Effective management of pediatric diabetes remains a clinical challenge, particularly due to the onset of lipodystrophy resulting from repeated insulin injections and inadequate rotation of injection sites. These complications adversely affect subcutaneous tissue integrity and insulin pharmacokinetics, ultimately compromising glycemic control. In this study, we introduce diPen, a novel smart case designed to integrate with commercial insulin pens and equipped with a dual-sensor system: an optical module for non-invasive lipodystrophy detection and an inertial measurement unit for monitoring injection-site rotation. To evaluate the feasibility of the proposed approach, a clinical study was conducted involving a pediatric cohort. The system employs a personalized machine learning pipeline, leveraging a leave-one-acquisition-out validation strategy to replicate realworld deployment scenarios, wherein newly acquired data from the same subject are evaluated without prior exposure during training. The system demonstrated promising performance in both detection and classification tasks, suggesting that di-Pen may represent a viable tool for enhancing insulin therapy through personalized, data-driven injection guidance and tissue health monitoring.

Nowadays diabetes still remains one of the leading causes of death worldwide and it has serious consequences if not properly treated. The advent of hybrid closed-loop systems, connection with consumer electronics and cloud-based data systems have hastened the advancement of diabetes technology. In the wake of this progress, we exploit information technology to make insulin pens smart so as to promote adherence to injection therapy and improve the socio-economic impact for the patient. In this respect, this work focuses on two main open issues, namely injection site rotation and lipodystrophies detection while the patient is taking the insulin. The first one is addressed collecting data with IMU sensor which are processed by a machine learning classifier to detect the injection site. The second one is tackled through a sensor equipped with two leds: features computed from such signals fed a one-class Support Vector Machine trained to recognise healthy tissue, so that samples different from those in the training set can be considered as lipodystrophies. The results obtained for the injection site recognition show an average accuracy larger than 0.957, whilst in the case of lipodystrophies detection we reach an accuracy greater than 0.95 using the IR led.

The involvement of axillary lymph node metastasis in breast cancer is one of the most important independent prognostic factors. While the metastasis of lymph node depends on primary tumour intrinsic behaviour, morphology and angioinvasivity, the involvement of the peritumoral tissue by the neoplastic cells also provides useful information for the potential tumour aggressiveness. The lymph node status is currently evaluated by histological invasive procedures with possible complications, asking for introducing safer approaches. Among different imaging techniques, the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) highlights physiological and morphological characteristics, reflecting breast lesions behaviour and aggressiveness. In the recent years, deep learning (DL) approaches, such as Convolutional Neural Networks, gained increasing popularity for biomedical image processing. Thanks to their ability to autonomously learn from images the set of features for the specific task to solve, they allow finding non-invasive alternatives to the standard procedures used up to now. This paper aims to evaluate the applicability of DL approaches for the axillary lymph node metastasis prediction, considering primary tumour DCE-MRI sequence. Differently from other work in the literature, we include a detailed analysis of healthy tissue influence in lymph node tumour spread through the evaluation of different tumour bounding options. Promising results are reported on a dataset of 153 patients with 155 malignant lesions.

The year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-ray images to recognize COVID-19 infected patients. In this context, we found out that convolutional neural networks perform well on a single dataset but struggle to generalize to other data sources. To overcome this limitation, we propose a late fusion approach where we combine the outputs of several state-of-the-art CNNs, introducing a novel method that allows us to construct an optimum ensemble determining which and how many base learners should be aggregated. This choice is driven by a two-objective function that maximizes, on a validation set, the accuracy and the diversity of the ensemble itself. A wide set of experiments on several publicly available datasets, accounting for more than 92,000 images, shows that the proposed approach provides average recognition rates up to 93.54% when tested on external datasets.

The year 2020 was marked by the worldwide COVID-19 pandemic, which caused over 2.5 million deaths by the end of February 2021. Different methods have been established since the beginning to identify infected patients and restrict the spread of the virus. In addition to laboratory analysis, used as the gold standard, several applications have been developed to apply deep learning algorithms to chest X-ray (CXR) images to diagnose patients affected by COVID-19. The literature shows that convolutional neural networks (CNNs) perform well on a single image dataset, but fail to generalize to other sources of data. To overcome this limitation, we present a late fusion approach in which multiple CNNs collaborate to diagnose the CXR scan of a patient, improving the generalizability. Experiments on three datasets publicly available show that the ensemble of CNNs outperforms stand-alone networks, achieving promising performance not only in cross-validation, but also when external validation is used, with an average accuracy of 95.18%.

