Background: Recent guidelines advocate the use of real-time ultrasound to locate umbilical venous catheter tip. So far, training programs are not well established.
Methods: A pre/post interventional study was carried out in our tertiary neonatal intensive care unit centre to evaluate the efficacy of a training protocol in the use of real-time ultrasound. Primary outcome was the percentage in the use of real-time ultrasound.
Results: Fifty-four patients were enrolled. The use of real-time ultrasound for tip location significantly increased after the training program (15.3% vs 89.2%, p < 0.0001). After the training the tip of the catheters was more frequently placed at the junction of the inferior vena cava and right atrium (75% vs 30.7%, p = 0.0023). Twenty-two catheters were also evaluated with serial scans during the intervention phase to assess migration rate which was 50%.
Conclusion: a multimodal, targeted training on the use of real-time ultrasound for umbilical venous catheter placement is feasible. Real-time ultrasound is easily teachable, increases the number of umbilical venous catheters placed in a correct position, reduces the number of line manipulations and the need of chest-x-rays.
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http://dx.doi.org/10.1186/s13052-021-01014-7 | DOI Listing |
CNS Neurosci Ther
January 2025
Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China.
Aims: To develop a transformer-based generative adversarial network (trans-GAN) that can generate synthetic material decomposition images from single-energy CT (SECT) for real-time detection of intracranial hemorrhage (ICH) after endovascular thrombectomy.
Materials: We retrospectively collected data from two hospitals, consisting of 237 dual-energy CT (DECT) scans, including matched iodine overlay maps, virtual noncontrast, and simulated SECT images. These scans were randomly divided into a training set (n = 190) and an internal validation set (n = 47) in a 4:1 ratio based on the proportion of ICH.
Surg Innov
January 2025
Department of Surgery, Show Chwan Memorial Hospital, Changhua, Taiwan.
This study evaluates the feasibility of Apple Vision Pro goggles as an augmented reality (AR) surgical navigation tool for laparoscopic-assisted ultrasound-guided radiofrequency ablation (RFA) of liver tumors. Traditional RFA is effective but challenging due to the integration of multiple imaging modalities. The primary aim of this research is to assess how Vision Pro goggles can enhance the surgical navigation process during RFA, improving tumor localization and the overall effectiveness of the procedure.
View Article and Find Full Text PDFVet Sci
January 2025
Beef Cattle Institute, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, USA.
Thoracic ultrasonography (TUS) has emerged as a critical tool in the diagnosis and management of respiratory diseases in cattle, particularly bovine respiratory disease (BRD), which is one of the most economically significant health issues in feedyard operations. The objective of this review is to explore TUS in veterinary medicine, including the historical development, methodologies, and clinical applications for diagnosing and prognosing respiratory diseases. This review also emphasizes the importance of operator training, noting that even novice operators can achieve diagnostic consistency with proper instructions.
View Article and Find Full Text PDFJ Funct Biomater
January 2025
Department of Life Sciences, Hemchandracharya North Gujarat University, Patan 384265, Gujarat, India.
Each year, the number of cases of strokes and deaths due to this is increasing around the world. This could be due to work stress, lifestyles, unhealthy food habits, and several other reasons. Currently, there are several traditional methods like thrombolysis and mechanical thrombectomy for managing strokes.
View Article and Find Full Text PDFJ Imaging
January 2025
Clinic of Medicine, Nord-Trøndelag Hospital Trust, Levanger Hospital, 7601 Levanger, Norway.
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a cornerstone in minimally invasive thoracic lymph node sampling. In lung cancer staging, precise assessment of lymph node position is crucial for clinical decision-making. This study aimed to demonstrate a new deep learning method to classify thoracic lymph nodes based on their anatomical location using EBUS images.
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