Aim: This study assessed the utility of multimodal ultrasound in enhancing the accuracy of breast cancer sentinel lymph node (SLN) assessment and compared it with single-modality ultrasound.Methods: Preoperative examinations, including two-dimensional ultrasound (2D US), intradermal contrast-enhanced ultrasound (CEUS), intravenous CEUS, shear-wave elastography (SWE), and surface localization, were conducted on 86 SLNs from breast cancer patients. The diagnostic performance of single and multimodal approaches for detecting metastatic SLNs was compared to postoperative pathological results.Results: Among the 86 SLNs, 29 were pathologically diagnosed as metastatic, and 57 as non-metastatic. Single-modality ultrasounds had AUC values of 0.826 (intradermal CEUS), 0.705 (intravenous CEUS), 0.678 (2D US), and 0.677 (SWE), respectively. Intradermal CEUS significantly outperformed the other methods (p0.05). Multimodal ultrasound, combining intradermal CEUS, intravenous CEUS, 2D US, and SWE, achieved an AUC of 0.893, with 86.21% sensitivity and 84.21% specificity. The DeLong test confirmed that multimodal ultrasound was significantly better than the four single-modal ultrasound methods (p<0.05). Decision curve analysis and clinical impact curves demonstrated the superior performance of multimodal ultrasound in identifying high-risk SLN patients.Conclusion: Multimodal ultrasound improves breast cancer SLN identification and diagnostic accuracy.

Background: This study evaluates the performance of GPT-4o in detecting errors in ACR TIRADS ultrasound reports and its potential to reduce report generation time. Methods: A retrospective analysis of 200 thyroid ultrasound reports from the Second Affiliated Hospital of Fujian Medical University was conducted, with reports categorized as correct or containing up to three errors. GPT-4o's performance was compared with ultrasound physicians of varying experience levels in error detection and processing time. Results: GPT-4o detected 90.0% (180/200) of errors, slightly less than the best-performing senior ultrasound physician's 93.0% (186/200) with no significant difference ( p =  0.281). GPT-4o's error detection rate was comparable to that of ultrasound physicians overall ( p =  0.098 to 0.866). It outperformed Resident 2 in diagnostic errors (87% vs. 69%). Reader agreement was low (Cohen's kappa = 0 to 0.31). GPT-4o reviewed reports significantly faster than all ultrasound physicians (0.79 vs. 1.8 to 3.1 h, p  < 0.001), making it a reliable and efficient tool for error detection in medical imaging. Conclusions: GPT-4o is comparable to experienced ultrasound physicians in error detection and significantly improves report processing efficiency, offering a valuable tool for enhancing diagnostic accuracy and aiding junior residents.

Introduction: Dysphagia is common in chronic obstructive pulmonary disease (COPD), prompting the need for predictive models for this condition. In this study, we aimed to develop a nomogram model for dysphagia prediction in patients with COPD.Methods: Data from 300 patients with COPD were divided into the training (n = 210) and validation (n = 90) cohorts. Independent risk factors for dysphagia were identified using logistic regression and used to construct a nomogram model. The model’s predictive efficacy, accuracy, and clinical utility were evaluated using receiver operating characteristic curve analysis, calibration, decision curve analysis, and clinical impact curves.Results: Hypoglossal-hyoid shortening rate, hyoid-larynx shortening approximation distance, chin time of movement of the genioglossus, and distance of movement of the genioglossus were identified as independent risk factors. The nomogram exhibited areas under the curve of 0.834 and 0.804 in the training and validation cohorts, respectively, indicating good predictive efficacy and calibration.Conclusion: The nomogram model effectively predicts dysphagia occurrence in patients with COPD, providing a valuable tool for risk assessment.

Objectives: Hyperuricaemia has been linked to cognitive decline, yet cerebral structural and haemodynamic changes in this population remain poorly defined. We evaluated transcranial colour-coded duplex (TCCD) sonography as a non-invasive screening tool for early mild cognitive impairment (MCI) in elderly hyperuricaemic men. Methods: In this cross-sectional study, 195 men aged ≥ 60 years with hyperuricaemia were stratified by the Montreal Cognitive Assessment (MoCA) into HUA + MCI (MoCA < 26, n = 46) and HUA (MoCA ≥ 26, n = 149) groups. TCCD measured third-ventricle width (TVW) and peak systolic/end-diastolic velocities to calculate resistive (RI) and pulsatility (PI) indices in the middle (MCA) and posterior (PCA) cerebral arteries. Serum uric acid was recorded. Kernel density plots and receiver operating characteristic (ROC) curves assessed diagnostic performance. Results: The HUA + MCI group exhibited higher serum uric acid (508.5 ± 36.3 vs. 492.9 ± 44.0 µmol/L; p = 0.031), greater TVW (0.55 ± 0.11 vs. 0.51 ± 0.08 cm; p = 0.037), and elevated left PCA RI (0.69 ± 0.07 vs. 0.64 ± 0.06) and PI (1.05 ± 0.17 vs. 0.95 ± 0.12; both p < 0.001). ROC analysis identified left PCA PI as the most specific marker (AUC = 0.701; specificity 90.6%; sensitivity 45.7%). Kernel density plots confirmed distinct distributions of key parameters. Conclusions: TCCD-detected ventricular enlargement and raised PCA pulsatility accurately distinguish MCI among hyperuricaemic men. As a non-invasive, accessible technique with high specificity, TCCD may complement MRI and cognitive testing in early screening of at-risk populations.