Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
A novel unsupervised machine learning clustering strategy to identify PET/MR biomarkers in arrhythmogenic cardiomyopathy |
| Συγγραφείς: |
Brunnhilde Ponsi, Hatem Necib, Lara Marteau, Aurélien Monnet, Thomas Carlier, Thomas Eugène, Jean-Michel Serfaty, Nicolas Piriou |
| Πηγή: |
ISMRM Annual Meeting. |
| Στοιχεία εκδότη: |
ISMRM, 2025. |
| Έτος έκδοσης: |
2025 |
| Περιγραφή: |
Motivation: Diagnosing arrhythmogenic cardiomyopathy (AC) is challenging without gold-standard criteria. LGE, T1/T2 mappings, and PET imaging offer complementary insights on fibrosis and inflammation. Goal(s): This study aims to demonstrate the potential of simultaneous PET/MR and inter-patient data linkage to discover novel regional markers of AC. Approach: Two-step clustering was applied to multimodal images of AC patients. Supervoxels were extracted from each patient, before being clustered to thirty-two inter-patient groups. Patient's health reports were generated and compared to cardiac imagers' reports using balanced accuracy (BA). Results: Clustering reports accurately represented the proportions of hyper-signal combinations per patient, while identifying most cardiac imagers observations (BA=0.76). Impact: A two-step multimodal PET/MR unsupervised clustering method combining supervoxel extraction and inter-patient clustering was developed, enabling robust identification, visualization, and quantification of abnormal regions in arrhythmogenic cardiomyopathy patients. It provides an encouraging step toward identifying prognostic clusters and patient profiles. |
| Τύπος εγγράφου: |
Article |
| ISSN: |
1545-4428 |
| DOI: |
10.58530/2025/1887 |
| Αριθμός Καταχώρησης: |
edsair.doi...........f1dff4e20783b3e9c3d1821ea5bfa13a |
| Βάση Δεδομένων: |
OpenAIRE |