Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation
Abstract Identifying syndesmotic instability in ankle fractures using conventional radiographs is still a major challenge. In this study we trained a convolutional neural network (CNN) to classify the fracture utilizing the AO-classification (AO-44 A/B/C) and to simultaneously detect syndesmosis ins...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-14604-w |
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| author | Joshua Kubach Tobias Pogarell Michael Uder Mario Perl Marcel Betsch Mario Pasurka Stefan Söllner Rafael Heiss |
| author_facet | Joshua Kubach Tobias Pogarell Michael Uder Mario Perl Marcel Betsch Mario Pasurka Stefan Söllner Rafael Heiss |
| author_sort | Joshua Kubach |
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| description | Abstract Identifying syndesmotic instability in ankle fractures using conventional radiographs is still a major challenge. In this study we trained a convolutional neural network (CNN) to classify the fracture utilizing the AO-classification (AO-44 A/B/C) and to simultaneously detect syndesmosis instability in the conventional radiograph by leveraging the intraoperative stress testing as the gold standard. In this retrospective exploratory study we identified 700 patients with rotational ankle fractures at a university hospital from 2019 to 2024, from whom 1588 digital radiographs were extracted to train, validate, and test a CNN. Radiographs were classified based on the therapy-decisive gold standard of the intraoperative hook-test and the preoperatively determined AO-classification from the surgical report. To perform internal validation and quality control, the algorithm results were visualized using Guided Score Class activation maps (GSCAM).The AO44-classification sensitivity over all subclasses was 91%. Furthermore, the syndesmosis instability could be identified with a sensitivity of 0.84 (95% confidence interval (CI) 0.78, 0.92) and specificity 0.8 (95% CI 0.67, 0.9). Consistent visualization results were obtained from the GSCAMs. The integration of an explainable deep-learning algorithm, trained on an intraoperative gold standard showed a 0.84 sensitivity for syndesmotic stability testing. Thus, providing clinically interpretable outputs, suggesting potential for enhanced preoperative decision-making in complex ankle trauma. |
| format | Article |
| id | doaj-art-e1d034d24533485b89fe7f4e46bbd7cc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-e1d034d24533485b89fe7f4e46bbd7cc2025-08-20T04:01:51ZengNature PortfolioScientific Reports2045-23222025-08-0115111110.1038/s41598-025-14604-wDevelopment of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validationJoshua Kubach0Tobias Pogarell1Michael Uder2Mario Perl3Marcel Betsch4Mario Pasurka5Stefan Söllner6Rafael Heiss7Department of Traumatology and Orthopedics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergInstitute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergInstitute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergDepartment of Traumatology and Orthopedics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergDepartment of Traumatology and Orthopedics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergDepartment of Traumatology and Orthopedics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergDepartment of Traumatology and Orthopedics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergInstitute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergAbstract Identifying syndesmotic instability in ankle fractures using conventional radiographs is still a major challenge. In this study we trained a convolutional neural network (CNN) to classify the fracture utilizing the AO-classification (AO-44 A/B/C) and to simultaneously detect syndesmosis instability in the conventional radiograph by leveraging the intraoperative stress testing as the gold standard. In this retrospective exploratory study we identified 700 patients with rotational ankle fractures at a university hospital from 2019 to 2024, from whom 1588 digital radiographs were extracted to train, validate, and test a CNN. Radiographs were classified based on the therapy-decisive gold standard of the intraoperative hook-test and the preoperatively determined AO-classification from the surgical report. To perform internal validation and quality control, the algorithm results were visualized using Guided Score Class activation maps (GSCAM).The AO44-classification sensitivity over all subclasses was 91%. Furthermore, the syndesmosis instability could be identified with a sensitivity of 0.84 (95% confidence interval (CI) 0.78, 0.92) and specificity 0.8 (95% CI 0.67, 0.9). Consistent visualization results were obtained from the GSCAMs. The integration of an explainable deep-learning algorithm, trained on an intraoperative gold standard showed a 0.84 sensitivity for syndesmotic stability testing. Thus, providing clinically interpretable outputs, suggesting potential for enhanced preoperative decision-making in complex ankle trauma.https://doi.org/10.1038/s41598-025-14604-w |
| spellingShingle | Joshua Kubach Tobias Pogarell Michael Uder Mario Perl Marcel Betsch Mario Pasurka Stefan Söllner Rafael Heiss Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation Scientific Reports |
| title | Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation |
| title_full | Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation |
| title_fullStr | Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation |
| title_full_unstemmed | Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation |
| title_short | Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation |
| title_sort | development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation |
| url | https://doi.org/10.1038/s41598-025-14604-w |
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