Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi-Center Study
Article
Mohammadi, Afshin, Torres-Cuenca, Thomas, Mirza-Aghazadeh-Attari, Mohammad, Faeghi, Fariborz, Acharya, U. Rajendra and Ardakani, Ali Abbasian. 2023. "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. 42 (10), pp. 2257-2268. https://doi.org/10.1002/jum.16244
| Article Title | Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi-Center Study |
|---|---|
| ERA Journal ID | 16537 |
| Article Category | Article |
| Authors | Mohammadi, Afshin, Torres-Cuenca, Thomas, Mirza-Aghazadeh-Attari, Mohammad, Faeghi, Fariborz, Acharya, U. Rajendra and Ardakani, Ali Abbasian |
| Journal Title | Journal of Ultrasound in Medicine |
| Journal Citation | 42 (10), pp. 2257-2268 |
| Number of Pages | 12 |
| Year | 2023 |
| Publisher | John Wiley & Sons |
| Place of Publication | United States |
| ISSN | 0278-4297 |
| 1550-9613 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1002/jum.16244 |
| Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1002/jum.16244 |
| 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. © 2023 American Institute of Ultrasound in Medicine. |
| Keywords | artificial intelligence; carpal tunnel syndrome; deep learning; median nerve; neuropathy; ultrasound |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | 2-s2.0-85153606547 |
| Byline Affiliations | Urmia University of Medical Science, Iran |
| National University of Colombia, Colombia | |
| Johns Hopkins University, United States | |
| Shahid Beheshti University of Medical Sciences, Iran | |
| School of Mathematics, Physics and Computing | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
Permalink -
https://research.usq.edu.au/item/z1vy1/deep-radiomics-features-of-median-nerves-for-automated-diagnosis-of-carpal-tunnel-syndrome-with-ultrasound-images-a-multi-center-study
37
total views0
total downloads3
views this month0
downloads this month