| Autor: |
Marco Penso, Mario Babbaro, Sara Moccia, Andrea Baggiano, Maria Ludovica Carerj, Marco Guglielmo, Laura Fusini, Saima Mushtaq, Daniele Andreini, Mauro Pepi, Gianluca Pontone, Enrico G. Caiani |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Frontiers in Cardiovascular Medicine, Vol 10 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2297-055X |
| DOI: |
10.3389/fcvm.2023.1151705 |
| Popis: |
AimsDiagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images.Methods and resultsFifty consecutive patients with known left ventricular (LV) dysfunction (LVD) underwent both CE-CMR and (early and late) CE-CCT. According to the CE-CMR patterns, patients were classified as ischemic (n = 15, 30%) or non-ischemic (n = 35, 70%) LVD. Delayed enhancement regions were manually traced on late CE-CCT using CE-CMR as reference. On early CE-CCT images, the myocardial sectors were extracted according to AHA 16-segment model and labeled as with scar or not, based on the late CE-CCT manual tracing. A DL model was developed to classify each segment. A total of 44,187 LV segments were analyzed, resulting in accuracy of 71% and area under the ROC curve of 76% (95% CI: 72%−81%), while, with the bull’s eye segmental comparison of CE-CMR and respective early CE-CCT findings, an 89% agreement was achieved.ConclusionsDL on early CE-CCT acquisition may allow detection of LV sectors affected with myocardial fibrosis, thus without additional contrast-agent administration or radiational dose. Such tool might reduce the user interaction and visual inspection with benefit in both efforts and time. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
