Feasibility of Real-Time Automated Vocal Fold Motion Tracking for In-Office Laryngoscopy.

Objectives: Major advancements have been made in applying artificial intelligence and computer vision to analyze videolaryngoscopy data. These models are limited to post hoc analysis and are aimed at research settings. In this work, we assess the feasibility of a real-time solution for automated vocal fold tracking during in-office laryngoscopy.

First-trimester nuclear magnetic resonance-based metabolomic profiling increases the prediction of gestational diabetes mellitus.

Background: Current strategies for predicting gestational diabetes mellitus demonstrate suboptimal performance.

Objective: To investigate whether nuclear magnetic resonance-based metabolomic profiling of maternal blood can be used for first-trimester prediction of gestational diabetes mellitus.

Study Design: This was a prospective study of 20,000 women attending routine pregnancy care visits at 11 to 13 weeks' gestation.

Newborn Screening for High-Risk Congenital Heart Disease by Dried Blood Spot Biomarker Analysis.

Importance: Congenital heart disease (CHD) is the most common human organ malformation, affecting approximately 1 of 125 newborns globally.

Objectives: Assessing the performance of 2 diagnostic tests using minimal amounts of dried blood spots (DBS) to identify high-risk CHD compared with controls in a Swedish cohort of neonates.

Design, Setting, And Participants: This diagnostic study took place in Sweden between 2019 and 2023 and enrolled full-term babies born between 2005 and 2023.

Evaluation of Circulating Cardiovascular Biomarker Levels for Early Detection of Congenital Heart Disease in Newborns in Sweden.

Importance: Congenital heart disease (CHD) is the most common congenital malformation in humans worldwide. Circulating cardiovascular biomarkers could potentially improve the early detection of CHD, even in asymptomatic newborns.

Objectives: To assess the performance of a dried blood spot (DBS) test to measure the cardiovascular biomarker amino terminal fragment of the prohormone brain-type natriuretic peptide (NT-proBNP) levels in newborns and to compare DBS with standard EDTA analysis in control newborns during the first week of life.

Synthetic minority oversampling of vital statistics data with generative adversarial networks.

Objective: Minority oversampling is a standard approach used for adjusting the ratio between the classes on imbalanced data. However, established methods often provide modest improvements in classification performance when applied to data with extremely imbalanced class distribution and to mixed-type data. This is usual for vital statistics data, in which the outcome incidence dictates the amount of positive observations.

Predicting risk of stillbirth and preterm pregnancies with machine learning.

Modelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study, we utilize state-of-the-art machine learning methods in the task of predicting early stillbirth, late stillbirth and preterm birth pregnancies.

Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.

Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome.