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Umar TP, Jain N, Papageorgakopoulou M, Shaheen RS, Alsamhori JF, Muzzamil M, Kostiks A. Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis. Amyotroph Lateral Scler Frontotemporal Degener 2024; 25:425-436. [PMID: 38563056 DOI: 10.1080/21678421.2024.2334836] [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: 09/07/2023] [Revised: 03/04/2024] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS is challenging due to the absence of a specific detection test. The use of artificial intelligence (AI) can assist in the investigation and treatment of ALS. METHODS We searched seven databases for literature on the application of AI in the early diagnosis and screening of ALS in humans. The findings were summarized using random-effects summary receiver operating characteristic curve. The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools. RESULTS In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). Based on type of input for classification, the pooled sensitivity of AI models for gait, electromyography, and magnetic resonance signals was 91.2%, 92.6%, and 82.2%, respectively. The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively. CONCLUSIONS Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.
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Affiliation(s)
- Tungki Pratama Umar
- Department of Medical Profession, Faculty of Medicine, Universitas Sriwijaya, Palembang, Indonesia
| | - Nityanand Jain
- Faculty of Medicine, Riga Stradinš University, Riga, Latvia
| | | | | | | | - Muhammad Muzzamil
- Department of Public Health, Health Services Academy, Islamabad, Pakistan, and
| | - Andrejs Kostiks
- Department of Neurology, Riga East University Clinical Hospital, Riga, Latvia
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Saljuqi M, Ghaderyan P. A novel method based on matching pursuit decomposition of gait signals for Parkinson's disease, Amyotrophic lateral sclerosis and Huntington's disease detection. Neurosci Lett 2021; 761:136107. [PMID: 34256106 DOI: 10.1016/j.neulet.2021.136107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 06/20/2021] [Accepted: 07/07/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND AND OBJECTIVE An accurate detection of neurodegenerative diseases (NDDs) definitely improves the life of patients and has attracted growing attention. METHODS In this paper, a general automatic method for detection of Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD) has been proposed based on the localized time-frequency information of gait signals. The new main part of the detection method is to obtain a small set of sparse coefficients for the local representation of gait signals with appropriate time and frequency resolution. For this purpose, a hybrid feature set based on sparse matching pursuit decomposition and two sets of nonlinear and linear features has been developed. Then, principal components of the proposed feature have been analyzed using a sparse coding classifier. Results The proposed approach has achieved high average accuracy rates of 93%, 94%, and 97% for PD, ALS, and HD detection, respectively. CONCLUSIONS The obtained results have indicated that combination of time and frequency information of the gait signals through adaptive localized window length in MP makes it more efficient in comparison with the existing time, frequency or other time-frequency gait parameters. The great potential of nonlinear sparse features for PD and HD detection and linear ones for ALS detection has also been shown.
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Affiliation(s)
- Masume Saljuqi
- Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Peyvand Ghaderyan
- Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
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Fernandes F, Barbalho I, Barros D, Valentim R, Teixeira C, Henriques J, Gil P, Dourado Júnior M. Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review. Biomed Eng Online 2021; 20:61. [PMID: 34130692 PMCID: PMC8207575 DOI: 10.1186/s12938-021-00896-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 06/09/2021] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease. METHODS This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions. DISCUSSIONS Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%). CONCLUSIONS Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.
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Affiliation(s)
- Felipe Fernandes
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
| | - Ingridy Barbalho
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
| | - Daniele Barros
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
| | - Ricardo Valentim
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
| | - César Teixeira
- Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal
| | - Jorge Henriques
- Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal
| | - Paulo Gil
- Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal
| | - Mário Dourado Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
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Cuzzolin F, Sapienza M, Esser P, Saha S, Franssen MM, Collett J, Dawes H. Metric learning for Parkinsonian identification from IMU gait measurements. Gait Posture 2017; 54:127-132. [PMID: 28288333 DOI: 10.1016/j.gaitpost.2017.02.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 01/24/2017] [Accepted: 02/16/2017] [Indexed: 02/02/2023]
Abstract
Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variability across individuals, and is subject to numerous nuisance factors. Therefore, off-the-shelf Machine Learning techniques may fail to classify it with the accuracy required in clinical trials. In this paper we propose a novel framework in which IMU gait measurement sequences sampled during a 10m walk are first encoded as hidden Markov models (HMMs) to extract their dynamics and provide a fixed-length representation. Given sufficient training samples, the distance between HMMs which optimises classification performance is learned and employed in a classical Nearest Neighbour classifier. Our tests demonstrate how this technique achieves accuracy of 85.51% over a 156 people with Parkinson's with a representative range of severity and 424 typically developed adults, which is the top performance achieved so far over a cohort of such size, based on single measurement outcomes. The method displays the potential for further improvement and a wider application to distinguish other conditions.
