{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T03:26:40Z","timestamp":1792294000870,"version":"4.4.0"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T00:00:00Z","timestamp":1674950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/diagnostics13030492","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T08:28:23Z","timestamp":1675067303000},"page":"492","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques"],"prefix":"10.3390","volume":"13","author":[{"given":"Marwa","family":"Elseddik","sequence":"first","affiliation":[{"name":"Department of the Robotics and Internet Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El Sheikh 33516, Egypt"},{"name":"Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6917-7873","authenticated-orcid":false,"given":"Reham R.","family":"Mostafa","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6618-0479","authenticated-orcid":false,"given":"Ahmed","family":"Elashry","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computers and Information, Kafrelsheiksh University, Kafr El Sheikh 33516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8177-9439","authenticated-orcid":false,"given":"Nora","family":"El-Rashidy","sequence":"additional","affiliation":[{"name":"Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafr El Sheikh 33516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9705-1477","authenticated-orcid":false,"given":"Shaker","family":"El-Sappagh","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Galala University, Suez 43511, Egypt"},{"name":"Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shimaa","family":"Elgamal","sequence":"additional","affiliation":[{"name":"Department of Neuropsychiatry, Faculty of Medicine, Kafrelsheiksh University, Kafr El Sheikh 33516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Aboelfetouh","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt"},{"name":"Delta Higher Institute for Management and Accounting Information Systems, Mansoura 35511, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hazem","family":"El-Bakry","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/S0028-3843(14)60122-5","article-title":"Sonographic diagnosis of carpal tunnel syndrome--diagnostic value of the triangular cross-section sign","volume":"45","year":"2011","journal-title":"Neurol. Neurochir. Pol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"417","DOI":"10.4103\/aer.AER_104_19","article-title":"Dexamethasone versus Hyaluronidase as an Adjuvant to Local Anesthetics in the Ultrasound-guided Hydrodissection of the Median Nerve for the Treatment of Carpal Tunnel Syndrome Patients","volume":"13","author":"Alsaeid","year":"2019","journal-title":"Anesth. Essays Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1186\/s12891-015-0685-0","article-title":"Association between work-related biomechanical risk factors and the occurrence of carpal tunnel syndrome: An overview of systematic reviews and a meta-analysis of current research","volume":"16","author":"Kozak","year":"2015","journal-title":"BMC Musculoskelet. Disord."},{"key":"ref_4","first-page":"34","article-title":"Carpal Tunnel Syndrome, Generalities, Management and Treatment","volume":"8","author":"Cabrera","year":"2022","journal-title":"EPRA Int. J. Multidiscip. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kudo, T., Yoshii, Y., Hara, Y., Ogawa, T., and Ishii, T. (2022). Clinical Relevance of Ultrasonographic and Electrophysiological Findings of the Median Nerve in Unilateral Carpal Tunnel Syndrome Patients. Diagnostics, 12.","DOI":"10.3390\/diagnostics12112799"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3603","DOI":"10.1007\/s00521-021-06631-1","article-title":"Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning","volume":"34","author":"Abuhmed","year":"2022","journal-title":"Neural. Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s13760-018-0963-3","article-title":"Median nerve ultrasound in carpal tunnel syndrome with normal electrodiagnostic tests","volume":"120","author":"Ersoy","year":"2020","journal-title":"Acta Neurol. Belg."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.cnp.2017.07.004","article-title":"Standard median nerve ultrasound in carpal tunnel syndrome: A retrospective review of 1,021 cases","volume":"2","author":"Billakota","year":"2017","journal-title":"Clin. Neurophysiol. Pract."