Background: This study aims to systematically evaluate the findings from computed tomography (CT) examinations conducted at least three months post-diagnosis of COVID-19 in patients diagnosed between 2020 and 2024. Objective: To determine the frequency and characteristics of CT findings in the post-COVID-19 period, analyze long-term effects on lung parenchyma, and contribute to the development of clinical follow-up and treatment strategies based on the collected data. Materials and Methods: Ethical approval was obtained for this retrospective study, and individual consent was waived. A total of 76 patients were included in the study, aged 18 and older, diagnosed with COVID-19 between March 2020 and November 2024, who underwent follow-up chest CT scans at 3–6 months, 6–12 months, and/or 12 months post-diagnosis. CT images were obtained in the supine position without contrast and evaluated by two experienced radiologists using a CT severity score (CT-SS) system, which quantifies lung involvement. Statistical analyses were performed using IBM SPSS 23.0, with significance set at p < 0.05. Results: The results indicated a mean CT-SS of 10.58 ± 0.659. Significant associations were found between age, CT scores, and the necessity for intensive care or mechanical ventilation. The most common CT findings included ground-glass opacities, reticular patterns, and traction bronchiectasis, particularly increasing with age and over time. Conclusion: This study emphasizes the persistent alterations in lung parenchyma following COVID-19, highlighting the importance of continuous monitoring and tailored treatment strategies for affected patients to improve long-term outcomes.

Amaç: Bu çalışmada, kırsal yaşam koşullarının etkili olduğu bir ilçede yaşayan kadınlarda kendi kendine meme muayenesi (KKMM) yapma sıklığını belirlenmesi ve meme kanseri risk düzeylerinin değerlendirilmesi amaçlandı. Yöntem: Tanımlayıcı, kesitsel tipte olan bu çalışmaya, Eylül 2018- Kasım 2018’de ilçe devlet hastanesi aile hekimliği genel polikliniğine herhangi bir sebeple başvuran 20 yaş ve üzeri kadınlardan çalışmaya katılmaya gönüllü olanlar dahil edildi. Katılımcılara yüz yüze görüşme yöntemiyle KKMM yapıp yapmadıkları soruldu ve Meme Kanseri Riski Değerlendirme Formu uygulandı. Bulgular: Çalışmamıza dahil edilen 155 kadının ortalama yaşı 43,70±14,31 (20-80) yıl, %81,9’u (n=127) evli, %83,9’u (n=130) ilköğretim ve altı eğitimli, %82,6’sının (n=128) ilk çocuk doğurma yaşı 30 yaşından önce idi. Ayrıca %3,2’si (n=5) sigara içiyor, %31,6’sı (n=49) fiziksel olarak aktif, %72,9’u (n=113) meyve, sebze ve lifli gıda ağırlıklı besleniyordu. KKMM yapma sıklığı %59,4 (n=92) iken, meme ultrasonografi ya da mamografi yaptıranların sıklığı ise %45,8 (n=71) idi. Meme kanseri risk anketi puan ortalaması 153,12±47,74 idi. Buna göre; %86,5'i düşük risk grubundaydı. Lineer regresyon analizi yapıldığında meme kanseri risk anketi puanındaki artışın %20,7’si bel çevresi ölçümünün fazla olmasına atfedilmektedir. Sonuç: Kırsal bir bölgede yaptığımız çalışmada katılımcıların KKMM yapma sıklığının yüksek olduğu ve çoğunluğunun meme kanseri risk değerlendirmesinde düşük risk grubunda yer aldıkları bulundu. Özellikle kanser taramalarının daha sıklıkla yapıldığı aile hekimliği polikliniklerine başvuran kadınların meme kanseri risk faktörleri gözden geçirilmeli, kendi kendine meme muayenesi yapma durumları sorgulanmalı ve klinik meme muayenesi ile doğru teknik öğretilmelidir. Erken tanı için uygun yaş aralığında ve gerektiği hallerde mamografik görüntüleme için yönlendirme yapılmalıdır.

