Purpose: This study aimed to determine whether micro-flow imaging (MFI) offers diagnostic performance comparable to that of contrast-enhanced ultrasonography (CEUS) in detecting segmental congestion among patients undergoing living donor liver transplantation (LDLT). Methods: Data from 63 patients who underwent LDLT between May and December 2022 were retrospectively analyzed. MFI and CEUS data collected on the first postoperative day were quantified. Segmental congestion was assessed based on imaging findings and laboratory data, including liver enzymes and total bilirubin levels. The reference standard was a postoperative contrast-enhanced computed tomography scan performed within 2 weeks of surgery. Additionally, a subgroup analysis examined patients who underwent reconstruction of the middle hepatic vein territory. Results: The sensitivity and specificity of MFI were 73.9% and 67.5%, respectively. In comparison, CEUS demonstrated a sensitivity of 78.3% and a specificity of 75.0%. These findings suggest comparable diagnostic performance, with no significant differences in sensitivity (P=0.655) or specificity (P=0.257) between the two modalities. Additionally, early postoperative laboratory values did not show significant differences between patients with and without congestion. The subgroup analysis also indicated similar diagnostic performance between MFI and CEUS. Conclusion MFI without contrast enhancement yielded results comparable to those of CEUS in detecting segmental congestion after LDLT. Therefore, MFI may be considered a viable alternative to CEUS.

Purpose: This study aimed to determine whether micro-flow imaging (MFI) offers diagnostic performance comparable to that of contrast-enhanced ultrasonography (CEUS) in detecting segmental congestion among patients undergoing living donor liver transplantation (LDLT). Methods: Data from 63 patients who underwent LDLT between May and December 2022 were retrospectively analyzed. MFI and CEUS data collected on the first postoperative day were quantified. Segmental congestion was assessed based on imaging findings and laboratory data, including liver enzymes and total bilirubin levels. The reference standard was a postoperative contrast-enhanced computed tomography scan performed within 2 weeks of surgery. Additionally, a subgroup analysis examined patients who underwent reconstruction of the middle hepatic vein territory. Results: The sensitivity and specificity of MFI were 73.9% and 67.5%, respectively. In comparison, CEUS demonstrated a sensitivity of 78.3% and a specificity of 75.0%. These findings suggest comparable diagnostic performance, with no significant differences in sensitivity (P=0.655) or specificity (P=0.257) between the two modalities. Additionally, early postoperative laboratory values did not show significant differences between patients with and without congestion. The subgroup analysis also indicated similar diagnostic performance between MFI and CEUS. Conclusion MFI without contrast enhancement yielded results comparable to those of CEUS in detecting segmental congestion after LDLT. Therefore, MFI may be considered a viable alternative to CEUS.

Purpose: This study aimed to determine whether micro-flow imaging (MFI) offers diagnostic performance comparable to that of contrast-enhanced ultrasonography (CEUS) in detecting segmental congestion among patients undergoing living donor liver transplantation (LDLT). Methods: Data from 63 patients who underwent LDLT between May and December 2022 were retrospectively analyzed. MFI and CEUS data collected on the first postoperative day were quantified. Segmental congestion was assessed based on imaging findings and laboratory data, including liver enzymes and total bilirubin levels. The reference standard was a postoperative contrast-enhanced computed tomography scan performed within 2 weeks of surgery. Additionally, a subgroup analysis examined patients who underwent reconstruction of the middle hepatic vein territory. Results: The sensitivity and specificity of MFI were 73.9% and 67.5%, respectively. In comparison, CEUS demonstrated a sensitivity of 78.3% and a specificity of 75.0%. These findings suggest comparable diagnostic performance, with no significant differences in sensitivity (P=0.655) or specificity (P=0.257) between the two modalities. Additionally, early postoperative laboratory values did not show significant differences between patients with and without congestion. The subgroup analysis also indicated similar diagnostic performance between MFI and CEUS. Conclusion MFI without contrast enhancement yielded results comparable to those of CEUS in detecting segmental congestion after LDLT. Therefore, MFI may be considered a viable alternative to CEUS.

