Using artificial intelligence (AI) offers opportunities to analyze medical images and to support early cancer detection. For instance, neural networks, in different implementation, can be used to analyze data image parts (i.e., voxels), defining a trained network useful for lung cancer nodules detection. We present our experience in designing and testing a transformerbased deep learning architecture, aiming to detect pulmonary cancer nodule candidates using 3D Computed Tomography (CT) images. The module also includes a preprocessing pipeline based on dynamic sampling of voxels extracted from images, to support data filtering and results explainability. The proposed architecture has been implemented, trained, and tested using the LUNA16 publicly available dataset. Experimental results proved both high effectiveness and competitive performance metrics across standard evaluations. Trained module can be used on a large CT dataset aiming to support clinicians in lung cancer early detection as well as to support in followup for lung cancer patients treatments. This work represent, indeed, results for preliminary applications in a research project (Advancing Lung Cancer Screening: Artificial Intelligence, Multimodal Imaging and Cutting-Edge Technologies for Early Detection and Characterization), conducted in collaboration with San Raffaele Hospital (Italy), Campus Biomedico University (Italy) and University Hospital of Salerno.

There is increasing evidence that many molecular processes exhibit differences with age and sex. Such differences produce also differences in the insurgence and progression of many complex diseases. For instance, demographic data on the insurgence of comorbidities of mellitus diabetes, on the lethality of COVID-19, and on some cancers shows differences between sex and age groups. Therefore, the growing interest in such areas requires the management of related data as well as the development of algorithms and tools for the analysis. The availability of omics data annotated with metadata related to age and sex is mandatory for building the analysis pipeline. The number of databases containing data related to age and sex is henceforth growing. We here show some databases and tools storing such data. Finally, future research directions are highlighted.

Healthcare access (HA) is a multi-dimensional concept that includes health services availability and accessibility for the populations. These services should be determined by population healthcare needs, especially for vulnerable populations. Digital healthcare service became more important to facilitate the access to medical care by vulnerable people and by citizens in general. Digital health and technologies have provided many online e-services to address social and healthcare services.In this contribution, we propose the implementation of an innovative platform to support and manage the access to health and social services for vulnerable people. Two different use cases have been proposed to demonstrate the application of this platform to different healthcare contexts. The results shows the benefits of using the platform in terms of request management times and reduction of hospitalization.

Magnetic Resonance Images (MRI) allow to extract meaningful structural information. Machine learning and neural network based algorithms are used to analyze such images, to extract features and to identify anomalies related to diseases. To perform anomaly detection tasks in MR images of the human brain, we propose the use of the Variational AutoEncoder (VAE) method. A VAE is a deep-learning method able to compress and reconstruct the original image through well-defined functions aiming to extract only significant features that are used to identify abnormal pattern. In this contribution, we present a tool based on VAE method for the identification and annotation of brain lesions in MRI aiming to support physicians in the detection of anomalies. Moreover, a MongoDB database is also used to store the data and manage the annotations.

Patient medical records contain several types of data, such as images, signals, or textual data. The integration of such data on a single system provides the possibility to select the clinical data of interest and then to choose the information extraction operation to be performed on such data. Formulating diagnoses of complex diseases is a challenging task, which is often the key to the precise identification of the correct therapies. Hence, a uniforming environment for data clinical staging, in which physicians can perform data annotations and images manipulation could be of great help in order to convey relevant information forming the clinical summary of a patient with great precision and detail. In this work we present a semi-automatic tool for clinical data annotation aiming to be a decision support system.