},{"key":"ref_9","first-page":"1","article-title":"Ultrasonographical Evaluation of the Median Nerve Mobility in Carpal Tunnel Syndrome: A Systematic Review and Meta-Analysis","volume":"1","author":"Lin","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1002\/mus.23894","article-title":"Electromyography and neuromuscular disorders: Clinical-electrophysiologic correlations, 3rd Edition edited by David C. Preston and Barbara E. Shapiro, 664 pp., Elsevier Saunders, 2012, $199","volume":"48","author":"Varma","year":"2013","journal-title":"Muscle Nerve"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1007\/s00256-014-1929-z","article-title":"Diagnostic criteria of carpal tunnel syndrome using high-resolution ultrasonography: Correlation with nerve conduction studies","volume":"43","author":"Ooi","year":"2014","journal-title":"Skeletal Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109518","DOI":"10.1016\/j.ejrad.2020.109518","article-title":"Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: A comparison with radiologists\u2019 assessment","volume":"136","author":"Faeghi","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nguyen, D.K., Lan, C.H., and Chan, C.L. (2021). Deep ensemble learning approaches in healthcare to enhance the prediction and diagnosing performance: The workflows, deployments, and surveys on the statistical, image-based, and sequential datasets. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182010811"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ullah, N., Khan, J.A., El-sappagh, S., and El-rashidy, N. (2023). A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network. Diagnostics, 13.","DOI":"10.3390\/diagnostics13010162"},{"key":"ref_15","first-page":"2901","article-title":"Incremental Learning Framework for Mining Big Data Stream","volume":"71","author":"Eisa","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1055\/s-0040-1710412","article-title":"Can the Boston Carpal Tunnel Syndrome Questionnaire Be Used as a Screening Tool among a Potentially High-Risk Population in Singapore?","volume":"14","author":"Sirisena","year":"2022","journal-title":"J. Hand Microsurg."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1016\/j.ultrasmedbio.2022.06.011","article-title":"Deep Learning Estimation of Median Nerve Volume Using Ultrasound Imaging in a Human Cadaver Model","volume":"48","author":"Kuroiwa","year":"2022","journal-title":"Ultrasound Med. Biol."},{"key":"ref_18","first-page":"3025","article-title":"Learning-Based Median Nerve Segmentation from Ultrasound Images for Carpal Tunnel Syndrome Evaluation","volume":"2021","author":"Fiorentino","year":"2021","journal-title":"Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s11416-009-0120-x","article-title":"Fast virus detection by using high speed time delay neural networks","volume":"6","year":"2010","journal-title":"J. Comput. Virol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1227\/NEU.0000000000001749","article-title":"Predicting Clinically Relevant Patient-Reported Symptom Improvement After Carpal Tunnel Release: A Machine Learning Approach","volume":"90","author":"Hoogendam","year":"2022","journal-title":"Neurosurgery"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1007\/s10278-022-00661-4","article-title":"Deep CTS: A Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome","volume":"35","author":"Zhou","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1186\/s13075-022-02729-6","article-title":"Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level","volume":"24","author":"Smerilli","year":"2022","journal-title":"Arthritis Res. Ther."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1016\/j.ultrasmedbio.2022.05.030","article-title":"Using deep learning for ultrasound images to diagnose carpal tunnel syndrome with high accuracy","volume":"10","author":"Shinohara","year":"2022","journal-title":"Ultrasound Med. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"11435","DOI":"10.1007\/s00500-022-07420-1","article-title":"Prediction of gestational diabetes based on explainable deep learning and fog computing","volume":"26","author":"Talaat","year":"2022","journal-title":"Soft Comput."},{"key":"ref_25","unstructured":"Kelceoglu, B. (2023). Preventing carpal tunnel syndrome: A Product Design Study to Assist Carpal Tunnel Syndrome Prevention Techniques. [Ph.D. Thesis, The Ohio State University]."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yoshii, Y., Zhao, C., and Amadio, P.C. (2020). Recent Advances in Ultrasound Diagnosis of Carpal Tunnel Syndrome. Diagnostics, 10.","DOI":"10.3390\/diagnostics10080596"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.inffus.2009.06.001","article-title":"An Efficient Algorithm for Pattern Detection Using Combined Classifiers and Data Fusion","volume":"11","year":"2010","journal-title":"Inf. Fusion"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17464","DOI":"10.1038\/s41598-021-97043-7","article-title":"Machine learning-based approach for disease severity classification of carpal tunnel syndrome","volume":"11","author":"Park","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tsamis, K.I., Kontogiannis, P., Gourgiotis, I., Ntabos, S., Sarmas, I., and Manis, G. (2021). Automatic electrodiagnosis of carpal tunnel syndrome using machine learning. Bioengineering, 8.","DOI":"10.3390\/bioengineering8110181"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"101687","DOI":"10.1016\/j.compmedimag.2019.101687","article-title":"MNT-DeepSL: