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Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques

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 c...

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Autores principales: Elseddik, Marwa, Mostafa, Reham R., Elashry, Ahmed, El-Rashidy, Nora, El-Sappagh, Shaker, Elgamal, Shimaa, Aboelfetouh, Ahmed, El-Bakry, Hazem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914125/
https://www.ncbi.nlm.nih.gov/pubmed/36766597
http://dx.doi.org/10.3390/diagnostics13030492
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author Elseddik, Marwa
Mostafa, Reham R.
Elashry, Ahmed
El-Rashidy, Nora
El-Sappagh, Shaker
Elgamal, Shimaa
Aboelfetouh, Ahmed
El-Bakry, Hazem
author_facet Elseddik, Marwa
Mostafa, Reham R.
Elashry, Ahmed
El-Rashidy, Nora
El-Sappagh, Shaker
Elgamal, Shimaa
Aboelfetouh, Ahmed
El-Bakry, Hazem
author_sort Elseddik, Marwa
collection PubMed
description 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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spelling pubmed-99141252023-02-11 Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques Elseddik, Marwa Mostafa, Reham R. Elashry, Ahmed El-Rashidy, Nora El-Sappagh, Shaker Elgamal, Shimaa Aboelfetouh, Ahmed El-Bakry, Hazem Diagnostics (Basel) Article 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. MDPI 2023-01-29 /pmc/articles/PMC9914125/ /pubmed/36766597 http://dx.doi.org/10.3390/diagnostics13030492 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Elseddik, Marwa
Mostafa, Reham R.
Elashry, Ahmed
El-Rashidy, Nora
El-Sappagh, Shaker
Elgamal, Shimaa
Aboelfetouh, Ahmed
El-Bakry, Hazem
Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques
title Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques
title_full Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques
title_fullStr Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques
title_full_unstemmed Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques
title_short Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques
title_sort predicting cts diagnosis and prognosis based on machine learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914125/
https://www.ncbi.nlm.nih.gov/pubmed/36766597
http://dx.doi.org/10.3390/diagnostics13030492
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