XSRD-Net: EXplainable Stroke Relapse Detection
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918138197573632 |
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| author | Gapp, Christian Tappeiner, Elias Welk, Martin Fritscher, Karl Mangesius, Stephanie Eisenschink, Constantin Deisl, Philipp Knoflach, Michael Grams, Astrid E. Gizewski, Elke R. Schubert, Rainer |
| author_facet | Gapp, Christian Tappeiner, Elias Welk, Martin Fritscher, Karl Mangesius, Stephanie Eisenschink, Constantin Deisl, Philipp Knoflach, Michael Grams, Astrid E. Gizewski, Elke R. Schubert, Rainer |
| contents | Stroke is the second most frequent cause of death world wide with an annual mortality of around 5.5 million. Recurrence rates of stroke are between 5 and 25% in the first year. As mortality rates for relapses are extraordinarily high (40%) it is of utmost importance to reduce the recurrence rates. We address this issue by detecting patients at risk of stroke recurrence at an early stage in order to enable appropriate therapy planning. To this end we collected 3D intracranial CTA image data and recorded concomitant heart diseases, the age and the gender of stroke patients between 2010 and 2024. We trained single- and multimodal deep learning based neural networks for binary relapse detection (Task 1) and for relapse free survival (RFS) time prediction together with a subsequent classification (Task 2). The separation of relapse from non-relapse patients (Task 1) could be solved with tabular data (AUC on test dataset: 0.84). However, for the main task, the regression (Task 2), our multimodal XSRD-net processed the modalities vision:tabular with 0.68:0.32 according to modality contribution measures. The c-index with respect to relapses for the multimodal model reached 0.68, and the AUC is 0.71 for the test dataset. Final, deeper interpretability analysis results could highlight a link between both heart diseases (tabular) and carotid arteries (vision) for the detection of relapses and the prediction of the RFS time. This is a central outcome that we strive to strengthen with ongoing data collection and model retraining. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | XSRD-Net: EXplainable Stroke Relapse Detection Gapp, Christian Tappeiner, Elias Welk, Martin Fritscher, Karl Mangesius, Stephanie Eisenschink, Constantin Deisl, Philipp Knoflach, Michael Grams, Astrid E. Gizewski, Elke R. Schubert, Rainer Computer Vision and Pattern Recognition Artificial Intelligence I.2.1 Stroke is the second most frequent cause of death world wide with an annual mortality of around 5.5 million. Recurrence rates of stroke are between 5 and 25% in the first year. As mortality rates for relapses are extraordinarily high (40%) it is of utmost importance to reduce the recurrence rates. We address this issue by detecting patients at risk of stroke recurrence at an early stage in order to enable appropriate therapy planning. To this end we collected 3D intracranial CTA image data and recorded concomitant heart diseases, the age and the gender of stroke patients between 2010 and 2024. We trained single- and multimodal deep learning based neural networks for binary relapse detection (Task 1) and for relapse free survival (RFS) time prediction together with a subsequent classification (Task 2). The separation of relapse from non-relapse patients (Task 1) could be solved with tabular data (AUC on test dataset: 0.84). However, for the main task, the regression (Task 2), our multimodal XSRD-net processed the modalities vision:tabular with 0.68:0.32 according to modality contribution measures. The c-index with respect to relapses for the multimodal model reached 0.68, and the AUC is 0.71 for the test dataset. Final, deeper interpretability analysis results could highlight a link between both heart diseases (tabular) and carotid arteries (vision) for the detection of relapses and the prediction of the RFS time. This is a central outcome that we strive to strengthen with ongoing data collection and model retraining. |
| title | XSRD-Net: EXplainable Stroke Relapse Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.1 |
| url | https://arxiv.org/abs/2509.07772 |