Purpose: This study aimed to determine whether micro-flow imaging (MFI) offers diagnostic performance comparable to that of contrast-enhanced ultrasonography (CEUS) in detecting segmental congestion among patients undergoing living donor liver transplantation (LDLT). Methods: Data from 63 patients who underwent LDLT between May and December 2022 were retrospectively analyzed. MFI and CEUS data collected on the first postoperative day were quantified. Segmental congestion was assessed based on imaging findings and laboratory data, including liver enzymes and total bilirubin levels. The reference standard was a postoperative contrast-enhanced computed tomography scan performed within 2 weeks of surgery. Additionally, a subgroup analysis examined patients who underwent reconstruction of the middle hepatic vein territory. Results: The sensitivity and specificity of MFI were 73.9% and 67.5%, respectively. In comparison, CEUS demonstrated a sensitivity of 78.3% and a specificity of 75.0%. These findings suggest comparable diagnostic performance, with no significant differences in sensitivity (P=0.655) or specificity (P=0.257) between the two modalities. Additionally, early postoperative laboratory values did not show significant differences between patients with and without congestion. The subgroup analysis also indicated similar diagnostic performance between MFI and CEUS. Conclusion MFI without contrast enhancement yielded results comparable to those of CEUS in detecting segmental congestion after LDLT. Therefore, MFI may be considered a viable alternative to CEUS.

Purpose: This study aimed to determine whether micro-flow imaging (MFI) offers diagnostic performance comparable to that of contrast-enhanced ultrasonography (CEUS) in detecting segmental congestion among patients undergoing living donor liver transplantation (LDLT). Methods: Data from 63 patients who underwent LDLT between May and December 2022 were retrospectively analyzed. MFI and CEUS data collected on the first postoperative day were quantified. Segmental congestion was assessed based on imaging findings and laboratory data, including liver enzymes and total bilirubin levels. The reference standard was a postoperative contrast-enhanced computed tomography scan performed within 2 weeks of surgery. Additionally, a subgroup analysis examined patients who underwent reconstruction of the middle hepatic vein territory. Results: The sensitivity and specificity of MFI were 73.9% and 67.5%, respectively. In comparison, CEUS demonstrated a sensitivity of 78.3% and a specificity of 75.0%. These findings suggest comparable diagnostic performance, with no significant differences in sensitivity (P=0.655) or specificity (P=0.257) between the two modalities. Additionally, early postoperative laboratory values did not show significant differences between patients with and without congestion. The subgroup analysis also indicated similar diagnostic performance between MFI and CEUS. Conclusion MFI without contrast enhancement yielded results comparable to those of CEUS in detecting segmental congestion after LDLT. Therefore, MFI may be considered a viable alternative to CEUS.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Background: and Purpose The accurate prediction of functional outcomes in patients with acute ischemic stroke (AIS) is crucial for informed clinical decision-making and optimal resource utilization. As such, this study aimed to construct an ensemble deep learning model that integrates multimodal imaging and clinical data to predict the 90-day functional outcomes after AIS. Methods: We used data from the Korean Stroke Neuroimaging Initiative database, a prospective multicenter stroke registry to construct an ensemble model integrated individual 3D convolutional neural networks for diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR), along with a deep neural network for clinical data, to predict 90-day functional independence after AIS using a modified Rankin Scale (mRS) of 3–6. To evaluate the performance of the ensemble model, we compared the area under the curve (AUC) of the proposed method with that of individual models trained on each modality to identify patients with AIS with an mRS score of 3–6. Results: Of the 2,606 patients with AIS, 993 (38.1%) achieved an mRS score of 3–6 at 90 days post-stroke. Our model achieved AUC values of 0.830 (standard cross-validation [CV]) and 0.779 (time-based CV), which significantly outperformed the other models relying on single modalities: b-value of 1,000 s/mm2 (P<0.001), apparent diffusion coefficient map (P<0.001), FLAIR (P<0.001), and clinical data (P=0.004). Conclusion The integration of multimodal imaging and clinical data resulted in superior prediction of the 90-day functional outcomes in AIS patients compared to the use of a single data modality.