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Taha MA, Morren JA. The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions. Muscle Nerve 2024; 69:260-272. [PMID: 38151482 DOI: 10.1002/mus.28023] [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: 12/04/2023] [Accepted: 12/09/2023] [Indexed: 12/29/2023]
Abstract
The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.
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Affiliation(s)
- Mohamed A Taha
- Neuromuscular Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - John A Morren
- Neuromuscular Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
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Peng J, Zeng J, Lai M, Huang R, Ni D, Li Z. One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:304-314. [PMID: 38044200 DOI: 10.1016/j.ultrasmedbio.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/23/2023] [Accepted: 10/22/2023] [Indexed: 12/05/2023]
Abstract
OBJECTIVE Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS), although identification of the median nerve (MN) and diagnosis of CTS depend heavily on the expertise of examiners. In the aim of alleviating this problem, we developed a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluated its effectiveness as a computer-aided diagnostic tool. METHODS We combined real-time MN delineation, accurate biometric measurements and explainable CTS diagnosis into a unified framework, called OSA-CTSD. We then collected a total of 32,301 static images from US videos of 90 normal wrists and 40 CTS wrists for evaluation using a simplified scanning protocol. RESULTS The proposed model exhibited better segmentation and measurement performance than competing methods, with a Hausdorff distance (95th percentile) score of 7.21 px, average symmetric surface distance score of 2.64 px, Dice score of 85.78% and intersection over union score of 76.00%. In the reader study, it exhibited performance comparable to the average performance of experienced radiologists in classifying CTS and outperformed inexperienced radiologists in terms of classification metrics (e.g., accuracy score 3.59% higher and F1 score 5.85% higher). CONCLUSION Diagnostic performance of the OSA-CTSD was promising, with the advantages of real-time delineation, automation and clinical interpretability. The application of such a tool not only reduces reliance on the expertise of examiners but also can help to promote future standardization of the CTS diagnostic process, benefiting both patients and radiologists.
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Affiliation(s)
- Jiayu Peng
- Department of Ultrasound, Second People's Hospital of Shenzhen, First Affiliated Hospital of Shenzhen University, Shenzhen, China; Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
| | - Jiajun Zeng
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Manlin Lai
- Ultrasound Division, Department of Medical Imaging, University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Ruobing Huang
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Dong Ni
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Zhenzhou Li
- Department of Ultrasound, Second People's Hospital of Shenzhen, First Affiliated Hospital of Shenzhen University, Shenzhen, China; Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
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Chato L, Regentova E. Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data. J Pers Med 2023; 13:1703. [PMID: 38138930 PMCID: PMC10744730 DOI: 10.3390/jpm13121703] [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: 10/05/2023] [Revised: 12/01/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.
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Affiliation(s)
- Lina Chato
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA;
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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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Mohammadi A, Torres-Cuenca T, Mirza-Aghazadeh-Attari M, Faeghi F, Acharya UR, Abbasian Ardakani A. Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi-Center Study. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:2257-2268. [PMID: 37159483 DOI: 10.1002/jum.16244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 03/18/2023] [Accepted: 04/16/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVES Ultrasound is widely used in diagnosing carpal tunnel syndrome (CTS). However, the limitations of ultrasound in CTS detection are the lack of objective measures in the detection of nerve abnormality and the operator-dependent nature of ultrasound imaging. Therefore, in this study, we developed and proposed externally validated artificial intelligence (AI) models based on deep-radiomics features. METHODS We have used 416 median nerves from 2 countries (Iran and Colombia) for the development (112 entrapped and 112 normal nerves from Iran) and validation (26 entrapped and 26 normal nerves from Iran, and 70 entrapped and 70 normal nerves from Columbia) of our models. Ultrasound images were fed to the SqueezNet architecture to extract deep-radiomics features. Then a ReliefF method was used to select the clinically significant features. The selected deep-radiomics features were fed to 9 common machine-learning algorithms to choose the best-performing classifier. The 2 best-performing AI models were then externally validated. RESULTS Our developed model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.910 (88.46% sensitivity, 88.46% specificity) and 0.908 (84.62% sensitivity, 88.46% specificity) with support vector machine and stochastic gradient descent (SGD), respectively using the internal validation dataset. Furthermore, both models consistently performed well in the external validation dataset, and achieved an AUC of 0.890 (85.71% sensitivity, 82.86% specificity) and 0.890 (84.29% sensitivity and 82.86% specificity), with SVM and SGD models, respectively. CONCLUSION Our proposed AI models fed with deep-radiomics features performed consistently with internal and external datasets. This justifies that our proposed system can be employed for clinical use in hospitals and polyclinics.
