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Bakalis D, Kontogiannis P, Ntais E, Simos YV, Tsamis KI, Manis G. Carpal Tunnel Syndrome Automated Diagnosis: A Motor vs. Sensory Nerve Conduction-Based Approach. Bioengineering (Basel) 2024; 11:175. [PMID: 38391661 PMCID: PMC10886232 DOI: 10.3390/bioengineering11020175] [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: 12/20/2023] [Revised: 01/27/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
The objective of this study was to evaluate the effectiveness of machine learning classification techniques applied to nerve conduction studies (NCS) of motor and sensory signals for the automatic diagnosis of carpal tunnel syndrome (CTS). Two methodologies were tested. In the first methodology, motor signals recorded from the patients' median nerve were transformed into time-frequency spectrograms using the short-time Fourier transform (STFT). These spectrograms were then used as input to a deep two-dimensional convolutional neural network (CONV2D) for classification into two categories: patients and controls. In the second methodology, sensory signals from the patients' median and ulnar nerves were subjected to multilevel wavelet decomposition (MWD), and statistical and non-statistical features were extracted from the decomposed signals. These features were utilized to train and test classifiers. The classification target was set to three categories: normal subjects (controls), patients with mild CTS, and patients with moderate to severe CTS based on conventional electrodiagnosis results. The results of the classification analysis demonstrated that both methodologies surpassed previous attempts at automatic CTS diagnosis. The classification models utilizing the motor signals transformed into time-frequency spectrograms exhibited excellent performance, with average accuracy of 94%. Similarly, the classifiers based on the sensory signals and the extracted features from multilevel wavelet decomposition showed significant accuracy in distinguishing between controls, patients with mild CTS, and patients with moderate to severe CTS, with accuracy of 97.1%. The findings highlight the efficacy of incorporating machine learning algorithms into the diagnostic processes of NCS, providing a valuable tool for clinicians in the diagnosis and management of neuropathies such as CTS.
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Affiliation(s)
- Dimitrios Bakalis
- Department of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece
| | - Prokopis Kontogiannis
- Department of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece
| | - Evangelos Ntais
- Department of Neurology, University Hospital of Ioannina, 45110 Ioannina, Greece
| | - Yannis V Simos
- Department of Physiology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece
| | - Konstantinos I Tsamis
- Department of Neurology, University Hospital of Ioannina, 45110 Ioannina, Greece
- Department of Physiology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece
| | - George Manis
- Department of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece
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Elseddik M, Alnowaiser K, Mostafa RR, Elashry A, El-Rashidy N, Elgamal S, Aboelfetouh A, El-Bakry H. Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome. Diagnostics (Basel) 2023; 13:3211. [PMID: 37892032 PMCID: PMC10606231 DOI: 10.3390/diagnostics13203211] [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: 08/31/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 10/29/2023] Open
Abstract
Carpal tunnel syndrome (CTS) is a prevalent medical condition resulting from compression of the median nerve in the hand, often caused by overuse or age-related factors. In this study, a total of 160 patients participated, including 80 individuals with CTS presenting varying levels of severity across different age groups. Numerous studies have explored the use of machine learning (ML) and deep learning (DL) techniques for CTS diagnosis. However, further research is required to fully leverage the potential of artificial intelligence (AI) technology in CTS diagnosis, addressing the challenges and limitations highlighted in the existing literature. In our work, we propose a novel approach for CTS diagnosis, prediction, and monitoring disease progression. The proposed framework consists of three main layers. Firstly, we employ three distinct DL models for CTS diagnosis. Through our experiments, the proposed approach demonstrates superior performance across multiple evaluation metrics, with an accuracy of 0.969%, precision of 0.982%, and recall of 0.963%. The second layer focuses on predicting the cross-sectional area (CSA) at 1, 3, and 6 months using ML models, aiming to forecast disease progression during therapy. The best-performing model achieves an accuracy of 0.9522, an R2 score of 0.667, a mean absolute error (MAE) of 0.0132, and a median squared error (MdSE) of 0.0639. The highest predictive performance is observed after 6 months. The third layer concentrates on assessing significant changes in the patients' health status through statistical tests, including significance tests, the Kruskal-Wallis test, and a two-way ANOVA test. These tests aim to determine the effect of injections on CTS treatment. The results reveal a highly significant reduction in symptoms, as evidenced by scores from the Symptom Severity Scale and Functional Status Scale, as well as a decrease in CSA after 1, 3, and 6 months following the injection. SHAP is then utilized to provide an understandable explanation of the final prediction. Overall, our study presents a comprehensive approach for CTS diagnosis, prediction, and monitoring, showcasing promising results in terms of accuracy, precision, and recall for CTS diagnosis, as well as effective prediction of disease progression and evaluation of treatment effectiveness through statistical analysis.
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Affiliation(s)
- Marwa Elseddik
- Department of the Robotics and Internet Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Khaled Alnowaiser
- College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
| | - Reham R Mostafa
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
- Research Institute of Sciences and Engineering (RISE), University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Ahmed Elashry
- Department of Information Systems, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
| | - Nora El-Rashidy
- Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
| | - Shimaa Elgamal
- Department of Neuropsychiatry, Faculty of Medicine, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
| | - Ahmed Aboelfetouh
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
- Delta Higher Institute for Management and Accounting Information Systems, Mansoura 35511, Egypt
| | - Hazem El-Bakry
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
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Elseddik M, Mostafa RR, Elashry A, El-Rashidy N, El-Sappagh S, Elgamal S, Aboelfetouh A, El-Bakry H. Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques. Diagnostics (Basel) 2023; 13:diagnostics13030492. [PMID: 36766597 PMCID: PMC9914125 DOI: 10.3390/diagnostics13030492] [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: 12/26/2022] [Revised: 01/12/2023] [Accepted: 01/20/2023] [Indexed: 01/31/2023] Open
Abstract
Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman's correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.
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Affiliation(s)
- Marwa Elseddik
- Department of the Robotics and Internet Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El Sheikh 33516, Egypt
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Reham R. Mostafa
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Elashry
- Department of Information Systems, Faculty of Computers and Information, Kafrelsheiksh University, Kafr El Sheikh 33516, Egypt
| | - Nora El-Rashidy
- Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafr El Sheikh 33516, Egypt
- Correspondence: (N.E.-R.); (S.E.-S.)
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 43511, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
- Correspondence: (N.E.-R.); (S.E.-S.)
| | - Shimaa Elgamal
- Department of Neuropsychiatry, Faculty of Medicine, Kafrelsheiksh University, Kafr El Sheikh 33516, Egypt
| | - Ahmed Aboelfetouh
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
- Delta Higher Institute for Management and Accounting Information Systems, Mansoura 35511, Egypt
| | - Hazem El-Bakry
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
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Ray J, Wijesekera L, Cirstea S. Machine learning and clinical neurophysiology. J Neurol 2022; 269:6678-6684. [PMID: 35907045 DOI: 10.1007/s00415-022-11283-9] [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: 06/13/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 11/29/2022]
Abstract
Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions.
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Affiliation(s)
- Julian Ray
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK.
| | - Lokesh Wijesekera
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
| | - Silvia Cirstea
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
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