Giulia Iaconi

Liver disease is a major global health concern. Given the critical role of medical image categorization in fibrosis staging (low, moderate, severe, cirrhotic) and the challenges posed by limited medical image datasets, this paper aims to leverage ultrasound imaging to assess liver margin characteristics at the level of Glisson’s capsule—here referred to as Glisson’s line—to develop a simple, automated model for accurately distinguishing fibrosis stages. The proposed approach combines traditional image processing techniques in a pre-processing stage with machine learning algorithms for classification. The pre-processing phase introduces an attention-focusing mechanism that stretches the gray levels of Glisson’s line while shrinking the intensity levels associated with the liver parenchyma and surrounding tissues. This results in the so-called region of contrast interest (ROCI), where potential classification distractors are minimized. For classification, a convolutional neural network (CNN)-based model is used to process original, rotated, and transformed ultrasound images. To address dataset imbalance and overfitting, a 10-fold cross-validation strategy was implemented. The results demonstrate that, by effectively enhancing the information content of Glisson’s line, different liver fibrosis stages can be accurately distinguished without the need for explicit edge detection, achieving accuracy levels comparable to those reported in the literature. The novelty of this work lies in analyzing the morphology of Glisson’s capsule—obtained through this method—rather than focusing on the liver parenchyma and texture, as is traditionally carried out.

Digital solutions in the field of restorative neurology offer significant assistance, enabling patients to engage in rehabilitation activities remotely. This research introduces ReMoVES, an Internet of Medical Things (IoMT) system delivering telemedicine services specifically tailored for multiple sclerosis rehabilitation, within the overarching framework of the STORMS project. The ReMoVES platform facilitates the provision of a rehabilitative exercise protocol, seamlessly integrated into the Individual Rehabilitation Project, curated by a multidimensional medical team operating remotely. This manuscript delves into the second phase of the STORMS pilot feasibility study, elucidating the technology employed, the outcomes achieved, and the practical, professional, and academic implications. The STORMS initiative, as the genesis of digital telerehabilitation solutions, aims to enhance the quality of life for multiple sclerosis patients.

Digital solutions in the field of restorative neurology offer significant assistance, enabling patients to engage in rehabilitation activities remotely. This research introduces ReMoVES, an Internet of Medical Things (IoMT) system delivering telemedicine services specifically tailored for Multiple Sclerosis rehabilitation, within the overarching framework of the STORMS project. The ReMoVES platform facilitates the provision of a rehabilitative exercise protocol, seamlessly integrated into the Individual Rehabilitation Project, curated by a multidimensional medical team operating remotely. This manuscript delves into the second phase of the study, elucidating the technology employed, the outcomes achieved, and the practical, professional, and academic implications. The STORMS initiative, as the genesis of digital telerehabilitation solutions, aims to enhance the quality of life for Multiple Sclerosis patients.

Digital medical solutions can be very helpful in restorative neurology, as they allow the patients to practice their rehabilitation activities remotely. This work discloses ReMoVES, an IoMT system providing telemedicine services, in the context of Multiple Sclerosis rehabilitation, within the frame of the project STORMS. A rehabilitative protocol of exercises can be provided as ReMoVES services and integrated into the Individual Rehabilitation Project as designed by a remote multidimensional medical team. In the present manuscript, the first phase of the study is described, including the definition of the needs to be addressed, the employed technology, the design and the development of the exergames, and the possible practical/professional and academic consequences. The STORMS project has been implemented with the aim to act as a starting point for the development of digital telerehabilitation solutions that support Multiple Sclerosis patients, improving their living conditions. This paper introduces a study protocol and it addresses pre-clinical research needs, where system issues can be studied and better understood how they might be addressed. It also includes tools to favor remote patient monitoring and to support the clinical staff.