Genomic determinants of biological age estimated by deep learning applied to retinal images.

With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between chronological age and predicted retinal age has been established previously to predict the age-related disease. In this study, we performed discovery genome-wide association analysis (GWAS) on the RAG using the 31,271 UK Biobank participants and replicated our findings in 8034 GoDARTS participants.

Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes.

Background: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score.

Methods: We included 6127 individuals with type 2 diabetes without myocardial infarction or stroke prior to study entry.

Internalizing Pathways to Adolescent Substance Use from Adverse Childhood Experiences.

The mediating role of anxious, depressive, and somatic symptoms was examined in the association between adverse childhood experiences (ACEs) and adolescent substance use, with attention to the unique effects of each set of symptoms within the same model. Adolescents (n = 701) were assessed over time (ages 3-17) in a majority male (70.5%) and white (89.

Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT.

Background: CT is commonly used to image patients with ischaemic stroke but radiologist interpretation may be delayed. Machine learning techniques can provide rapid automated CT assessment but are usually developed from annotated images which necessarily limits the size and representation of development data sets. We aimed to develop a deep learning (DL) method using CT brain scans that were labelled but not annotated for the presence of ischaemic lesions.

Towards Integrating Federated Learning with Split Learning via Spatio-temporal Graph Framework for Brain Disease Prediction.

Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, collecting and labeling the data is time-consuming and costly, which limits the amount of valid data collected at a single healthcare site; on the other hand, integrating data from multiple sites is challenging due to data privacy restrictions. To address these issues, we propose a novel, integrated Federated learning and Split learning Spatio-temporal Graph framework (FSG).

Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images.

Segmentation masks of pathological areas are useful in many medical applications, such as brain tumour and stroke management. Moreover, healthy counterfactuals of diseased images can be used to enhance radiologists' training files and to improve the interpretability of segmentation models. In this work, we present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map.

Do Acculturative Gap Conflicts Impact Parenting Practices and Youth E-Cigarette Use? Tests of Moderated Mediation.

Background: Latino/a youth are at increased risk of electronic (e)-cigarette or electronic nicotine delivery systems (ENDS) use; thus, identifying factors impacting initiation is critical. Parenting practices reflecting warmth (e.g.

Cardiac implantable electronic device infection awareness - A European survey amongst implanting physicians.

Background And Aims: Cardiac Implantable Electronic Device (CIED) infections pose significant mortality and morbidity despite optimal treatment. This survey aimed to understand whether and how the risk of CIED infection is assessed and mitigated in clinical practice in Europe, and to detect gaps with respect to EHRA recommendations.

Methods: An Expert Group of 8 European cardiologists with specific expertise across CIED therapy designed and distributed electronically a survey to a number of European Cardiologists.

Association of optic disc pallor and RNFL thickness with cerebral small vessel disease in the PREVENT-Dementia study.

Introduction: We tested associations between two retinal measures (optic disc pallor, peripapillary retinal nerve fiber layer [pRNFL] thickness) and four magnetic resonance imaging markers of cerebral small vessel disease (SVD; lacunes, microbleeds, white matter hyperintensities, and enlarged perivascular spaces [ePVSs]).

Methods: We used PallorMetrics to quantify optic disc pallor from fundus photographs, and pRNFL thickness from optical coherence tomography scans. Linear and logistic regression assessed relationships between retinal measures and SVD markers.

PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness.

Purpose: We sough to develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fiber layer (pRNFL) thickness.

Methods: We used deep learning to segment the optic disc, fovea, and vessels in fundus photographs, and measured pallor. We assessed the relationship between pallor and pRNFL thickness derived from optical coherence tomography scans in 118 participants.

The e-cigarette assessment for youth-Revised (EAsY-R): Preliminary results of a pilot study of measure refinement via cognitive interviewing.

The prevalence of youth vaping has, in a relatively short time, become an "epidemic." In the wake of such labeling by the Surgeon General, a number of important examinations of vaping have been conducted. These have largely focused on high school and college-age youth as this demographic shows the greatest prevalence of use.

From data to diagnosis: skin cancer image datasets for artificial intelligence.

Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. This review provides an overview of the datasets used to develop AI algorithms and highlights the importance of dataset transparency for the evaluation of algorithm generalizability across varying populations and settings.

Mother-child dynamics: Examining reciprocal relations between parental knowledge, child disclosure, parental legitimacy beliefs, and adolescent alcohol use.

Early adolescent alcohol use is associated with adverse developmental and health outcomes. Parental knowledge can prevent or delay substance use, while youth behaviors may concurrently influence parenting. More research is needed to examine the role of youth's perceptions of legitimacy of parental authority.

Ultra-Widefield Imaging of the Retinal Macrovasculature in Parkinson Disease Versus Controls With Normal Cognition Using Alpha-Shapes Analysis.

Purpose: To investigate retinal vascular characteristics using ultra-widefield (UWF) scanning laser ophthalmoscopy in Parkinson disease (PD).

Methods: Individuals with an expert-confirmed clinical diagnosis of PD and controls with normal cognition without PD underwent Optos California UWF imaging. Patients with diabetes, uncontrolled hypertension, glaucoma, dementia, other movement disorders, or known retinal or optic nerve pathology were excluded.

The Scottish Medical Imaging Archive: 57.3 Million Radiology Studies Linked to Their Medical Records.

MRI, Imaging Sequences, Ultrasound, Mammography, CT, Angiography, Conventional Radiography Published under a CC BY 4.0 license. See also the commentary by Whitman and Vining in this issue.

Classifying real-world macroscopic images in the primary-secondary care interface using transfer learning: implications for development of artificial intelligence solutions using nondermoscopic images.

Background: The application of deep learning (DL) to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion classification performance on curated datasets comparable to that of experienced dermatologists. Most skin disease images encountered in clinical settings are macroscopic, without dermoscopic information, and exhibit considerable variability. Further research is necessary to determine the generalizability of DL algorithms across populations and acquisition settings.

Interoception, alexithymia, and anxiety among individuals with alcohol use disorder.

Background: Interoception (i.e., the ability to recognize bodily signals), alexithymia (i.