Ardakani, A.A. and Afshar, A. and Bhatt, S. and Bureau, N.J. and Tahmasebi, A. and Acharya, U.R. and Mohammadi, A. (2020) Diagnosis of carpal tunnel syndrome: A comparative study of shear wave elastography, morphometry and artificial intelligence techniques. Pattern Recognition Letters, 133. pp. 77-85.
Full text not available from this repository.Abstract
Ultrasonography is an acceptable modality to evaluate median nerve (MN) in patients with carpal tunnel syndrome (CTS). Additional investigations are needed to evaluate sonographic parameters and compare their performances with artificial intelligence (AI) methods. The aim of this study is to compare the performance of shear wave elastography, morphometry, and AI techniques to predict MN entrapment accurately. 200 wrists including 100 CTS and 100 control wrists were included. Twelve morphological and five elasticity parameters were measured from each MN. Two AI techniques namely, support vector machine (SVM), and convolutional neural network (CNN) were used to diagnose CTS. MN area with area under receiver-operating characteristic curve (AUC) of 0.949 and mean elasticity with AUC of 0.942 showed the highest performance to differentiate CTS from control wrists among morphological and elasticity parameters, respectively. The CNN achieved the best performance with AUC of 0.980, while SVM obtained AUC of 0.943 in testing dataset to diagnose CTC. MN is larger, stiffer, more irregular and extended in CTS patients. Deep learning technique yielded the highest performance in diagnosing CTS automatically. AI methods have vast potential to be implemented in clinical practice as an auxiliary tool for the assessment of CTS with high accuracy. © 2020
| Item Type: | Article |
|---|---|
| Additional Information: | cited By 0 |
| Uncontrolled Keywords: | Convolutional neural networks; Deep learning; Diagnosis; Elasticity; Learning algorithms; Medical imaging; Neurodegenerative diseases; Shear flow; Shear waves; Statistical tests; Support vector machines; Temperature control; Ultrasonography, Artificial intelligence techniques; Carpal tunnel syndrome; Comparative studies; Elasticity imaging; Elasticity parameters; Learning techniques; Receiver operating characteristic curves; Shear wave elastography, Learning systems |
| Subjects: | WE Musculoskeletal System |
| Depositing User: | eprints admin |
| Date Deposited: | 13 Sep 2020 09:11 |
| Last Modified: | 13 Sep 2020 09:11 |
| URI: | http://eprints.iums.ac.ir/id/eprint/23711 |
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