Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning
Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/8/11/181 |
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| author | Konstantinos I. Tsamis Prokopis Kontogiannis Ioannis Gourgiotis Stefanos Ntabos Ioannis Sarmas George Manis |
| author_facet | Konstantinos I. Tsamis Prokopis Kontogiannis Ioannis Gourgiotis Stefanos Ntabos Ioannis Sarmas George Manis |
| author_sort | Konstantinos I. Tsamis |
| collection | DOAJ |
| description | Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeuropathy and decision making about carpal tunnel syndrome. The study included 38 volunteers, examined prospectively. The purpose was to investigate the possibility of automatically detecting the median nerve mononeuropathy based on common electrodiagnostic criteria, used in everyday clinical practice, as well as new features selected based on physiology and mathematics. Machine learning techniques were used to combine the examined characteristics for a stable and accurate diagnosis. Automatic electrodiagnosis reached an accuracy of 95% compared to the standard neurophysiological diagnosis of the physicians with nerve conduction studies and 89% compared to the clinical diagnosis. The results show that the automatic detection of carpal tunnel syndrome is possible and can be employed in decision making, excluding human error. It is also shown that the novel features investigated can be used for the detection of the syndrome, complementary to the commonly used ones, increasing the accuracy of the method. |
| first_indexed | 2024-03-10T05:42:20Z |
| format | Article |
| id | doaj.art-8462da5c4d0546169eb70b8145bcc595 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T05:42:20Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-8462da5c4d0546169eb70b8145bcc5952023-11-22T22:26:47ZengMDPI AGBioengineering2306-53542021-11-0181118110.3390/bioengineering8110181Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine LearningKonstantinos I. Tsamis0Prokopis Kontogiannis1Ioannis Gourgiotis2Stefanos Ntabos3Ioannis Sarmas4George Manis5Department of Neurology, University Hospital of Ioannina, 45110 Ioannina, GreeceDepartment of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, GreeceDepartment of Neurology, University Hospital of Ioannina, 45110 Ioannina, GreeceDepartment of Neurology, University Hospital of Ioannina, 45110 Ioannina, GreeceDepartment of Neurology, University Hospital of Ioannina, 45110 Ioannina, GreeceDepartment of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, GreeceRecent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeuropathy and decision making about carpal tunnel syndrome. The study included 38 volunteers, examined prospectively. The purpose was to investigate the possibility of automatically detecting the median nerve mononeuropathy based on common electrodiagnostic criteria, used in everyday clinical practice, as well as new features selected based on physiology and mathematics. Machine learning techniques were used to combine the examined characteristics for a stable and accurate diagnosis. Automatic electrodiagnosis reached an accuracy of 95% compared to the standard neurophysiological diagnosis of the physicians with nerve conduction studies and 89% compared to the clinical diagnosis. The results show that the automatic detection of carpal tunnel syndrome is possible and can be employed in decision making, excluding human error. It is also shown that the novel features investigated can be used for the detection of the syndrome, complementary to the commonly used ones, increasing the accuracy of the method.https://www.mdpi.com/2306-5354/8/11/181carpal tunnel syndromeCTSfeature extractionmachine learningmedian nerve mononeuropathynerve conduction studies |
| spellingShingle | Konstantinos I. Tsamis Prokopis Kontogiannis Ioannis Gourgiotis Stefanos Ntabos Ioannis Sarmas George Manis Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning Bioengineering carpal tunnel syndrome CTS feature extraction machine learning median nerve mononeuropathy nerve conduction studies |
| title | Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning |
| title_full | Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning |
| title_fullStr | Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning |
| title_full_unstemmed | Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning |
| title_short | Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning |
| title_sort | automatic electrodiagnosis of carpal tunnel syndrome using machine learning |
| topic | carpal tunnel syndrome CTS feature extraction machine learning median nerve mononeuropathy nerve conduction studies |
| url | https://www.mdpi.com/2306-5354/8/11/181 |
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