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Optimizing Catheter Verification: An Understandable AI Model for Efficient Assessment of Central Venous Catheter Placement in Chest Radiography.
Invest Radiol
October 2024
From the Department of Radiology and Nuclear Medicine, UKSH Lübeck, Lübeck, Germany (J.S., M.M., L.B., Y.E., J.B., M.M.S.); Institute of Medical Informatics, University of Lübeck, Lübeck, Germany (L.H., M.P.H.); Philips Research Hamburg, Hamburg, Germany (A.S., H.S.); and Institute of Interventional Radiology, UKSH Lübeck, Lübeck, Germany (M.M.S.).
Purpose: Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations that lack clinician-friendly comprehensibility. This study aims to introduce an approach that employs segmentation of support material and anatomy to enhance the precision and comprehensibility of CVC misplacement detection.
View Article and Find Full Text PDFContinuing Medical Education Questions: August 2024.
Am J Gastroenterol
August 2024
Baylor College of Medicine, Houston, Texas, USA.
Article Title: Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.
View Article and Find Full Text PDFDecoding Brain Development and Aging: Pioneering Insights From MRI Techniques.
Invest Radiol
October 2024
From the Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (A.H., S.K., J.K., M.N., W.U., S.F., T.A., A.W., K.K., S.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (A.H., M.N., S.F.); Polytechnique Montréal, Montreal, Quebec, Canada (S.N.); Montreal Heart Institute, University of Montreal, Montreal, Quebec, Canada (S.N.); and Center for Advanced Interdisciplinary Research, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia (S.N.).
The aging process induces a variety of changes in the brain detectable by magnetic resonance imaging (MRI). These changes include alterations in brain volume, fluid-attenuated inversion recovery (FLAIR) white matter hyperintense lesions, and variations in tissue properties such as relaxivity, myelin, iron content, neurite density, and other microstructures. Each MRI technique offers unique insights into the structural and compositional changes occurring in the brain due to normal aging or neurodegenerative diseases.
View Article and Find Full Text PDFElastography-based AI model can predict axillary status after neoadjuvant chemotherapy in breast cancer with nodal involvement: A prospective, multicenter, diagnostic study.
Int J Surg
October 2024
Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Objective: To develop a model for accurate prediction of axillary lymph node (LN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients with nodal involvement.
Methods: Between October 2018 and February 2024, 671 breast cancer patients with biopsy-proven LN metastasis who received NAC followed by axillary LN dissection were enrolled in this prospective, multicenter study. Preoperative ultrasound (US) images, including B-mode ultrasound (BUS) and shear wave elastography (SWE), were obtained.
A Course-Wide Approach to Building Generative Artificial Intelligence Literacy Across an Undergraduate Nursing Curriculum.
Nurse Educ
December 2024
Author Affiliations: Faculty of Health, School of Nursing and Midwifery & Centre for Quality and Patient Safety Research in the Institute for Health Transformation, Deakin University, Geelong, Victoria, Australia (Drs Tomlinson, Schoch, and McDonall); Faculty of Health, Deakin Learning Futures, Deakin University, Geelong, Victoria, Australia (Ms Macfarlane); and Faculty of Health, School of Nursing and Midwifery, Deakin University, Geelong, Victoria, Australia (Mss Aryal, Kumar, and Bunker).
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