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Affiliation(s)
- Fabio Cuzzolin
- Artificial Intelligence and Vision Group, Department of Computing and Communication Technologies, Oxford Brookes University, Wheatley Campus, Oxford OX33 1HX, UK.
| | - Michael Sapienza
- Torr Vision Group, Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
| | - Patrick Esser
- Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK.
| | - Suman Saha
- Artificial Intelligence and Vision Group, Department of Computing and Communication Technologies, Oxford Brookes University, Wheatley Campus, Oxford OX33 1HX, UK.
| | - Miss Marloes Franssen
- Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK.
| | - Johnny Collett
- Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK.
| | - Helen Dawes
- Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK.
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Zhang Z, VanSwearingen J, Brach JS, Perera S, Sejdić E. Most suitable mother wavelet for the analysis of fractal properties of stride interval time series via the average wavelet coefficient method. Comput Biol Med 2016; 80:175-184. [PMID: 27960102 DOI: 10.1016/j.compbiomed.2016.11.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Revised: 11/20/2016] [Accepted: 11/23/2016] [Indexed: 10/20/2022]
Abstract
Human gait is a complex interaction of many nonlinear systems and stride intervals exhibiting self-similarity over long time scales that can be modeled as a fractal process. The scaling exponent represents the fractal degree and can be interpreted as a "biomarker" of relative diseases. The previous study showed that the average wavelet method provides the most accurate results to estimate this scaling exponent when applied to stride interval time series. The purpose of this paper is to determine the most suitable mother wavelet for the average wavelet method. This paper presents a comparative numerical analysis of 16 mother wavelets using simulated and real fractal signals. Simulated fractal signals were generated under varying signal lengths and scaling exponents that indicate a range of physiologically conceivable fractal signals. The five candidates were chosen due to their good performance on the mean square error test for both short and long signals. Next, we comparatively analyzed these five mother wavelets for physiologically relevant stride time series lengths. Our analysis showed that the symlet 2 mother wavelet provides a low mean square error and low variance for long time intervals and relatively low errors for short signal lengths. It can be considered as the most suitable mother function without the burden of considering the signal length.
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Affiliation(s)
- Zhenwei Zhang
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Jessie VanSwearingen
- Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Jennifer S Brach
- Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Subashan Perera
- Department of Medicine, Division of Geriatrics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.
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Zeng W, Liu F, Wang Q, Wang Y, Ma L, Zhang Y. Parkinson's disease classification using gait analysis via deterministic learning. Neurosci Lett 2016; 633:268-278. [PMID: 27693437 DOI: 10.1016/j.neulet.2016.09.043] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 09/12/2016] [Accepted: 09/25/2016] [Indexed: 11/17/2022]
Abstract
Gait analysis plays an important role in maintaining the well-being of human mobility and health care, and is a valuable tool for obtaining quantitative information on motor deficits in Parkinson's disease (PD). In this paper, we propose a method to classify (diagnose) patients with PD and healthy control subjects using gait analysis via deterministic learning theory. The classification approach consists of two phases: a training phase and a classification phase. In the training phase, gait characteristics represented by the gait dynamics are derived from the vertical ground reaction forces under the usual and self-selected paces of the subjects. The gait dynamics underlying gait patterns of healthy controls and PD patients are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. The gait patterns of healthy controls and PD patients constitute a training set. In the classification phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern of a certain PD patient to be classified (diagnosed), a set of classification errors are generated. The average L1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test PD gait pattern according to the smallest error principle. When the gait patterns of 93 PD patients and 73 healthy controls are classified with five-fold cross-validation method, the accuracy, sensitivity and specificity of the results are 96.39%, 96.77% and 95.89%, respectively. Based on the results, it may be claimed that the features and the classifiers used in the present study could effectively separate the gait patterns between the groups of PD patients and healthy controls.
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Affiliation(s)
- Wei Zeng
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China.
| | - Fenglin Liu
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Qinghui Wang
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Ying Wang
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Limin Ma
- Department of Orthopaedic Surgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China
| | - Yu Zhang
- Department of Orthopaedic Surgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China
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