Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny
Dec 15, 2025
The article considers issues of training models of convolutional neuronic network (CNN) for automated identification of point functions of visualization to discern mammography pictures belonging to negative, false benign and malignant cases, targetin...
Psychiatric mental health nurse practitioners (PMHNPs) play a vital role in addressing substance use disorders, particularly in underserved regions. This article aimed to explore the effectiveness of artificial intelligence (AI)-generated Screening...
BACKGROUND: In an ophthalmology emergency department, determining treatment urgency is crucial for patient safety and the efficient use of resources. The aim of this study was to use artificial intelligence to develop a neural network and evaluate it...
People in vulnerable positions who need support in their daily lives often face challenges in receiving timely access to care; for instance, due to disabilities or individual and situational vulnerabilities. There has been an increasing turn to techn...
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common complication after type A aortic dissection surgery and often leads to worsened clinical outcomes for patients. The early prediction of postoperative ARDS is a crucial challenge in cl...
BACKGROUND: Urolithiasis, particularly anhydrous uric acid stones (AUAs), imposes significant clinical and economic burdens. Accurate preoperative differentiation of AUAs from other stone types remains challenging, yet essential for personalized pati...
BACKGROUND: Opioid overuse is a costly and significant problem in the United States. Medical specialties including surgery are a contributor to opioid prescriptions while having few clear prescribing guidelines. Machine learning predictive tools can ...
BACKGROUND: This study aimed to evaluate the efficacy of a convolutional neural network (CNN) model in estimating fetal brain age from MRI scans during second and third trimesters.
BACKGROUND: Digital phenotyping refers to the objective measurement of human behavior via devices such as smartphones or watches and constitutes a promising advancement in personalized medicine. Digital phenotypes derived from heart rate, mobility, o...
BACKGROUND: Computer perception (CP) technologies hold significant promise for advancing precision mental health care systems, given their ability to leverage algorithmic analysis of continuous, passive sensing data from wearables and smartphones (eg...
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