BACKGROUND: Estimating craniofacial patterns is essential for successful orthodontic treatment. However, conventional static measurements are inadequate for capturing dynamic changes, and manual cephalometric analysis is labor-intensive and requires ...
BACKGROUND AND OBJECTIVES: We aimed to identify and optimize contributing factors associated with allergic diseases by machine/deep learning algorithms among school-age children aged 6-14 years.
PURPOSE: Objectives were to develop a machine learning (ML) model based on electronic health record (EHR) data to predict the risk of vomiting within a 96-hour window after admission to the pediatric oncology and hematopoietic cell transplant (HCT) s...
BACKGROUND: Experiences of unfair treatment on college campuses are linked to adverse mental and physical health outcomes, highlighting the need for interventions. However, detecting such experiences relies mainly on self-reports. No prior research h...
PURPOSE: Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydroce...
BACKGROUND AND OBJECTIVE: Bangladesh, a South Asian country, continues to face significant challenges in maternal health, as reflected by its high maternal mortality ratio (MMR). According to the 2022 Bangladesh Demographic and Health Survey (BDHS), ...
We evaluated the potential utility of imaging parameters derived by normalizing muscle signal intensity on T1-weighted lower leg MRIs in Charcot-Marie-Tooth disease type 1 A (CMT1A) patients, using a deep learning-based automated muscle segmentation ...
This study investigates the relationship between AI ethics literacy and students' self-rated learning competence using AI by developing a comprehensive framework of AI ethics literacy comprising knowledge, attitude, and competence dimensions. Data we...
Given the prevalence of depression among young adults, particularly those aged 18-25, this study aims to address a critical need in higher education institutions for proactive, private, automated mental health self-awareness. This study protocol outl...
INTRODUCTION: Hypertension is a leading contributor to maternal and cardiometabolic morbidity in Bangladesh. We developed and interpreted machine-learning (ML) models to predict hypertension and rank associated factors among married women with the go...
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