- “Methodological Approach for the Analysis and Segmentations of CT Scans in the Vi.CURA Project”:
3D reconstruction of the heart can be very useful in medicine because such models can provide clinicians with more structural and functional details and can also be used for educational purposes within Universities. Computer vision techniques with supervised segmentation are used to achieve this goal.
- " Latest Advances in Vision and Artificial Intelligence Techniques: Applications in the Detection and Prevention of Chronic and Infectious Diseases ":
In biomedical detection, AI innovations are making notable strides across various health conditions.
For intradialytic hypotension, AI enables the prediction of hypotensive events during dialysis by analyzing real-time physiological data, allowing for timely interventions.
In institutionalized elderly populations, AI systems can detect infections earlier than traditional methods, enabling proactive measures before a doctor can make a diagnosis.
For sexually transmitted infections (STIs), AI improves diagnostic accuracy by analyzing smartphone images of samples taken under a microscope.
Overall, these advancements highlight AI's transformative impact on healthcare, offering more timely, accurate, and personalized approaches to diagnosis and management.
- “Introduction to the Use of B-Lines in Ultrasound for Dialysis Patients: State of the Art in AI Application for Ultrasound Image Analysis”:
Lung ultrasound (LUS) is nowadays gaining growing attention from both the clinical and technical world. B-line artifacts correlate to an increase in extravascular lung water, interstitial lung diseases, cardiogenic and non-cardiogenic lung edema. Detection and localization of B- lines in a LUS video are therefore tasks of great clinical interest, with accurate, objective and timely evaluation being critical. Method aimed at supporting clinicians by automatically detecting and localizing B-lines in an ultrasound scan have been recently described. | 
