Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

Southern European countries have milder climatic conditions that differ from their colder northern counterparts, which greatly influence indoor ventilation strategies. The relation between a building’s airtightness and the air change rates remains a topic to be fully tackled in these countries, since natural ventilation is very frequent. In this work, the ventilation and airtightness of a case study were analyzed in-depth to support a discussion on this topic. CO₂ concentration decay and blower-door measurements were used to characterize the infiltration and ventilation conditions of the case study. The case study represents a common Portuguese situation, with highly permeable envelopes, combined with highly variable air change rates. Transient simulations were carried out for the comparison of scenarios where different configurations of possible air paths were analyzed. The simulations included both natural and mechanical ventilation scenarios. An air sweeping effect from bedroom to bathroom only occurred when the mechanical extraction ventilation (MEV) was on. Different air leakage path configurations resulted in substantial offsets, up to 63%, of the air change rate (ACH) due to natural occurring forces. The results confirmed that the relation between airtightness and air change rates should be carefully analyzed in southern European countries, as indoor air quality, comfort of occupants, and energy efficiency are highly influenced by the considered variables.