Background and Objective: Axial spondyloarthritis (axSpA) is a group of chronic inflammatory diseases that primarily affect the sacroiliac joints. Early diagnosis is crucial for preventing irreversible structural damage. Magnetic Resonance Imaging (MRI) is the gold standard for detecting early inflammatory changes such as sacroiliitis. However, conventional MRI interpretation is inherently subjective and susceptible to both intra- and inter-observer variability. Therefore, artificial intelligence (AI)-driven diagnostic solutions are increasingly being explored. Among them, the Gated Attention Multiple Instance Learning (MIL) framework holds strong potential in modeling heterogeneous inflammatory distributions, thanks to its slice-level attention mechanism. This study aims to evaluate the diagnostic performance of a deep learning model based on Gated Attention MIL for automated sacroiliitis detection. Furthermore, its results are compared with a baseline deep learning architecture (standard ResNet-18), and its consistency with radiologist annotations is analyzed. Materials and Methods: The dataset included 554 subjects, comprising 276 patients diagnosed with axSpA and 278 healthy controls. All MRI data were derived from axial T2-weighted fat-suppressed (T2_TSE_TRA_FS) sequences. Patient-wise data splitting was employed to construct training, validation, and independent test sets. The proposed model architecture integrates ResNet-18-based feature extraction, a gated attention mechanism for instance-level weighting, and bag-level classification. Additionally, Test-Time Augmentation (TTA) was implemented to enhance robustness during inference. Results: On the independent test set, the model achieved an accuracy of 85.88%, sensitivity of 92.86%, specificity of 79.07%, and an F1-score of 86.67%. Attention heatmaps generated by the MIL module showed strong spatial overlap with bone marrow edema regions annotated by expert radiologists. Implementation of TTA led to an approximate 10% improvement in overall classification accuracy. Conclusions: The Gated Attention MIL framework demonstrated high diagnostic performance for sacroiliitis detection, indicating its value as a reliable decision support tool for early axSpA diagnosis. Validation on larger, multi-center datasets is warranted to ensure generalizability and to support clinical integration in routine radiology workflows.

Background: Axial spondyloarthritis (axSpA) is a group of chronic inflammatory diseases that primarily affect the sacroiliac joints. Early diagnosis is crucial for preventing irreversible structural damage. Magnetic Resonance Imaging (MRI) is the gold standard for detecting early inflammatory changes such as sacroiliitis. However, conventional MRI interpretation is inherently subjective and susceptible to both intra- and inter-observer variability. Therefore, artificial intelligence (AI)-driven diagnostic solutions are increasingly being explored. Among them, the Gated Attention Multiple Instance Learning (MIL) framework holds strong potential in modeling heterogeneous inflammatory distributions, thanks to its slice-level attention mechanism. Objective: This study aims to evaluate the diagnostic performance of a deep learning model based on Gated Attention MIL for automated sacroiliitis detection. Furthermore, its results are compared with conventional machine learning methods, and its consistency with radiologist annotations is analyzed. Materials and Methods: The dataset included 554 subjects, comprising 276 patients diagnosed with axSpA and 278 healthy controls. All MRI data were derived from axial T2-weighted fat-suppressed (T2_TSE_TRA_FS) sequences. Patient-wise data splitting was employed to construct training, validation, and independent test sets. The proposed model architecture integrates ResNet 18-based feature extraction, a gated attention mechanism for instance-level weighting, and bag-level classification. Additionally, Test-Time Augmentation (TTA) was implemented to enhance robustness during inference. Results: On the independent test set, the model achieved an accuracy of 85.88%, sensitivity of 92.86%, specificity of 79.07%, and an F1-score of 86.67%. Attention heatmaps generated by the MIL module showed strong spatial overlap with bone marrow edema regions annotated by expert radiologists. Implementation of TTA led to an approximate 10% improvement in overall classification accuracy. Conclusion: The Gated Attention MIL framework demonstrated high diagnostic performance for sacroiliitis detection, indicating its value as a reliable decision support tool for early axSpA diagnosis. Validation on larger, multi-center datasets is warranted to ensure generalizability and to support clinical integration in routine radiology workflows.

Radyoloji başucu serisi tıp fakültesi öğrencileri, radyoloji asistanları, radyoloji uzmanları, radyolojinin geniş ilgi alanında anlatılan konuyla ilgili diğer hekim ve sağlık personelleri için, hap formatında, her konunun en sık görülen tanılarının bulunduğu başucu veya başvuru kitabı olarak hazırlanmıştır.