Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recognition necessitates continuous monitoring, encompassing not only specific clinical conditions but also diverse lifestyles. Consequently, an increasing number of studies are striving to automate and progress in the identification of different cardiopathies. Notably, the assessment of electrocardiograms (ECGs) is crucial, given that it serves as the initial diagnostic test for patients, proving to be both the simplest and the most cost-effective tool. This research employs a customized architecture of Convolutional Neural Network (CNN) to forecast heart diseases by analyzing the images of both three bands of electrodes and of each single electrode signal of the ECG derived from four distinct patient categories, representing three heart-related conditions as well as a spectrum of healthy controls. The analyses are conducted on a real dataset, providing noteworthy performance (recall greater than 80% for the majority of the considered diseases and sometimes even equal to 100%) as well as a certain degree of interpretability thanks to the understanding of the importance a band of electrodes or even a single ECG electrode can have in detecting a specific heart-related pathology.

Parkinson’s Disease (PD) is a neurodegenerative condition primarily affecting the elderly but also occurring in younger individuals. It is caused by a progressive loss of nerve cells in the brain’s substantia nigra that release dopamine, essential for controlling movements. Dopamine deficiency results in symptoms affecting both motor and non-motor functions, which vary among individuals. Diagnosis relies on clinical symptoms and medical history, often supported by brain scans, as there is no specific diagnostic test available. Diagnosis is challenging due to vague initial symptoms resembling other conditions. Current research indicates that AI can significantly enhance data and image analysis, aiding in the diagnosis and monitoring of PD progression. To this aim, this study proposes a hybrid model allowing the integrated use of clinical data and single photon emission computed tomography images of a patient to predict the presence of the disease. The approach consists of a combination of two types of neural networks, an LSTM for clinical data and a CNN for images. The validation is performed on a widely validated dataset belonging to the Parkinson’s Progression Markers Initiative, from which the data recording visits of 1,814 patients were extracted. The obtained results are interesting and useful to address further investigations.

The architectural space impacts the emotional state of its inhabitants. Nevertheless, no studies have investigated, to date, how it influences the perception of others' affective states, possibly impacting our social behavior. This paper analyzes the eye-gaze data collected during a social scenario recreated after a promenade within virtual architectures. Immersive and dynamic virtual architectures were characterized by decreasing or increasing sidewall distance, ceiling height, windows height, and different colors. At the end of such an experience, participants judged the arousal level expressed by a virtual avatar. For the first time, we apply machine and deep learning techniques to the behavioral, environmental, and eye-gaze features extracted during the dynamic experience of virtual architectures. In order to verify the feasibility of automated classification of the final arousal judgment on the avatar emotional expression, we have considered both interpretable, i.e., decision trees, and black-box models, i.e., dense neural networks. The decision tree reached an accuracy rate of 66%, showing the importance of eye-gaze parameters to classify the participants' arousal judgment. The black-box dense neural network increased the accuracy up to 80%. Overall, our findings demonstrate the capability of artificial intelligence methodologies to classify and possibly predict the arousal judgment of body expressions at the end of a virtual promenade. Such knowledge will serve the design and evaluation of future spaces by combining virtual reality and artificial intelligence within the experience of architecture. In such a way, it will be possible to predict the influence of the surrounding architecture on human social behavior.