The aim of this study was to evaluate the reliability and quality of information provided by artificial intelligence chatbots regarding the diagnosis, preventive methods, and treatment of contrast-associated acute kidney injury, while also discussing their benefits and drawbacks. The most frequently asked questions regarding contrast-associated acute kidney injury on Google Trends between January 2022 and January 2024 were posed to four artificial intelligence chatbots: ChatGPT, Gemini, Copilot, and Perplexity. The responses were evaluated based on the DISCERN score, the Patient Education Materials Assessment Tool for Printable Materials score, the Web Resource Rating scale, the Coleman-Liau index, and a Likert scale. As per the DISCERN score, the quality of information provided by Perplexity received a rating of "good", while the quality of information acquired by ChatGPT, Gemini, and Copilot was scored as "average." Based on the Coleman-Liau index, the readability of the responses was greater than 11 for all artificial intelligence chatbots, suggesting a high level of complexity requiring a university-level education. Similarly, the understandability and applicability scores on the Patient Education Materials Assessment Tool for Printable Materials and the Web Resource Rating scale were low for all artificial intelligence programs. In consideration of the Likert score, all artificial intelligence chatbots received favorable ratings. While patients increasingly utilize artificial intelligence chatbots to acquire information about contrast-associated acute kidney injury, the readability and understandability of the information provided may be low.

Objectives: To investigate whether sarcopenia had an effect on in-hospital mortality independent from other demographic characteristics in patients with Coronavirus disease 2019 (COVID-19), and to determine a reliable cut-off value for sarcopenia if there is such a relationship. Methods: A total of 302 patients with COVID-19 were included in the study. Sarcopenia was assessed by indexed skeletal muscle mass at T12 vertebrae level (T12-SMI) on initial chest computed tomography (CT). A receiver operating characteristic (ROC) curve analysis was performed to detect a cut-off value of T12-SMI for mortality prediction. Then, sarcopenia was diagnosed by this value. Multivariable logistic regression analysis was used to detect independent variables for mortality. Results: Patients were separated into groups; 26 (8.6%) patients in the mortality group and 276 (91.4%) patients in the no-mortality group. In ROC analysis, cut-off values of 34.06 cm²/m² (sensitivity: 70%, specificity: 77%) in males and 29.36 cm²/m² (sensitivity: 67%, specificity: 69%) in females for T12-SMI were computed for mortality prediction. There were 110 (36.4%) patients with sarcopenia. Sarcopenia was more frequent in the mortality group than the no-mortality group (73.1% vs 33%, p < 0.001). In multivariate analysis age, previous cardiovascular and respiratory disease, and sarcopenia were independently associated with mortality in COVID-19 patients. Conclusions: A cut-off value of 34.06 cm²/m² in males and 29.36 cm²/m² in females for T12-SMI can be used to diagnose sarcopenia in patients with COVID-19. Sarcopenia is clearly associated with mortality in these patients.

Background: This study aimed to evaluate the diagnostic performance of the Kaiser score (KS) on the modified abbreviated breast magnetic resonance imaging (AB-MRI) protocol for characterizing breast lesions by comparing it with full-protocol MRI (FP-MRI), using the histological data as the reference standard. Methods: Breast MRIs detecting histologically verified contrast-enhancing breast lesions were evaluated retrospectively. A modified AB-MRI protocol was created from the standard FP-MRI, which comprised axial fat-suppressed T2-weighted imaging (T2WI), pre-contrast T1-weighted imaging (T1WI), and first, second, and fourth post-contrast phases. Two radiologists reviewed both protocols, recording the KS for each detected lesion. Sensitivity, specificity, and positive and negative predictive values, as well as accuracy, were calculated for each protocol. Receiver operating characteristic (ROC) analysis was performed to determine the diagnostic performance of the modified AB-MRI compared to the FP-MRI. Results: In total, 154 patients with 158 histopathologically proven lesions (107 malignant, 51 benign) were included. For the diagnostic performance of the KS for modified AB-MRI and FP-MRI, the sensitivity was 96.3% vs. 98.1%, the specificity was 78.4% vs. 74.5%, PPV was 90.4% vs. 89%, NPV was 90.9% vs. 95%, and the diagnostic accuracy was 90.5% vs. 90.5%. The area under the curve (AUC) obtained from the ROC curve analysis was 0.873 and 0.863 for modified AB-MRI and FP-MRI for reader 1, respectively, and 0.859 and 0.878 for modified AB-MRI and FP-MRI for reader 2, respectively, (p < 0.001). Conclusions: Our modified AB-MRI protocol revealed comparable results in terms of the diagnostic value of the KS in characterizing breast lesions compared to FP-MRI and reduced both scanning and interpretation time.

Objective: To investigate the frequency of sarcopenia in cirrhotic patients, the correlation of sarcopenia with conventional prognostic scores and its relationship with mortality by measuring the psoas muscle area at the level of mid-3rd lumbar (L3) vertebra on computed tomography (CT) scan. Materials and Methods: We included 229 cirrhotic patients who had undergone abdominal CT scan for hepatocellular carcinoma screening. A control group was formed in 204 healthy individuals who had undergone abdominal computed tomography examination due to trauma. 1- and 3-year follow-up results were recorded. Cross-sectional areas of the right and left psoas muscles were calculated in axial sections passing through the level of the mid-L3 in CT images. The cases with two standard deviations under the mean of the total psoas muscle area in control group were evaluated as sarcopenic. Results: Sarcopenia was detected in 39.7% of 229 cirrhotic patients. While a statistically significant difference was detected in the Child-Pugh prognostic score between cirrhotic patients with and without sarcopenia, there was a modest difference in the MELD score. The percentage of cirrhotic patients with sarcopenia who passed during 1- and 3- year follow-ups (28.6% and 40.7%, respectively) were significantly higher than non-sarcopenic patients (9.4% and 24.6%, respectively). Conclusion: The findings obtained in this study suggest that psoas muscle atrophy shown by CT scan image at the level of the mid-L3 vertebra has predictive value for mortality in cirrhotic patients.

Introduction: About one-third of acute coronavirus disease 2019 (COVID-19) survivors have suffered from persisting symptoms called long-COVID. Clinical factors such as age and intensity (moderate or acute) of COVID-19 have been found to be associated with long-COVID. Many tissues might be damaged functionally or structurally during acute COVID-19 which can be detected by blood assays and chest computed tomography (CT). We aimed to evaluate the relationship between long-COVID and the initial findings of blood assays and chest CT as possible predictors. Methodology: The study included patients with acute COVID-19. Laboratory tests and chest CT were obtained from each patient at the time of admission to the hospital. Chest CT was evaluated for pneumonic involvement and severity score. Multivariable regression model was created to find the factors that were independently associated with long-COVID. Results: There were 60 (38.2%) patients with long-COVID and 97 (61.8%) without. Baseline demographic, laboratory and chest CT parameters were similar in both groups, except for age, chronic lung disease and chest CT severity score (46.9 ± 15.1 years vs 52.6 ± 15.9 years, p = 0.03; 11.7% vs 3.1%, p = 0.03 and 10.3 ± 9.6 vs 6.5 ± 7.6, p = 0.02, respectively). In multivariable model, chest CT severity score (OR: 1.059, 95% CI: 1.002-1.119, p = 0.04) and age (OR: 0.953, 95% CI: 0.928-0.979, p < 0.001) were independently associated with long-COVID. Conclusions: Chest CT severity score and age were independently associated with long-COVID and may be used to predict the future risk of long-COVID.