The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by aggregating automatically extracted biometrics from every frame across an entire scan, with no need for operator intervention. We use a neural network to classify each frame of an ultrasound video recording.
View Article and Find Full Text PDFExecutive functioning (EF) has been linked to chronic disease risk in children. Health behaviors are thought to partially explain this association. The current cross-sectional study evaluated specific domains of EF and varied health behaviors in three pediatric life stages.
View Article and Find Full Text PDFWe report a 28-year-old G2P0 at 24 weeks 5 days who presented for evaluation secondary to suspected skeletal dysplasia in her fetus. Fetal ultrasound imaging demonstrated foreshortened long bones by 9-10 weeks, multiple bowing deformities and fractures, 11 foreshortened paired ribs with fractures, decreased skull mineralization, frontal bossing, enlarged cavum septum pellucidi, and severe fetal growth restriction (< 2%). Findings were concerning for life limiting condition with thoracic circumference < 2.
View Article and Find Full Text PDFObjectives: Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements.
Methods: A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI.
(1) Background: Research on mental health literacy (MHL) and attitudes toward mental health problems (ATMHP) among non-medical college students in Nepal is limited. This study examined the relationship between MHL and ATMHP, considering demographic variables and familiarity with mental health issues; (2) Methods: We conducted a cross-sectional survey with 385 college students from Chitwan and Kathmandu, Nepal, using opportunity sampling. Descriptive and inferential statistics examined demographic differences, while Pearson's correlation assessed relationships among latent variables; (3) Results: No relationship was found between MHL and ATMHP (r = -0.
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