Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

Emotion recognition through machine learning techniques is a widely investigated research field, however the recent obligation to wear a face mask, following the COVID19 health emergency, precludes the application of systems developed so far. Humans naturally communicate their emotions through the mouth; therefore, the intelligent systems developed to date for identifying emotions of a subject primarily rely on this area in addition to other anatomical features (eyes, forehead, etc..). However, if the subject is wearing a face mask this region is no longer visible. For this reason, the goal of this work is to develop a tool able to compensate for this shortfall. The proposed tool uses the AffectNet dataset which is composed of eight class of emotions. The iterative training strategy relies on well-known convolutional neural network architectures to identify five sub-classes of emotions: following a pre-processing phase the architecture is trained to perform the task on the eight-class dataset, which is then recategorized into five classes allowing to obtain 96.92% of accuracy on the testing set. This strategy is compared to the most frequently used learning strategies and finally integrated within a real time application that allows to detect faces within a frame, determine if the subjects are wearing a face mask and recognize for each one the current emotion.

Despite technological advances in diagnostic imaging, to distinguish the type of renal tumor without performing a biopsy is still an unsolved challenge. In particular, this is even more striking in the case of clear cell renal cell carcinoma and small oncocytomas. To tackle this problem, a fully automated tool is proposed that can provide decision support for physicians to distinguish between these two types of masses in the most critical cases. In this work three approaches for the development of this tool are implemented and compared, specifically two approaches are based on the use of radiomic features and one on the use of deep features. The nnU-net is exploited to achieve tumor segmentation necessary to obtain the different types of features. The architectures are trained and tested by combining two different datasets, the public dataset KiTS2019 and data from the Careggi University Hospital. The best method is able to obtain 73.77% balanced accuracy, 94.59% sensitivity, 52.94% specificity and 86.84% accuracy.

Industry 4.0 conceptualizes the automation of processes through the introduction of technologies such as artificial intelligence and advanced robotics, resulting in a significant production improvement. Detecting defects in the production process, predicting mechanical malfunctions in the assembly line, and identifying defects of the final product are just a few examples of applications of these technologies. In this context, this work focuses on the detection of ultrasound probes’ surface defects, with a focus on Esaote S.p.A.’s production line probes. To date, this control is performed manually and therefore biased by many factors such as surface morphology, color, size of the defect, and by lighting conditions (which can cause reflections preventing detection). To overcome these shortfalls, this work proposes a fully automatic machine vision system for surface acquisition of ultrasound probes coupled with an automated defect detection system that leverage artificial intelligence. The paper addresses two crucial steps: (i) the development of the acquisition system (i.e., selection of the acquisition device, analysis of the illumination system, and design of the camera handling system); (ii) the analysis of neural network models for defect detection and classification by comparing three possible solutions (i.e., MMSD-Net, ResNet, EfficientNet). The results suggest that the developed system has the potential to be used as a defect detection tool in the production line (full image acquisition cycle takes ~ 200 s), with the best detection accuracy obtained with the EfficientNet model being 98.63% and a classification accuracy of 81.90%.

Point clouds have become a widely used data format in computer vision, driven by the increasing availability of 3D scanning devices in applications such as robotics and autonomous driving. However, challenges in data acquisition, such as occlusion, reflection, and limited sensor resolution, often result in incomplete scans. Consequently, point cloud completion, the task of reconstructing the complete geometry of a point cloud object from partial data, has emerged as a key area of research. Numerous deep learning-based methods have been proposed to tackle this problem. Although some surveys on shape completion exist, the rapid growth of research in recent years calls for an updated review. In this work, we present a comprehensive survey of recent advancements in deep neural networks for point cloud completion, covering literature from 2024 up to December 2025. After establishing a reference context by analyzing the major foundational methods up to the year 2023, we review the most recent methods with an analytical perspective based on how they have improved each specific component of the network. Furthermore, we present a comparative analysis of the performance of the reviewed methods on the main benchmark datasets, and finally, we discuss open challenges, offering insights and suggestions for future research directions.