TX 79401, USA.
| | - Aryan Kia Roghani
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Frenship High School, Lubbock, TX 79382, USA.
| | - Ricardo Isaiah Garcia
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Sachi Khemka
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Vasanthkumar Pattoor
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; University of South Florida, Tampa, FL 33620, USA.
| | - Michael Jacob
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Biology, The University of Texas at San Antonio, San Antonio, TX 78249, USA.
| | - P Hemachandra Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Nutritional Sciences Department, College of Human Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Public Health Department of Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department pf Speech, Language and Hearing Services, School Health Professions, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Ujala Sehar
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| |
Collapse
|
4
|
Zampogna A, Borzì L, Soares C, Demrozi F. Editorial: High-tech personalized healthcare in movement disorders. Front Neurol 2024; 15:1452612. [PMID: 39015322 PMCID: PMC11250080 DOI: 10.3389/fneur.2024.1452612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/18/2024] Open
Affiliation(s)
- Alessandro Zampogna
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed Institute, Pozzilli, Italy
| | - Luigi Borzì
- Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy
| | - Carolina Soares
- Department of Neurology, Unidade Local de Saúde São João, E.P.E, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine of University of Porto, Alameda Prof. Hernâni Monteiro, Porto, Portugal
| | - Florenc Demrozi
- Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway
| |
Collapse
|
5
|
Huang Y, Roy N, Dhar E, Upadhyay U, Kabir MA, Uddin M, Tseng CL, Syed-Abdul S. Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information. Cancers (Basel) 2023; 15:cancers15082232. [PMID: 37190161 DOI: 10.3390/cancers15082232] [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: 03/07/2023] [Revised: 04/07/2023] [Accepted: 04/07/2023] [Indexed: 05/17/2023] Open
Abstract
(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients. (2) Aims and objectives: In this study, we aimed to explore the potential of using deep learning (DL) model approaches to predict the survival outcomes of end-stage cancer patients. Furthermore, we also aimed to compare the accuracy of our proposed activity monitoring and survival prediction model with traditional prognostic tools, such as the Karnofsky Performance Scale (KPS) and the Palliative Performance Index (PPI). (3) Method: This study recruited 78 patients from the Taipei Medical University Hospital's palliative care unit, with 66 (39 male and 27 female) patients eventually being included in our DL model for predicting their survival outcomes. (4) Results: The KPS and PPI demonstrated an overall accuracy of 0.833 and 0.615, respectively. In comparison, the actigraphy data exhibited a higher accuracy at 0.893, while the accuracy of the wearable data combined with clinical information was even better, at 0.924. (5) Conclusion: Our study highlights the significance of incorporating clinical data alongside wearable sensors to predict prognosis. Our findings suggest that 48 h of data is sufficient for accurate predictions. The integration of wearable technology and the prediction model in palliative care has the potential to improve decision making for healthcare providers and can provide better support for patients and their families. The outcomes of this study can possibly contribute to the development of personalized and patient-centered end-of-life care plans in clinical practice.
Collapse
Affiliation(s)
- Yaoru Huang
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei 110, Taiwan
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
| | - Nidita Roy
- Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh
| | - Eshita Dhar
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
| | - Umashankar Upadhyay
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
- Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Solan 173229, Himachal Pradesh, India
| | - Muhammad Ashad Kabir
- School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2678, Australia
| | - Mohy Uddin
- Research Quality Management Section, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard-Health Affairs, Riyadh 11481, Saudi Arabia
| | - Ching-Li Tseng
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
- International Ph.D. Program in Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
| |
Collapse
|