Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges.

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, and tabular data (EHR). Unlike previous narrowly focused reviews, our study encompasses a broad array of medical data modalities and explores various generative models. Our aim is to offer insights into their current and future applications in medical research, particularly in the context of synthesis applications, generation techniques, and evaluation methods, as well as providing a GitHub repository as a dynamic resource for ongoing collaboration and innovation.

Role of Maternal Vitamin D Levels in Shaping Adolescent Vascular Health: Evidence From a Spanish Population-Based Birth Cohort.

Background: Low gestational vitamin D levels may increase offspring risk of cardiovascular disease from an early age. Studies investigating the impact on offspring macrovascular function have been inconsistent. Few included pulse wave velocity as an arterial stiffness indicator, and none included measures of microvascularization as an early marker of cardiovascular health.

Effects of Different Combinations of Phytochemical-Rich Fruits and Vegetables on Chronic Disease Risk Markers and Gene Expression Changes: Insights from the MiBLEND Study, a Randomized Trial.

Adequate fruit and vegetable (F and V) intake, as recommended by the World Health Organization (over 400 g/day), is linked to reduced chronic disease risk. However, human intervention trials, especially with whole F and V and in complex combinations, are lacking. The MiBlend Study explored the effects of various phytochemical-rich F and V combinations on chronic disease risk markers, phytochemical absorption, and gene expression in blood.

Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification.

Aim: To evaluate the MONA.health artificial intelligence screening software for detecting referable diabetic retinopathy (DR) and diabetic macular edema (DME), including subgroup analysis.

Methods: The algorithm's threshold value was fixed at the 90% sensitivity operating point on the receiver operating curve to perform the disease classification.

Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning.

Purpose: Standard automated perimetry is the gold standard to monitor visual field (VF) loss in glaucoma management, but it is prone to intrasubject variability. We trained and validated a customized deep learning (DL) regression model with Xception backbone that estimates pointwise and overall VF sensitivity from unsegmented optical coherence tomography (OCT) scans.

Methods: DL regression models have been trained with four imaging modalities (circumpapillary OCT at 3.

Deep learning on fundus images detects glaucoma beyond the optic disc.

Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. Here, we propose a methodology that advances explainable deep learning in the field of glaucoma detection and vertical cup-disc ratio (VCDR), an important risk factor. We trained and evaluated deep learning models using fundus images that underwent a certain cropping policy.

Pathological myopia classification with simultaneous lesion segmentation using deep learning.

Background And Objectives: Pathological myopia (PM) is the seventh leading cause of blindness, with a reported global prevalence up to 3%. Early and automated PM detection from fundus images could aid to prevent blindness in a world population that is characterized by a rising myopia prevalence. We aim to assess the use of convolutional neural networks (CNNs) for the detection of PM and semantic segmentation of myopia-induced lesions from fundus images on a recently introduced reference data set.

Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection.

Purpose: Heatmapping techniques can support explainability of deep learning (DL) predictions in medical image analysis. However, individual techniques have been mainly applied in a descriptive way without an objective and systematic evaluation. We investigated comparative performances using diabetic retinopathy lesion detection as a benchmark task.

Age and sex affect deep learning prediction of cardiometabolic risk factors from retinal images.

Deep neural networks can extract clinical information, such as diabetic retinopathy status and individual characteristics (e.g. age and sex), from retinal images.

Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning.

Purpose: To assess the use of deep learning (DL) for computer-assisted glaucoma identification, and the impact of training using images selected by an active learning strategy, which minimizes labelling cost. Additionally, this study focuses on the explainability of the glaucoma classifier.

Methods: This original investigation pooled 8433 retrospectively collected and anonymized colour optic disc-centred fundus images, in order to develop a deep learning-based classifier for glaucoma diagnosis.

Artery-vein segmentation in fundus images using a fully convolutional network.

Epidemiological studies demonstrate that dimensions of retinal vessels change with ocular diseases, coronary heart disease and stroke. Different metrics have been described to quantify these changes in fundus images, with arteriolar and venular calibers among the most widely used. The analysis often includes a manual procedure during which a trained grader differentiates between arterioles and venules.

Participatory Patterns in an International Air Quality Monitoring Initiative.

The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g.

The Aeroflex: a bicycle for mobile air quality measurements.

Fixed air quality stations have limitations when used to assess people's real life exposure to air pollutants. Their spatial coverage is too limited to capture the spatial variability in, e.g.