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AI-enhanced predictive modeling for treatment duration and personalized treatment planning of cleft lip and palate therapy.
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Division of Plastic and Reconstructive Surgery, Neonatal and Pediatric Craniofacial Airway Orthodontics, Department of Surgery, Stanford University School of Medicine, 770 Welch Road, Palo Alto, CA, 94394, USA.
Background: Alveolar molding plate treatment (AMPT) plays a critical role in preparing neonates with cleft lip and palate (CLP) for the first reconstruction surgery (cleft lip repair). However, determining the number of adjustments to AMPT in near-normalizing cleft deformity prior to surgery is a challenging task, often affecting the treatment duration. This study explores the use of machine learning in predicting treatment duration based on three-dimensional (3D) assessments of the pre-treatment maxillary cleft deformity as part of individualized treatment planning.
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View Article and Find Full Text PDFAnalyzing Depression in College Students Using NLP and Transformer Models: Implications for Career and Educational Counseling.
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Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
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Artificial intelligence in nutrition science: Balancing innovation and ethical responsibility.
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Artificial intelligence (AI) is increasingly applied in nutrition science to support clinical decision-making, prevent diet-related diseases such as obesity and type 2 diabetes, and improve nutrition care in both preventive and therapeutic settings. By analyzing diverse datasets, AI systems can support highly individualized nutritional guidance. We focus on machine learning applications and image recognition tools for dietary assessment and meal planning, highlighting their potential to enhance patient engagement and adherence through mobile apps and real-time feedback.
View Article and Find Full Text PDFDiscrepancies in Vessel Diameter Measurements Between CTA and DSA in MCA M1 Occlusions: An Interobserver Study.
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Department of Diagnostic Imaging, Division of Neuroradiology, University of Toronto, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
Purpose: Accurate vessel measurement is essential in endovascular thrombectomy (EVT) for acute ischemic stroke. Discrepancies between computed tomography angiography (CTA) and digital subtraction angiography (DSA) may impact procedural planning and device selection. This study compares vessel diameter measurements from CTA and DSA in patients with middle cerebral artery (MCA) M1 occlusions.
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