Glioblastoma Multiforme (GBM) is considered one of the most aggressive malignant tumors, characterized by a tremendously low survival rate. Despite alkylating chemotherapy being typically adopted to fight this tumor, it is known that O(6)-methylguanine-DNA methyltransferase (MGMT) enzyme repair abilities can antagonize the cytotoxic effects of alkylating agents, strongly limiting tumor cell destruction. However, it has been observed that MGMT promoter regions may be subject to methylation, a biological process preventing MGMT enzymes from removing the alkyl agents. As a consequence, the presence of the methylation process in GBM patients can be considered a predictive biomarker of response to therapy and a prognosis factor. Unfortunately, identifying signs of methylation is a non-trivial matter, often requiring expensive, time-consuming, and invasive procedures. In this work, we propose to face MGMT promoter methylation identification analyzing Magnetic Resonance Imaging (MRI) data using a Deep Learning (DL) based approach. In particular, we propose a Convolutional Neural Network (CNN) operating on suspicious regions on the FLAIR series, pre-selected through an unsupervised Knowledge-Based filter leveraging both FLAIR and T1-weighted series. The experiments, run on two different publicly available datasets, show that the proposed approach can obtain results comparable to (and in some cases better than) the considered competitor approach while consisting of less than $0.29\%$ of its parameters. Finally, we perform an eXplainable AI (XAI) analysis to take a little step further toward the clinical usability of a DL-based approach for MGMT promoter detection in brain MRI.

Hospital overcrowding, driven by both structural management challenges and widespread medical emergencies, has prompted extensive research into machine learning (ML) solutions for predicting patient length of stay (LOS) to optimize bed allocation. While many existing models simplify the LOS prediction problem to a classification task, predicting broad ranges of hospital days, an exact day-based regression model is often crucial for precise planning. Additionally, available data are typically limited and heterogeneous, often collected from a small patient cohort. To address these challenges, we present a novel multimodal ML framework that combines imaging and clinical data to enhance LOS prediction accuracy. Specifically, our approach uses the following: (i) feature extraction from chest CT scans via a convolutional neural network (CNN), (ii) their integration with clinically relevant tabular data from patient exams, refined through a feature selection system to retain only significant predictors. As a case study, we applied this framework to pneumonia patient data collected during the COVID-19 pandemic at two hospitals in Naples, Italy—one specializing in infectious diseases and the other general-purpose. Under our experimental setup, the proposed system achieved an average prediction error of only three days, demonstrating its potential to improve patient flow management in critical care environments.

Fasciola hepatica and Calicophoron daubneyi are trematodes with significant health and economic impacts on ruminant livestock farms. An effective and reliable diagnosis is essential to control their spread. To improve copromicroscopic diagnosis, the Kubic FLOTAC Microscope (KFM), a portable digital microscope, was designed for both laboratory and field use. It is based on the use of FLOTAC/Mini-FLOTAC techniques and combines their high sensitivity, accuracy and precision with a reliable system based on an Artificial Intelligence (AI) predictive model. It features automated parasite egg detection, powered by an integrated battery, a web interface for microscope control, and a dedicated AI server for image analysis. In this study, the system was optimized to better discriminate between the eggs of these two parasites through additional processing steps and a robust detection model. Two protocols, egg-spiked samples and naturally infected samples, were used to simulate different sample conditions, creating a dataset for model training and evaluation. A second dataset of field samples, with egg counts verified by optical microscopy, was used to assess performance. The detection performance during the evaluation of samples from both protocols was found to be satisfactory. Specifically, the average fecal egg count, obtained through the clinical report generated by the KFM system, exhibited a mean absolute error of only 8 eggs per sample. This result demonstrates that the KFM is a valuable tool for parasitological diagnosis that supports the livestock industry.