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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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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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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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Di Cosmo M, Fiorentino MC, Villani FP, Frontoni E, Smerilli G, Filippucci E, Moccia S. A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet. Med Biol Eng Comput 2022; 60:3255-3264. [PMID: 36152237 PMCID: PMC9537213 DOI: 10.1007/s11517-022-02662-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 08/22/2022] [Indexed: 11/29/2022]
Abstract
AbstractUltrasound (US) imaging is recognized as a useful support for Carpal Tunnel Syndrome (CTS) assessment through the evaluation of median nerve morphology. However, US is still far to be systematically adopted to evaluate this common entrapment neuropathy, due to US intrinsic challenges, such as its operator dependency and the lack of standard protocols. To support sonographers, the present study proposes a fully-automatic deep learning approach to median nerve segmentation from US images. We collected and annotated a dataset of 246 images acquired in clinical practice involving 103 rheumatic patients, regardless of anatomical variants (bifid nerve, closed vessels). We developed a Mask R-CNN with two additional transposed layers at segmentation head to accurately segment the median nerve directly on transverse US images. We calculated the cross-sectional area (CSA) of the predicted median nerve. Proposed model achieved good performances both in median nerve detection and segmentation: Precision (Prec), Recall (Rec), Mean Average Precision (mAP) and Dice Similarity Coefficient (DSC) values are 0.916 ± 0.245, 0.938 ± 0.233, 0.936 ± 0.235 and 0.868 ± 0.201, respectively. The CSA values measured on true positive predictions were comparable with the sonographer manual measurements with a mean absolute error (MAE) of 0.918 mm2. Experimental results showed the potential of proposed model, which identified and segmented the median nerve section in normal anatomy images, while still struggling when dealing with infrequent anatomical variants. Future research will expand the dataset including a wider spectrum of normal anatomy and pathology to support sonographers in daily practice.
Graphical abstract
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Affiliation(s)
- Mariachiara Di Cosmo
- Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131, Ancona, AN, Italy.
| | - Maria Chiara Fiorentino
- Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131, Ancona, AN, Italy
| | | | - Emanuele Frontoni
- Department of Political Sciences, Communication and International Relations, Università di Macerata, Macerata, Italy
| | - Gianluca Smerilli
- Rheumatology Unit, Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, "Carlo Urbani" Hospital, Ancona, Italy
| | - Emilio Filippucci
- Rheumatology Unit, Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, "Carlo Urbani" Hospital, Ancona, Italy
| | - Sara Moccia
- The BioRobotics Institute, Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
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