| Autor: |
Ryu, Sangeon, Ahn, Shawn, Espinoza, Jeacy, Jha, Alokkumar, Halene, Stephanie, Duncan, James S., Kwan, Jennifer M, Dvornek, Nicha C. |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
Background: We propose a novel method to identify who may likely have clonal hematopoiesis of indeterminate potential (CHIP), a condition characterized by the presence of somatic mutations in hematopoietic stem cells without detectable hematologic malignancy, using deep learning techniques. Methods: We developed a convolutional neural network (CNN) to predict CHIP status using 4 different views from standard delayed gadolinium-enhanced cardiac magnetic resonance imaging (CMR). We used 5-fold cross validation on 82 cardio-oncology patients to assess the performance of our model. Different algorithms were compared to find the optimal patient-level prediction method using the image-level CNN predictions. Results: We found that the best model had an area under the receiver operating characteristic curve of 0.85 and an accuracy of 82%. Conclusions: We conclude that a deep learning-based diagnostic approach for CHIP using CMR is promising. |
| Databáze: |
arXiv |
| Externí odkaz: |
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