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Hand-Assisted Laparoscopic Surgery (HALS) as an Alternative to Unplanned Laparoscopic Conversion to Open Surgery (LCOS) in Colectomies for Acute Diverticulitis.
Surg Innov
January 2025
Morristown Medical Center, Department of Surgery, Morristown, NJ, USA.
Background: In difficult colorectal cases, surgeons may opt for a hand-assisted laparoscopic (HALS) colectomy or attempt a laparoscopic surgery that may require an unplanned conversion to open (LCOS). We aimed to compare the clinical outcomes of these 2 types of surgeries.
Methods: Colectomies for acute diverticulitis with a HALS or LCOS surgery were selected from the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) 2022 Targeted Colectomy Database.
Acellular Dermal Matrices in Reconstructive Pediatric Complex Lower Limb Trauma: An Observational Study.
J Trauma Nurs
January 2025
Author Affiliations: St Andrew's Anglia Ruskin (StAAR) Research Group, Faculty of Health, Education, Medicine, and Social Care, Anglia Ruskin University, Chelmsford, UK (Dr Adegboye); Division of Plastic and Reconstructive Surgery, Red Cross War Memorial Children's Hospital; and Division of Plastic & Reconstructive Surgery, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa (Dr Pillay and Prof Adams).
Background: Contemporary research has shown that acellular dermal matrices can benefit adult lower extremity traumatic injuries; however, its use in children has not been explored.
Objective: This study aims to explore the use of acellular dermal matrices in pediatric complex lower extremity trauma.
Methods: This single-center retrospective observational cohort study of children with complex lower extremity trauma treated with Pelnac™, commercial acellular dermal matrices, was conducted at a tertiary hospital in South Africa from 2010 to 2017.
Improved Outcomes and Resource Use With Normothermic Machine Perfusion in Liver Transplantation.
JAMA Surg
January 2025
Division of Transplant Surgery, Department of Surgery, Mayo Clinic Arizona, Phoenix.
Importance: Normothermic machine perfusion (NMP) has been shown to reduce peritransplant complications. Despite increasing NMP use in liver transplant (LT), there is a scarcity of real-world clinical experience data.
Objective: To compare LT outcomes between donation after brain death (DBD) and donation after circulatory death (DCD) allografts preserved with NMP or static cold storage (SCS).
Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Use of ChatGPT Large Language Models to Extract Details of Recommendations for Additional Imaging From Free-Text Impressions of Radiology Reports.
AJR Am J Roentgenol
January 2025
Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Boston, MA 02120 Phone: 617-525-9702.
Automated extraction of actionable details of recommendations for additional imaging (RAIs) from radiology reports could facilitate tracking and timely completion of clinically necessary RAIs and thereby potentially reduce diagnostic delays. To assess the performance of large-language models (LLMs) in extracting actionable details of RAIs from radiology reports. This retrospective single-center study evaluated reports of diagnostic radiology examinations performed across modalities and care settings within five subspecialties (abdominal imaging, musculoskeletal imaging, neuroradiology, nuclear medicine, thoracic imaging) in August 2023.
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