Background/Objectives: The long-term sequelae of COVID-19 pneumonia, particularly the persistence of imaging abnormalities and their relationship to clinical symptoms, remain unclear. While the acute radiologic patterns are well-documented, the transition to chronic pulmonary changes—and their implications for long COVID symptoms—require systematic investigation. Methods: Our study included 93 patients with moderate to severe COVID-19 pneumonia who were admitted to Istanbul Medical Faculty Hospital, each having one follow-up CT scan over a ten-month period. Two thoracic radiologists independently calculated semi-quantitative initial chest CT scores to evaluate lung involvement in pneumonia (0–5 per lobe, total score 0–25). Two radiologists and one pulmonologist retrospectively examined the persistence of follow-up imaging findings, interpreting them alongside the relevant clinical and laboratory data. Additionally, in a subcohort (n = 46), mid-term (5–7 months) and long-term (≥10 months) scans were compared to assess temporal trajectories. Results: Among the 93 patients with long-term follow-up imaging, non-fibrotic changes persisted in 34 scans (36.6%), while fibrotic-like changes were observed in 70 scans (75.3%). The most common persistent non-fibrotic changes were heterogeneous attenuation (29%, n = 27) and ground-glass opacities (17.2%, n = 16), and the persistent fibrotic-like changes were pleuroparenchymal bands or linear atelectasis (58%, n = 54), fine reticulation (52.6%, n = 49), and subpleural curvilinear lines (34.4%, n = 32). Both persistent non-fibrotic and fibrotic-like changes were statistically correlated with the initial CT score (p < 0.001), LDH (p < 0.001), and ferritin levels (p = 0.008 and p = 0.003, respectively). Fatigue (p = 0.025) and chest pain (p < 0.001) were reported more frequently in patients with persistent non-fibrotic changes, while chest pain (p = 0.033) was reported more frequently among those with persistent fibrotic-like changes. Among the 46 patients who underwent both mid- and long-term follow-up imaging, 47.2% of those with non-fibrotic changes (17 out of 36) and 10% of those with fibrotic-like changes (4 out of 40) exhibited regression over the long term. Conclusions: Initial imaging and laboratory findings may indicate persistent imaging findings related to long-term sequelae of COVID-19 pneumonia. Many of these persistent imaging abnormalities, particularly non-fibrotic changes seen in the mid-term, tend to lessen over the long term. A correlation exists between persistent imaging findings and clinical outcomes of long COVID-19, underscoring the need for further research.

(1) Acute pancreatitis (AP) is a medical emergency associated with high mortality rates. Early and accurate prognosis assessment during admission is crucial for optimizing patient management and outcomes. This study seeks to develop robust radiomics-based machine learning (ML) models to classify the severity of AP using contrast-enhanced computed tomography (CECT) scans. (2) Methods: A retrospective cohort of 287 AP patients with CECT scans was analyzed, and clinical data were collected within 72 h of admission. Patients were classified as mild or moderate/severe based on the Revised Atlanta classification. Two radiologists manually segmented the pancreas and peripancreatic regions on CECT scans, and 234 radiomic features were extracted. The performance of the ML algorithms was compared with that of traditional scoring systems, including Ranson and Glasgow-Imrie scores. (3) Results: Traditional severity scoring systems produced AUC values of 0.593 (Ranson, Admission), 0.696 (Ranson, 48 h), 0.677 (Ranson, Cumulative), and 0.663 (Glasgow-Imrie). Using LASSO regression, 12 radiomic features were selected for the ML classifiers. Among these, the best-performing ML classifier achieved an AUC of 0.876 in the training set and 0.777 in the test set. (4) Conclusions: Radiomics-based ML classifiers significantly enhanced the prediction of AP severity in patients undergoing CECT scans within 72 h of admission, outperforming traditional severity scoring systems. This research is the first to successfully predict prognosis by analyzing radiomic features from both pancreatic and peripancreatic tissues using multiple ML algorithms applied to early CECT images.

Objective: To assess the diagnostic accuracy and clinical applicability of the artificial intelligence (AI) program “Canon Automation Platform” for the automated detection and localization of pulmonary embolisms (PEs) in chest computed tomography pulmonary angiograms (CTPAs). Methods: A total of 1474 CTPAs suspected of PEs were retrospectively evaluated by 2 senior radiology residents with 5 years of experience. The final diagnosis was verified through radiology reports by 2 thoracic radiologists with 20 and 25 years of experience, along with the patients’ clinical records and histories. The images were transferred to the Canon Automation Platform, which integrates with the picture archiving and communication system (PACS), and the diagnostic success of the platform was evaluated. This study examined all anatomic levels of the pulmonary arteries, including the left pulmonary artery, right pulmonary artery, and interlobar, segmental, and subsegmental branches. Results: The confusion matrix data obtained at all anatomic levels considered in our study were as follows: AUC-ROC score of 0.945 to 0.996, accuracy of 95.4% to 99.7%, sensitivity of 81.4% to 99.1%, specificity of 98.7% to 100%, PPV of 89.1% to 100%, NPV of 95.6% to 99.9%, F1 score of 0.868 to 0.987, and Cohen Kappa of 0.842 to 0.986. Notably, sensitivity in the subsegmental branches was lower (81.4% to 84.7%) compared with more central locations, whereas specificity remained consistent (98.7% to 98.9%). Conclusions: The results showed that the chest pain package of the Canon Automation Platform accurately provides rapid automatic PE detection in chest CTPAs by leveraging deep learning algorithms to facilitate the clinical workflow. This study demonstrates that AI can provide physicians with robust diagnostic support for acute PE, particularly in hospitals without 24/7 access to radiology specialists.