Hereditary thoracic aortic diseases (HTAD) are often associated with multifaceted phenotypic manifestations in different anatomical districts, including skeletal abnormalities. Therefore, diagnostic criteria account for multiple parameters to compute a systemic risk score. Despite the forefoot is known to be different in HTAD, its complex morphology is difficult to be quantified objectively and it is not currently considered in diagnostic criteria. Here, we investigated the potential application of artificial intelligence to compute a HTAD risk score from smartphone-acquired images of the forefoot. To this end, we conducted a pilot study including 44 adults, of which 22 had high risk of HTAD (in line with EACTS/STS guidelines 2024 and revised Ghent criteria). The remaining 22 individuals did not show characteristic features indicative of HTAD. A deep learning architecture was then trained to compute a risk score using specific strategies to account for limited sample sizes: transfer learning and leave-one-out cross validation. The computed risk score was significantly higher in the HTAD group with respect to the control group (p < 0.0001), achieving remarkable sensitivity (82%) and specificity (91%), with an AuC of 0.94. Altogether this study highlights the usefulness of AI to assist the analysis of complex morphological traits, suggesting an association with forefoot abnormalities with HTAD and potentially enabling a greater number of healthcare professionals to identify patients at risk. The study was approved by the Swiss Cantonal Ethics Committee with protocol number 2023-00643.
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The diagnosis of ascending aortic aneurysm, its precise assessment criteria and indications for surgery represent a chapter of great stimulus and interest for the clinical cardiologist. There are many factors influencing the classification of patients, but it is the contemporary evolution of knowledge in this field that contributes to a more global view of the aorta. The approach is increasingly multidisciplinary, both in clinical and genetic terms, and multimodal imaging is a crucial support for therapeutic decisions and monitoring. All these aspects, together with the most recent developments, will be explored and reasoned about in this review.
Sex and gender is the original name of gender medicine. It is important to define medical concepts without ignoring key terminology. The purpose of and gender is to focus on both sex and gender differences, to analyze how these two sides of the human being overlap and, finally, to improve their medical understanding. On the one hand sex, besides defining male and female, refers to the biological differences among humans, animals, tissues and cells. On the other, the concept of gender is applicable only to humans, and includes identity, roles and relations in the society. However, despite its 20 years of history, gender medicine is still little known. Biological differences among cardiovascular diseases are ignored. Symptoms and their expressions, which may be different in women, are often described as atypical because of the masculine vision of the heart attack and pain. Similarly, anxious syndrome is often conceived as the first reason to explain chest discomfort in women. In reality, prejudices and vagueness around women still dominate prevention and medical treatment. Our objective is to distinguish the concepts of sex and gender in order to understand the best way to face differences and medical knowledge in both.