Simulated bones play a crucial role in surgical training, yet existing models vary in fidelity and cost. We developed a novel, low-cost, high-quality SLA 3D-printed metacarpal to enhance hand trauma training. Our design incorporates novel cortico-medullary differentiation to replicate the tactile feedback experienced when drilling through real bone. Our device was tested across 3 centres with 17 hand surgeons providing feedback. Overall surgeons highly rated our device, with it outperforming homogenous control simulators. All surgeons agreed that it had the potential to enhance surgical training. We have addressed the single use aspect of our devices by partnering with a plastic upcycling firm to repurpose used simulators, minimising landfill waste. We look forward to implementing our device in surgical training, whilst further improving it via the addition of built in fractures and a soft tissue envelope.
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http://dx.doi.org/10.1016/j.bjps.2025.01.045 | DOI Listing |
J Surg Case Rep
March 2025
Department of Palliative Care, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland.
Infective endocarditis (IE) is a common complication in patients who inject drugs. We present the case of a 36-year-old woman with IE affecting both the aortic and tricuspid valves, along with a cardiac implantable electronic device infection, 11 weeks after combined aortic valve replacement, tricuspid valve replacement, and pacemaker implantation. The patient declined the medically indicated cardiac surgery due to her recent taxing surgical and rehabilitation experiences.
View Article and Find Full Text PDFIndian J Plast Surg
February 2025
Centre of Excellence in Industrial and Product Design, Punjab Engineering College, Chandigarh, India.
Maxillary reconstruction poses unique challenges for the reconstructive surgeon because of the complex three-dimensional (3D) anatomy of the maxilla. Undertaking this endeavor on secondary reconstruction makes it more difficult due to problems in recreating the true defect. This study is an attempt to demonstrate the role of virtual surgical planning (VSP), 3D printing, and mock surgery in reconstructing such defects using free fibula flaps.
View Article and Find Full Text PDFFront Public Health
March 2025
Department of Health Systems Management and Health Economics, School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia.
Background: Policy makers and stakeholders may benefit from understanding maternal delivery referral practices as they develop efficient mechanisms to implement appropriate referral linkage. However, the practice of maternal delivery referral is not well known. This study aims to assess the maternal referral practices and associated factors among laboring mothers referred to public hospitals of Bahir Dar City, Northwest, Ethiopia.
View Article and Find Full Text PDFFront Surg
February 2025
The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, United States.
Objective: This systematic literature review of the integration of artificial intelligence (AI) applications in surgical practice through hand and instrument tracking provides an overview of recent advancements and analyzes current literature on the intersection of surgery with AI. Distinct AI algorithms and specific applications in surgical practice are also examined.
Methods: An advanced search using medical subject heading terms was conducted in Medline (via PubMed), SCOPUS, and Embase databases for articles published in English.
Chin J Cancer Res
January 2025
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan 250117, China.
Objective: The neglect of occult lymph nodes metastasis (OLNM) is one of the pivotal causes of early non-small cell lung cancer (NSCLC) recurrence after local treatments such as stereotactic body radiotherapy (SBRT) or surgery. This study aimed to develop and validate a computed tomography (CT)-based radiomics and deep learning (DL) fusion model for predicting non-invasive OLNM.
Methods: Patients with radiologically node-negative lung adenocarcinoma from two centers were retrospectively analyzed.
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