Traditional and Virtual Congress Meetings During the COVID-19 Pandemic and the Post-COVID-19 Era: Is it Time to Change the Paradigm?

The speed and reach of the COVID-19 pandemic have forced rapid changes in how we conduct medical practice and research. The rapid evolution in how scientific meetings are conducted may have long-term benefits. A new reality in which technology and sociality are merged may offer a more engaging and adaptable scientific congress experience with more flexible and dynamic use of content modulated to the needs of each attendee

Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient's organ with its 2D endoscopic image, to assist surgeons during the procedure

ESA ESRIN'S VALUE FOR ITALY

The report presents an overview of the evolution of ESA's ESRIN stablishment. The programs and activities carried out by ESRIN are then analyzed, with particular attention to the Earth Observation and VEGA programs. We then moved on to assessing the economic and strategic impact of ESRIN for Italy. In particular, an estimate was made of the direct and indirect economic impact and of the scientific value developed by ESRIN on the Italian territory

Machine Learning Techniques in Prostate Cancer Diagnosis According to Prostate-Specific Antigen Levels and Prostate Cancer Gene 3 Score

Purpose: To explore the role of artificial intelligence and machine learning (ML) techniques in oncological urology. In recent years, our group investigated the prostate cancer gene 3 (PCA3) score, prostate-specific antigen (PSA), and free-PSA predictive role for prostate cancer (PCa), using the classical binary logistic regression (LR) modeling. In this research, we approached the same clinical problem by several different ML algorithms, to evaluate their performances and feasibility in a real-world evidence PCa detection trial.Materials and Methods: The occurrence of a positive biopsy has been studied in a large cohort of 1,246 Italian men undergoing first or repeat biopsy. Seven supervised ML algorithms were selected to build biomarkers-based predictive models: generalized linear model, gradient boosting machine, eXtreme gradient boosting machine (XGBoost), distributed random forest/ extremely randomized forest, multilayer artificial Deep Neural Network, naïve Bayes classifier, and an automatic ML ensemble function.Results: All the ML models showed better performances in terms of area under curve (AUC) and accuracy, when compared to LR model. Among them, an XGBoost model tuned by the autoML function reached the best metrics (AUC, 0.830), well overtaking LR results (AUC, 0.738). In the variable importance ranking coming from this XGBoost model (accuracy, 0.824), the PCA3 score importance was 3-fold and 4-fold larger, when compared to that of free-PSA and PSA, respectively.Conclusions: The ML approach proved to be feasible and able to achieve good predictive performances with reproducible results: it may thus be recommended, when applied to PCa prediction based on biomarkers fluctuations