Coronary artery disease (CAD) is usually associated with the elderly, but an increase in its incidence has been recently reported among young people, including very young women. The aim of this study is to assess the associations between different clinical variables and the risk of early CAD and occurrence of major adverse cardiovascular events (MACEs) during follow-up. Our cohort consists of women ≤40 years referred for coronary angiography due to suspicion of CAD; a nested case–control study was conducted among these patients. In total, 19,321 coronary angiographies were performed between 2006 and 2015, of which 2.6% were in patients ≤40 years old; 52 women were finally included. Family history of CAD was strongly associated with the early onset of the disease [OR 5.94, 95%CI (1.13–31.15); p = 0.035] in young women. The incidence of MACE was also associated with depression [HR 8.20 95%CI (1.03–65.17); p = 0.047] and Castelli Index [HR 11.49, 95%CI (1.40–94.51); p = 0.023]. Primary prevention focused on genetic analysis for high-risk women with a family history of CAD and secondary prevention, targeting a better cholesterol management and mental health assistance must be considered.

This study introduces a novel Interleaved Fusion Learning (IFL) methodology leveraging transfer learning to generate a family of models optimized for specific datasets while maintaining superior generalization performance across others. The approach is demonstrated in cervical cancer screening, where cytology image datasets present challenges of heterogeneity and imbalance. By interleaving transfer steps across dataset partitions and regulating adaptation through a dynamic learning parameter, IFL promotes both domain-specific accuracy and cross-domain robustness. To evaluate its effectiveness, complementary metrics are used to capture not only predictive accuracy but also fairness in performance distribution across datasets. Results highlight the potential of IFL to deliver reliable and unbiased models in clinical decision support. Beyond cervical cytology, the methodology is designed to be scalable to other medical imaging tasks and, more broadly, to domains requiring equitable AI solutions across multiple heterogeneous datasets.

Understanding Alzheimer’s Disease (AD) progression using resting-state functional Magnetic Resonance Imaging (rs-fMRI) remains an open challenge. Variational Autoencoders (VAEs) provide compact representations of high-dimensional neuroimaging data but lack mechanisms to highlight disease-relevant features. We propose KS-VAE, a novel framework that integrates the Kolmogorov–Smirnov test into the latent space of VAEs to identify statistically significant variables discriminating healthy from pathological brain states. This integration enables measurement of latent space shifts associated with cognitive decline, offering a quantitative approach to neurodegenerative processes. By modifying the most relevant variables, KS-VAE generates synthetic samples that simulate transitions between clinical conditions while preserving anatomical plausibility. The method enhances the modeling of temporal and distributional dynamics underlying disease progression and provides interpretable analysis of class-relevant features. Applied to rs-fMRI scans of 220 subjects from the ADNI cohort, KS-VAE demonstrated robust class separation between cognitively normal and Alzheimer’s disease subjects, achieving a classification accuracy of 84.5% and an F1-score of 84.5%, and clinically consistent synthetic transitions. KS-VAE thus offers a statistically grounded and clinically interpretable framework for understanding Alzheimer’s disease progression.

Creutzfeldt–Jakob disease (CJD) is a rare, degenerative, and fatal brain disorder caused by abnormal proteins called prions. This research introduces a novel approach combining AI and graph theory to analyze histopathological microscopic images of brain tissues affected by CJD. The detection and quantification of spongiosis, characterized by the presence of vacuoles in the brain tissue, plays a crucial role in aiding the accurate diagnosis of CJD. The proposed methodology employs image processing techniques to identify these pathological features in high-resolution medical images. By developing an automatic pipeline for the detection of spongiosis, we aim to overcome some limitations of manual feature extraction. The results demonstrate that our method correctly identifies and characterize spongiosis and allows the extraction of features that will help to better understand the spongiosis patterns in different CJD patients.