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Affiliation(s)
- Afshin Mohammadi
- Department of Radiology, Faculty of Medicine, Urmia University of Medical Science, Urmia, Iran
| | - Thomas Torres-Cuenca
- Department of Physical Medicine and Rehabilitation, National University of Colombia, Bogotá, Colombia
| | - Mohammad Mirza-Aghazadeh-Attari
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Fariborz Faeghi
- Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Queensland, Australia
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
| | - Ali Abbasian Ardakani
- Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Lyu S, Zhang M, Zhang B, Yu J, Zhu J, Gao L, Yang L, Zhang Y. Application of ultrasound images-based radiomics in carpal tunnel syndrome: Without measuring the median nerve cross-sectional area. JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:1198-1204. [PMID: 37313858 DOI: 10.1002/jcu.23505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/27/2023] [Accepted: 05/31/2023] [Indexed: 06/15/2023]
Abstract
PURPOSE By constructing a prediction model of carpal tunnel syndrome (CTS) based on ultrasound images, it can automatically and accurately diagnose CTS without measuring the median nerve cross-sectional area (CSA). METHODS A total of 268 wrists ultrasound images of 101 patients diagnosed with CTS and 76 controls in Ningbo NO.2 Hospital from December 2021 to August 2022 were retrospectively analyzed. The radiomics method was used to construct the Logistic model through the steps of feature extraction, feature screening, reduction, and modeling. The area under the receiver operating characteristic curve was calculated to evaluate the performance of the model, and the diagnostic efficiency of the radiomics model was compared with two radiologists with different experience. RESULTS The 134 wrists in the CTS group included 65 mild CTS, 42 moderate CTS, and 17 severe CTS. In the CTS group, 28 wrists median nerve CSA were less than the cut-off value, 17 wrists were missed by Dr. A, 26 wrists by Dr. B, and only 6 wrists were missed by radiomics model. A total of 335 radiomics features were extracted from each MN, of which 10 features were significantly different between compressed and normal nerves, and were used to construct the model. The area under curve (AUC) value, sensitivity, specificity, and accuracy of the radiomics model in the training set and testing set were 0.939, 86.17%, 87.10%, 86.63%, and 0.891, 87.50%, 80.49%, and 83.95%, respectively. The AUC value, sensitivity, specificity, and accuracy of the two doctors in the diagnosis of CTS were 0.746, 75.37%, 73.88%, 74.63% and 0.679, 68.66%, 67.16%, and 67.91%, respectively. The radiomics model was superior to the two-radiologist diagnosis, especially when there was no significant change in CSA. CONCLUSION Radiomics based on ultrasound images can quantitatively analyze the subtle changes in the median nerve, and can automatically and accurately diagnose CTS without measuring CSA, especially when there was no significant change in CSA, which was better than radiologists.
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Affiliation(s)
- Shuyi Lyu
- Department of Interventional Therapy, Ningbo NO.2 Hospital, Ningbo, People's Republic of China
- Department of Ultrasound, Zhenhai Hospital of Traditional Chinese Medicine, Ningbo, People's Republic of China
| | - Meiwu Zhang
- Department of Interventional Therapy, Ningbo NO.2 Hospital, Ningbo, People's Republic of China
| | - Baisong Zhang
- Department of Interventional Therapy, Ningbo NO.2 Hospital, Ningbo, People's Republic of China
| | - Jianjun Yu
- Department of Neuroelectrophysiology, Ningbo NO.2 Hospital, Ningbo, People's Republic of China
| | - Jiazhen Zhu
- Department of Multi-Disciplinary Diagnosis and Treatment, Ningbo NO.2 Hospital, Ningbo, People's Republic of China
| | - Libo Gao
- Department of Interventional Therapy, Ningbo NO.2 Hospital, Ningbo, People's Republic of China
| | - Liu Yang
- Department of Interventional Therapy, Ningbo NO.2 Hospital, Ningbo, People's Republic of China
| | - Yan Zhang
- Department of Interventional Therapy, Ningbo NO.2 Hospital, Ningbo, People's Republic of China
- Department of Ultrasound, Zhenhai Hospital of Traditional Chinese Medicine, Ningbo, People's Republic of China
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Lyu S, Zhang Y, Zhang M, Zhu J, Yu J, Zhang B, Gao L, Wei H. The Application of Ultrasound Image-Based Radiomics in the Diagnosis of Mild Carpal Tunnel Syndrome. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:1499-1508. [PMID: 36565451 DOI: 10.1002/jum.16160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
OBJECTIVES The ultrasound diagnosis of mild carpal tunnel syndrome (CTS) is challenging. Radiomics can identify image information that the human eye cannot recognize. The purpose of our study was to explore the value of ultrasound image-based radiomics in the diagnosis of mild CTS. METHODS This retrospective study included 126 wrists in the CTS group and 88 wrists in the control group. The radiomics features were extracted from the cross-sectional ultrasound images at the entrance of median nerve carpal tunnel, and the modeling was based on robust features. Two radiologists with different experiences diagnosed CTS according to two guidelines. The area under receiver (AUC) operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the diagnostic efficacy of the two radiologists and the radiomics model. RESULTS According to guideline one, the AUC values of the two radiologists for CTS were 0.72 and 0.67, respectively; according to guideline two, the AUC were 0.73 and 0.68, respectively. The radiomics model achieved the best accuracy when 16 important robust features were selected. The AUC values of training set and test set were 0.92 and 0.90, respectively. CONCLUSIONS The radiomics label based on ultrasound images had excellent diagnostic efficacy for mild CTS. It is expected to help radiologists to identify early CTS patients as soon as possible, especially for inexperienced doctors.
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Affiliation(s)
- Shuyi Lyu
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Yan Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Meiwu Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Jiazhen Zhu
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
- Multi-disciplinary Diagnosis and Treatment Department, Ningbo No. 2 Hospital, Zhejiang, China
| | - Jianjun Yu
- Department of Neuroelectrophysiology, Ningbo No. 2 Hospital, Zhejiang, China
| | - Baisong Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Libo Gao
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
| | - Huilin Wei
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Zhejiang, China
- Ningbo Clinical Research Center for Medical Imaging, Zhejiang, China
- Provincial and Municipal Co-construction Key Discipline for Medical Imaging, Zhejiang, China
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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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