Ovarian cancer represents 7% of all cancers in pregnant women. Characterising an ovarian mass during pregnancy is essential to avoid unnecessary treatment and, if treatment is required, to plan it accordingly. Although ultrasonography (US) is the first-line modality to characterise adnexal masses, MRI is indicated when adnexal masses are indeterminate at the US examination. An MRI risk stratification system has been proposed to assign a malignancy probability based on the adnexal lesion’s MRI, but features of the scoring system require the administration of intravenous gadolinium-based contrast agents, a method that might have a limited use in pregnant women. The non-contrast MRI score (NCMS) has been used and evaluated in non-pregnant women to characterise adnexal masses indeterminate at the US examination. Therefore, we evaluated the diagnostic accuracy of the NCMS in pregnant women, analysing 20 cases referred to our specialised institution. We also evaluated the diagnostic agreement between two radiologists with different expertise. The two readers classified ovarian masses as benign or malignant using both subjective assessment (SA), based on the interpretive evaluation of imaging findings derived from personal experience, and the NCMS, which includes five categories where 4 and 5 indicate a high probability of a malignant mass. The expert radiologist correctly classified 90% of the diagnoses, using both SA and the NCMS, relying on a sensitivity of 85.7% and a specificity of 92.3%, with a false positive rate of 7.7% and a false negative rate of 14.3%. The non-expert radiologist correctly identified patients at a lower rate, especially using the SA. The analysis of the inter-observer agreement showed a K = 0.47 (95% CI: 0.48–0.94) for the SA (agreement in 71.4% of cases) and a K = 0.8 (95% CI: 0.77–1.00) for the NCMS (agreement in 90% of cases). Although in pregnant patients, non-contrast MRI is used, our results support the use of a quantitative score, i.e., the NCMS, as an accurate tool. This procedure may help less experienced radiologists to reduce the rate of false negatives or positives, especially in centres not specialised in gynaecological imaging, making the MRI interpretation easier and more accurate for radiologists who are not experts in the field, either.

Brain–computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements—open/close, finger tapping, and wrist rotation—along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.

Multiple sclerosis (MS) is a complex, chronic, and heterogeneous disease of the central nervous system that affects 3 million people globally. The multifactorial nature of MS necessitates an adaptive and personalized approach to diagnosis, monitoring, and treatment. This paper proposes a novel Digital Twin for Multiple Sclerosis (DTMS) designed to integrate diverse data sources, including Magnetic resonance imaging (MRI), clinical biomarkers, and digital health metrics, into a unified predictive model. The DTMS aims to enhance the precision of MS management by providing real-time, individualized insights into disease progression and treatment efficacy. Through a federated learning approach, the DTMS leverages explainable AI to offer reliable and personalized therapeutic recommendations, ultimately striving to delay disability and improve patient outcomes. This comprehensive digital framework represents a significant advancement in the application of AI and digital twins in the field of neurology, promising a more tailored and effective management strategy for MS.

Digital twin technology in healthcare offers personalized care through advanced analytics, real-time data, and virtual models. This paper introduces the Double Digital Twin (DDT) of the hands using the Virtual Glove (VG), a supporting rehabilitation device capable of collecting and reconstructing models from both hands (impaired and healthy). A framework associated with DDT allows one to analyze and quantify the mobility of the impaired hand compared to the healthy one, to calculate numerically the discrepancies between the hands, and to provide a predictive analysis of the recovery times based on the reduction of discrepancies during repeated sessions of a given rehabilitation task. Very preliminary experiments are also presented.