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Artificial intelligence for brain neuroanatomical segmentation in magnetic resonance imaging: A literature review.
J Clin Neurosci
January 2025
Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, NSW, Australia; Computational NeuroSurgery (CNS) Lab, Macquarie University, NSW, Australia.
Purpose: This literature review aims to synthesise current research on the application of artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in magnetic resonance imaging (MRI).
Methods: A literature search was conducted using the databases Embase, Medline, Scopus, and Web of Science, and captured articles were assessed for inclusion in the review. Data extraction was performed for the summary of the AI model used, and key findings of each article, advantages and disadvantages were identified.
A rare case of culture-negative infective endocarditis following cataract surgery.
J Int Med Res
January 2025
Colombo South Teaching Hospital, Colombo, Sri Lanka.
A 70-year-old man developed intermittent fever with chills, severe anorexia, generalized weakness, and mild exertional difficulty in breathing following posterior chamber intraocular lens replacement surgery for a mature white cataract in the left eye. Laboratory tests revealed persistent negative blood cultures, normocytic and normochromic anemia, neutrophilia, and elevated inflammatory markers despite multiple courses of antibiotics. All other investigations conducted to identify the cause of prolonged fever, including transthoracic echocardiography, were negative.
View Article and Find Full Text PDFImpacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance.
BMC Oral Health
January 2025
Bangkok Hospital Dental Center Holistic Care and Dental Implant, Bangkok Hospital, Bangkok, 10310, Thailand.
Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.
View Article and Find Full Text PDFIntraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning.
Front Oncol
January 2025
Department of Anesthesiology, Shenshan Medical Central, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, China.
Background: Head and neck free flap reconstruction presents challenges in managing intraoperative circulation, potentially leading to prolonged length of stay (PLOS). Limited research exists on the associations between intraoperative circulation and PLOS given the difficulty of manual quantification of intraoperative circulation time-series data. Therefore, this study aimed to quantify intraoperative circulation data and investigate its association with PLOS after free flap reconstruction utilizing machine learning algorithms.
View Article and Find Full Text PDFOverview of basic design recommendations for user-centered explanation interfaces for AI-based clinical decision support systems: A scoping review.
Digit Health
January 2025
Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Objective: The application of artificial intelligence (AI)-based clinical decision support systems (CDSS) in the healthcare domain is still limited. End-users' difficulty understanding how the outputs of opaque black AI models are generated contributes to this. It is still unknown which explanations are best presented to end users and how to design the interfaces they are presented in (explanation user interface, XUI).
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