Median nerve tracking from carpal tunnel ultrasound images with deep similarity learning and analysis on continuous wrist motions","volume":"80","author":"Wang","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shinohara, I., Inui, A., Mifune, Y., Nishimoto, H., Yamaura, K., Mukohara, S., Yoshikawa, T., Kato, T., Furukawa, T., and Hoshino, Y. (2022). Diagnosis of Cubital Tunnel Syndrome Using Deep Learning on Ultrasonographic Images. Diagnostics, 12.","DOI":"10.3390\/diagnostics12030632"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.1016\/j.ultrasmedbio.2021.03.018","article-title":"Automated Segmentation of the Median Nerve in the Carpal Tunnel using U-Net","volume":"47","author":"Festen","year":"2021","journal-title":"Ultrasound Med. Biol."},{"key":"ref_33","unstructured":"Hafiane, A., Vieyres, P., and Delbos, A. (2017). Deep learning with spatiotemporal consistency for nerve segmentation in ultrasound images. arXiv."},{"key":"ref_34","first-page":"4","article-title":"Clinical Utility of Boston-CTS and Six-Item CTS Questionnaires in Carpal Tunnel Syndrome Associated with Diabetic Polyneuropathy","volume":"13","author":"Ciobanu","year":"2023","journal-title":"Diagnostics"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1002\/mus.26297","article-title":"A prognostic model for the patient-reported outcome of surgical treatment of carpal tunnel syndrome","volume":"58","author":"Bowman","year":"2018","journal-title":"Muscle and Nerve"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e4279","DOI":"10.1097\/GOX.0000000000004279","article-title":"Developing Machine Learning Algorithms to Support Patient-centered, Value-based Carpal Tunnel Decompression Surgery","volume":"10","author":"Harrison","year":"2022","journal-title":"Plast. Reconstr. Surg. Glob. Open"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.patrec.2020.02.020","article-title":"Diagnosis of carpal tunnel syndrome: A comparative study of shear wave elastography, morphometry and artificial intelligence techniques","volume":"133","author":"Ardakani","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1080\/02841850802438520","article-title":"Reproducibility of sonographic measurements of carotid intima-media thickness","volume":"49","author":"Serrano","year":"2008","journal-title":"Acta Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1093\/rheumatology\/keh190","article-title":"Ultrasonography versus nerve conduction study in patients with carpal tunnel syndrome: Substantive or complementary tests?","volume":"43","author":"Aty","year":"2004","journal-title":"Rheumatology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"71","DOI":"10.7556\/jaoa.2005.105.2.71","article-title":"Evaluating the Patient with Peripheral Nervous System Complaints","volume":"105","author":"Scott","year":"2005","journal-title":"J. Osteopath. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1002\/mpr.329","article-title":"Multiple imputation by chained equations: What is it and how does it work?","volume":"20","author":"Azur","year":"2011","journal-title":"Int. J. Methods Psychiatr. Res."},{"key":"ref_42","first-page":"30","article-title":"Multiple imputation with multivariate imputation by chained equation (MICE) package","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann. Transl. Med."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Buuren, S., and Groothuis-Oudshoorn, C. (2011). MICE: Multivariate Imputation by Chained Equations in R. J. Stat. Softw., 45.","DOI":"10.18637\/jss.v045.i03"},{"key":"ref_44","first-page":"22","article-title":"A Study on Data Scaling Methods for Machine Learning","volume":"1","author":"Sharma","year":"2022","journal-title":"Int. J. Glob. Acad. Sci. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1016\/j.imavis.2007.02.001","article-title":"New Fast Normalized Neural Networks for Pattern Detection","volume":"25","author":"Mastorakis","year":"2007","journal-title":"Image Vis. Comput. J."},{"key":"ref_46","first-page":"151","article-title":"Fast Normalized Neural Processors for Pattern Detection Based on Cross Correlation Implemented in the Frequency Domain","volume":"38","author":"Zhao","year":"2006","journal-title":"J. Res. Pract. Inf. Technol."},{"key":"ref_47","first-page":"573","article-title":"A New Fast Forecasting Technique using High Speed Neural Networks","volume":"4","author":"Mastorakis","year":"2008","journal-title":"WSEAS Trans. Signal Process."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/3\/492\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:19:01Z","timestamp":1760120341000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/3\/492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,29]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["diagnostics13030492"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13030492","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,29]]}}}