BACKGROUND:This study evaluated short-term knowledge change and learner perceptions following a virtual reality (VR)-based point-of-care ultrasound (POCUS) training program for undergraduate medical students. METHODS:A single-center observational pre-post educational evaluation was conducted within an undergraduate POCUS training course at the National University of Singapore. Students completed a self-directed online module followed by a VR simulation session covering cardiac, lung, and eFAST ultrasound. Knowledge was assessed using a 19-item multiple-choice questionnaire administered immediately before and after the VR session. Post-session survey responses assessed learner experience, usability, perceived learning benefit, and adverse effects. RESULTS:Of 290 students who completed the pre-training assessment and participated in the VR session, 275 matched pre-post datasets were available for analysis. Mean MCQ score increased from 0.689 out of 1 (SD 0.162) before training to 0.809 out of 1 (SD 0.153) after training, corresponding to a mean paired improvement of 0.120, or approximately 2.3 additional correct answers out of 19 items (95% CI 0.104 to 0.135; p < 0.001; Cohen's dz = 0.93). Survey responses demonstrated favorable learner perceptions. Participants reporting adverse effects rated the session as less engaging than those without adverse effects. CONCLUSIONS:VR-based POCUS simulation, delivered within a blended learning pathway, was associated with improved immediate MCQ-assessed knowledge and favorable learner perceptions. Further studies should assess effectiveness by practical skills assessment and long-term knowledge retention.
The rapid integration of Artificial Intelligence (AI) into Point-of-Care Ultrasound (POCUS) represents a transformative shift, offering the potential to democratize diagnostic expertise while simultaneously presenting significant risks regarding clinical validation, workforce preparedness, and health equity. Informed by a recent global survey indicating that while 81% of practitioners are optimistic about AI, major concerns remain regarding training and evidence gaps, the World Interactive Network Focused On Critical UltraSound (WINFOCUS) proposes a unified strategic framework. This manifesto outlines seven foundational pillars to guide the ethical and effective adoption of AI-augmented POCUS: (1) earning trust through rigorous, prospective evidence; (2) building an AI-literate workforce through evolved curricula; (3) championing global equity to prevent widening health disparities; (4) ensuring algorithmic transparency and accountability; (5) designing for seamless human-AI collaboration; (6) establishing a sustainable, privacy-centric data infrastructure; and (7) committing to continuous, patient-centered evaluation. We present this roadmap as a global call to action for clinicians, researchers, and industry partners to collectively shape a future where technology amplifies clinical wisdom and improves patient outcomes.
BACKGROUND:Ultrasound-guided (USG) peripheral intravenous catheter (PIVC) insertion improves cannulation success, particularly in patients with difficult intravenous access. Whether USG also improves post-insertion outcomes, including catheter failure, dwell time, and complications, remains uncertain. METHODS:We conducted a systematic review and meta-analysis of randomized controlled trials and comparative cohort studies comparing USG with landmark-guided PIVC insertion and reporting at least one post-insertion outcome. PubMed, Embase, Cochrane CENTRAL, and CINAHL were searched from January 2000 to March 2026. Risk of bias was assessed using RoB 2 and the Newcastle-Ottawa Scale. Random-effects meta-analysis used the DerSimonian-Laird estimator; REML with Hartung-Knapp-Sidik-Jonkman adjustment was performed as a post-hoc sensitivity analysis because few studies contributed to pooled outcomes. RESULTS:Fourteen studies (5 randomized trials, 9 cohort studies; 78,209 participants) were included. Catheter failure did not differ significantly between USG and landmark groups (k = 4; RR 1.23, 95% CI 0.99-1.51; p = 0.056), although the estimate was sensitive to model choice and exclusion of the largest study. Dwell time and extravasation were not pooled because of substantial heterogeneity (I² = 91.9% and 95.7%, respectively). Infiltration also showed no significant difference (k = 2; RR 0.68, 95% CI 0.12-3.83). Several studies reporting favorable USG outcomes were substantially confounded by systematic differences in catheter length or material between groups. CONCLUSIONS:Current evidence does not demonstrate a consistent post-insertion advantage of USG over landmark PIVC placement. Reported benefits in some studies may reflect catheter-specification differences rather than ultrasound guidance itself. Larger, well-controlled trials are needed.
BACKGROUND:The objective of this study was to evaluate whether deep learning radiomic (DLR) models utilizing B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) could improve the preoperative prediction of lymph node metastasis (LNM) in patients with hilar cholangiocarcinoma (HCCA). METHODS:The study included 110 HCCA patients from two clinical centers, divided into primary and external validation cohorts. Pathological verification of lymph node status was performed, and the ResNet101 architecture was used to extract deep learning features (DLFs) from BUS and CEUS images. The Genetic Programming-based Symbolic Regression (GPSR) algorithm was applied to integrate radiomic features (RadFs) and DLFs, generating deep learning radiomic features (DLRFs). DLR models were subsequently constructed using the eXtreme Gradient Boosting (XGBoost) algorithm. RESULTS:Lymph node metastasis was identified in 48 out of 110 patients (43.64%). No significant differences in clinical characteristics were observed between LNM-positive and LNM-negative groups (P-values ranging from 0.14 to 0.98). A total of 837 RadFs and 4095 DLFs were initially extracted from each tumor region of interest (ROI). After feature selection, 10 RadFs (4 from BUS, 6 from CEUS) and 27 DLFs (5 from BUS, 22 from CEUS) were retained. Using the GPSR algorithm, 5 BUS-DLRFs, 10 CEUS-DLRFs, and 15 Combination-DLRFs were generated, leading to the development of three corresponding DLR models. In internal validation, the AUC values were 0.70 for the BUS-DLR model, 0.77 for the CEUS-DLR model, and 0.83 for the Combination-DLR model. In external validation, the AUC values were 0.66, 0.68, and 0.72, respectively. These results indicate that the integration of multiphasic CEUS and BUS data is essential for more comprehensively identifying LNM and achieving precise preoperative staging. CONCLUSIONS:The DLR models based on DLRFs demonstrated an enhanced ability to preoperatively predict LNM in HCCA patients, indicating that the integration of deep learning and RadFs from BUS and CEUS may offer improved predictive performance for LNM.
BACKGROUND:The role of venous Doppler ultrasound in assessing venous congestion in the setting of post-lung transplantation surgery is unknown. We performed a feasibility study on the use of venous Doppler ultrasound in the immediate post-operative period after lung transplantation in a retrospective, single-center observational evaluation. Patients had at least two venous congestion Doppler ultrasound exams involving interrogation of IVC diameter, hepatic vein, portal vein and renal vein Doppler assessments. Results: A total of 9 patients were included in our study with a median age of 60 years, 66.7% being male, and a median BMI of 20.9. All patients had VExUS grade ≥1 on their first venous Doppler exam. All nine patients had a decrease in portal vein pulsatility from initial to final exams. Correlation between portal venous pulsatility and net fluid balance was not statistically significant R= 0.33 (P= 0.39). Other parameters, including VExUS grade, IVC diameter, HV and RV Doppler changes from initial to final ultrasound exams showed variable changes in the post-operative period. CONCLUSION:Venous congestion Doppler ultrasound assessments appear feasible in immediate postoperative lung transplant patients. A multitude of factors beyond fluid status likely affect waveforms.