Recent years have witnessed the rise of pathomics as a mean to describe histopathological images with quantitative biomarkers for predictive and prognostic ends, combining digital pathology, omic science and artificial intelligence. This novel research branch is the counterpart of radiomics which pursues the same aims extracting knowledge from radiological images. In this paper, we present the design of a pathomic deep learning-based system to predict the treatment outcome in non-small cell lung cancer patients. We describe the system design and optimization under the condition of limited data and limited training, with corresponding tests. The experimental results show the feasibility of the proposed scalable architecture providing also a comparison between different transfer learning strategies.

BACKGROUND: Biological phenomena usually evolves over time and recent advances in high-throughput microscopy have made possible to collect multiple 3D images over time, generating [Formula: see text] (or 4D) datasets. To extract useful information there is the need to extract spatial and temporal data on the particles that are in the images, but particle tracking and feature extraction need some kind of assistance. RESULTS: This manuscript introduces our new freely downloadable toolbox, the Visual4DTracker. It is a MATLAB package implementing several useful functionalities to navigate, analyse and proof-read the track of each particle detected in any [Formula: see text] stack. Furthermore, it allows users to proof-read and to evaluate the traces with respect to a given gold standard. The Visual4DTracker toolbox permits the users to visualize and save all the generated results through a user-friendly graphical user interface. This tool has been successfully used in three applicative examples. The first processes synthetic data to show all the software functionalities. The second shows how to process a 4D image stack showing the time-lapse growth of Drosophila cells in an embryo. The third example presents the quantitative analysis of insulin granules in living beta-cells, showing that such particles have two main dynamics that coexist inside the cells. CONCLUSIONS: Visual4DTracker is a software package for MATLAB to visualize, handle and manually track [Formula: see text] stacks of microscopy images containing objects such cells, granules, etc.. With its unique set of functions, it remarkably permits the user to analyze and proof-read 4D data in a friendly 3D fashion. The tool is freely available at https://drive.google.com/drive/folders/19AEn0TqP-2B8Z10kOavEAopTUxsKUV73?usp=sharing.

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.

Breast cancer is the most common tumour in women and it is characterized by a huge variety of clinical and histological scenarios and imaging pattern. The axillary lymph node metastases presence or absence is one of the most important prognostic factors affecting the loco-regional recurrence and the overall survival. The lymph node status is usually determined by an histological exam, an invasive procedure that could result in complications. This work aims to provide a safer and non-invasive prognostic approach by introducing a radiomics-based method that predicts axillary lymph node metastasis. It combines primary tumor histological features and patients clinical data with quantitative measures extracted from the MR images. To compute these latter quantities we determine the convex hull of the ROIs and we introduce the Three Orthogonal Planes-Local Binary Pattern (TOP-LBP). On 99 samples the approach achieves a promising AUC equal to 85.6%.

The COVID-19 disease caused by the coronavirus SARS-nCoV2 is currently a global public health threat and Italy is one of the countries mostly suffering from this epidemic. It is therefore important to analyze epidemic data, considering also that the government deployed laws limiting the societal activities. We model COVID-19 dynamics with a SIQR (susceptible - infectious - quarantined - recovered) model, where we take into account the temporal variability of its parameters. Particle Swarm Optimization is used to find out the best parameters in the case of Italy and of Italian regions where the epidemic has the greatest impact. The basic reproductive number is estimated by a novel approach that averages out different PSO fits computed considering different temporal time-windows and reducing possible noise in the data. The results on data collected from February 24 to April 24 show that our approach is able to fit the data with low errors and that the basic reproductive number is characterized by a descending trend in time from 3.5 to a value below 1.

In many real world medical datasets learning under class imbalance can limit the performance of most supervised algorithms, resulting in a low recognition rate for samples belonging to the minority class. Within the wide literature on this issue the class of techniques that goes under the name of ensemble methods have been less investigated, despite their effectiveness. In this panorama, we present here an original strategy to construct the training set of each base classifier in the ensemble: it exploits information in the feature space that can give rise to unreliable classifications, which are determined by a novel algorithm here introduced. Our proposal is compared against multiple standard ensemble approaches on 18 publicly available biological datasets, showing promising results.

Class imbalance limits the performance of most learning algorithms, resulting in a low recognition rate for samples belonging to the minority class. Although there are different strategies to address this problem, methods that generate ensemble of classifiers have proven to be effective in several applications. This paper presents a new strategy to construct the training set of each classifier in the ensemble by exploiting information in the feature space that can give rise to unreliable classifications, which are determined by a novel algorithm here introduced. The performance of our proposal is compared against multiple standard ensemble approaches on 25 publicly available datasets, showing promising results.

