| Yazarlar (2) |
Dr. Öğr. Üyesi İsmail DAL
Kastamonu Üniversitesi, Türkiye |
Doç. Dr. Kemal AKYOL
Kastamonu Üniversitesi, Türkiye |
| Özet |
| Background Pneumothorax (PTX) requires rapid recognition in emergency and critical care. Lung ultrasound (LUS) offers a fast, radiation-free alternative to computed tomography (CT), but its accuracy is limited by operator dependence. Artificial intelligence (AI) may standardize interpretation and improve performance. Methods This retrospective single-center study included 46 patients (23 with CT-confirmed PTX and 23 controls). Sixty B-mode and M-mode frames per patient were extracted using a Clarius C3 HD3 wireless device, yielding 2,760 images. CT served as the diagnostic reference. Two transformer-based models, Vision Transformer (ViT) and DINOv2, were trained and tested under two scenarios: random frame split and patient-level split. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the ROC curve (AUC). Results Both transformers achieved high diagnostic accuracy, with B-mode images outperforming M-mode inputs. In Scenario 1, ViT reached 99.1% accuracy, while DINOv2 achieved 97.3%. In Scenario 2, which avoided data leakage, DINOv2 performed best in the B-mode region (90% accuracy, 80% sensitivity, 100% specificity, F1-score 88.9%). ROC analysis confirmed strong discriminative ability, with AUC values of 0.973 for DINOv2 and 0.964 for ViT on B-mode images. Conclusions AI-assisted LUS substantially improves PTX detection, with transformers—particularly DINOv2—achieving near-expert accuracy. Larger multicenter datasets are required for validation and clinical integration. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Tomography |
| Dergi ISSN | 2379-139X Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 10-2025 |
| Cilt No | 11 |
| Sayı | 121 |
| Sayfalar | 1 / 23 |
| Doi Numarası | 10.3390/tomography11110121 |
| Makale Linki | https://www.mdpi.com/2379-139X/11/11/121 |
| Atıf Sayıları |
