Automated detection of scaphoid fractures using deep neural networks in radiographs
Article
Singh, Amanpreet, Ardakani, Ali Abbasian, Loh, Hui Wen, Anamika, P.V., Acharya, U. Rajendra, Kamath, Sidharth and Bhat, Anil K.. 2023. "Automated detection of scaphoid fractures using deep neural networks in radiographs." Engineering Applications of Artificial Intelligence. 122. https://doi.org/10.1016/j.engappai.2023.106165
| Article Title | Automated detection of scaphoid fractures using deep neural networks in radiographs |
|---|---|
| ERA Journal ID | 32032 |
| Article Category | Article |
| Authors | Singh, Amanpreet, Ardakani, Ali Abbasian, Loh, Hui Wen, Anamika, P.V., Acharya, U. Rajendra, Kamath, Sidharth and Bhat, Anil K. |
| Journal Title | Engineering Applications of Artificial Intelligence |
| Journal Citation | 122 |
| Article Number | 106165 |
| Number of Pages | 9 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0952-1976 |
| 1873-6769 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.engappai.2023.106165 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0952197623003494 |
| Abstract | The scaphoid is one of the most common carpal bone fractures diagnosed using radiographs. However, occult scaphoid fractures not visible on radiographs make early diagnosis and treatment difficult. Hence, the objective of this study was to develop a high-performing deep-learning model for the detection of apparent fracture and non-apparent occult scaphoid fractures using only plain wrist radiographs. This work used 525 x-ray images, 250 of which were normal scaphoids, 219 were fractured scaphoids, and 56 were occult fracture X-rays. These X-ray images were obtained from the Department of Orthopaedics, Kasturba Medical College’s (KMC), Manipal. A CNN-based deep learning model was developed for two classes (normal VS fracture) and three classes (normal VS fracture VS occult). For fracture localization, gradient-weighted class activation mapping (Grad-CAM) was used. For two-class classification, the proposed CNN model achieved sensitivity, specificity, accuracy and AUC of 92%, 88%, 90% and 0.95, respectively. For three-class classification, the proposed model achieved an overall performance of 85% sensitivity, 91% specificity, 90% accuracy and 0.88 AUC. Wrist radiograph images used to train the model did not undergo a segmentation process, saving time and improving efficiency if implemented in a hospital setting. |
| Keywords | CNN; Scaphoid; Fracture; X-ray; Radiograph; Deep learning; Grad-CAM |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Sri Guru Ram Das Institute of Medical Sciences and Research, India |
| Shahid Beheshti University of Medical Sciences, Iran | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Dolcera Information Technology Services, India | |
| School of Mathematics, Physics and Computing | |
| Asia University, Taiwan | |
| Kumamoto University, Japan | |
| Manipal Academy of Higher Education, India |
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https://research.usq.edu.au/item/z1w27/automated-detection-of-scaphoid-fractures-using-deep-neural-networks-in-radiographs
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