INTRODUCTION: Autistic people experience higher risk of suicidal ideation (SI) and suicide attempts (SA) compared to non-autistic people, yet there is limited understanding of complex, multilevel factors that drive this disparity. Further, determinan...
AIMS: To synthesise the current research on long-term care workers' perceptions (i.e., attitudes, concerns, and expected functions) of robot-assisted care and their perceived effects of different types of robot-assisted care for older adults in long-...
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Mar 18, 2025
BACKGROUND: Detection algorithms targeting anatomic landmarks in three-dimensional (3D) ultrasound (US) volume (three-dimensional US) appear to be a relevant and easy-to-implement option to address junior and occasional operators' difficulties in pro...
Almost 40 years after the adoption of the International Code of Marketing of Breast-Milk Substitutes ('the Code') in Mexico, noncompliance persists. In other countries, smartphone applications for reporting Code noncompliance have proven effective. T...
International journal of legal medicine
Mar 18, 2025
INTRODUCTION: Age estimation, especially in adults, presents substantial challenges in different contexts ranging from forensic to clinical applications. Bone mineral density (BMD), with its distinct age-related variations, has emerged as a critical ...
PURPOSE: To develop three novel Vision Transformer (ViT) frameworks for the specific diagnosis of bacterial and fungal keratitis using different types of anterior segment images and compare their performances.
PURPOSE: This study aimed to compare a conventional three-dimensional (3-D) magnetic resonance cholangiopancreatography (MRCP) sequence with a deep learning (DL)-accelerated MRCP sequence (hereafter, MRCP) regarding acquisition time and image quality...
OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM).
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