Prostate cancer is the most common form of cancer in Western countries and there is the need to develop clinical decision support systems able to support physicians in the diagnosis of clinical relevant prostate cancer and avoid useless invasive prostate biopsies. In this respect, this paper introduces a radiomic approach that classifies the prostate cancer aggressiveness by combining Three Orthogonal Planes-Local Binary Pattern (TOP - LBP) with other texture measures. Furthermore, to combat the skewed nature of class priors, our proposal employs a data augmentation technique. The results achieved on 99 samples are up-and-coming, they favorably compare against conventional PI-RADS-based approach, and they show also the benefit given by the introduction of TOP-LBP in the radiomic signature.

The possibility of planning a therapy minimizing side effects and optimizing efficacy of cancer treatments is one of the main challenges tackled by precision medicine research in oncology. In this context, radiomics is revealing itself as a promising path for better understanding the correct approach to personalized cures. Its primary aim is to go beyond basic medical images analysis, which only leverages on direct measurements on the tumor mass, i.e. dimension and shape. On the contrary, radiomics approach is oriented to the extraction of heterogeneous and quantitative data from the images to characterize the disease from a wider perspective, in order to provide the physician a valid support for the therapy decision and survival prediction. This manuscript presents an application of radiomics to Non-Small Cell Lung Cancer, dealing with the novel task of predicting the possibility to carry out an adaptive therapy. We achieved promising performance, reporting a radiomics signature for predicting tumor reduction during therapy.

The diffusion of cheap sensing and programmable hardware systems enables today the quick design of complex smart sensor devices, i.e. sensing systems equipped with computational and communication functionalities, ready to be included in IoT systems. In this paper we present a general software architecture for the implementation of such smart sensors. The goal of the proposed model is to provide a reference framework usable in multiple scenarios, suitable for the management of different sensors, and providing a general and standard interface and simple computational functionalities. The paper presents our model, examples of different smart sensors based on our model and the implementation, test and performance assessment of a simple analogue smart sensor. The implementation of the proposed model is available online as a template for the design and development of smart sensors equipped with updatable edge computing functionalities. The performance evaluation of our analogue smart sensor shows that the application of our model has limited overhead which is highly compensated by its flexibility.

Investigation of membrane fluidity by two photon fluorescence microscopy opens up a new and important area of translational research, being a useful and sensitive method for disease monitoring and treatment. In this paper we investigate if biomembranes in human red blood cells (RBC) and peripheral mononuclear cells (PMC) could be used as markers for type 1 diabetes mellitus (T1DM) diagnosis, leading to the development of a method for monitoring T1DM progression that nowadays is lacking, as clinical exams cannot pursue this task with enough reliability. To this aim, we present a set of features computed from PMC and RBC images that are given to a multi-experts system leveraging on multi-spectral information for positive/negative classifications. The experiments are carried out on a dataset of 800 blood cell images belonging to 18 subjects adopting the leave-one-person-out approach.

Indirect immunofluorescence (IIF) is the recommended technique to detect rheumatic diseases through the analysis of images exhibiting a fluorescence in the green band. Since the request of such tests has recently increased, researches efforts have been directed towards the development of computer-aided diagnosis (CAD) tools supporting the specialists and improving the standardization of the method. Technological advances have made available color cameras for IIF image acquisition, but their use requires to determine which color to greyscale conversion method provides most useful information for the needs of CAD development. In this respect, we experimentally compare four different methods converting a color image into a greyscale one, analyzing wide features sets for each conversion method and applying four classification paradigms. Experiments have been carried out on an annotated dataset of HEp-2 cells, finding out a subset of features which is independent from the color model used and showing that a greyscale representation based on the HSI model better exploits information for IIF images analysis.

Detection of antibodies via indirect immunofluorescence (IIF) is a common marker in patients with suspected connective tissue diseases. IIF readings are affected by several issues limiting their reproducibility and reliability: hence, the development of a computer-aided diagnosis (CAD) tool supporting IIF diagnostic procedure would be beneficial in many respects. Although some works in the literature use greyscale cooled cameras for IIF image acquisition, recent research as well as commercial CAD solution use colour cameras. Indeed, colour cameras are cheaper than greyscale cameras and have adequate performance for the needs of IIF image acquisition. However, their application asks for studying how to extract useful information from colour images for CAD development. This paper presents an experimental comparison between four different methods converting a colour image into a greyscale one. This analysis has been carried out testing different popular classification paradigms on an annotated IIF image dataset, and also performing pre-clinical tests. Results show that a conversion method based on information derived from RGB primary components outperforms the others relying on different colour models.