Multisensor Data Fusion for Human Activities Classification and Fall Detection

Significant research exists on the use of wearable sensors in the context of assisted living for activities recognition and fall detection, whereas radar sensors have been studied only recently in this domain. This paper approaches the performance limitation of using individual sensors, especially for classification of similar activities, by implementing information fusion of features extracted from experimental data collected by different sensors, namely a tri-axial accelerometer, a micro-Doppler radar, and a depth camera. Preliminary results confirm that combining information from heterogeneous sensors improves the overall performance of the system. The classification accuracy attained by means of this fusion approach improves by 11.2% compared to radar-only use, and by 16.9% compared to the accelerometer. Furthermore, adding features extracted from a RGB-D Kinect sensor, the overall classification accuracy increases up to 91.3%

Ultra-Early Treatment of Neurosurgical Emergencies with Endoscopic Endonasal Approach: Experience from Three Italian Referral Centers

Purpose: the aim of this multicenter study is to preliminarily assess the role of the Endoscopic Endonasal Approach (EEA) in ultra-early (i.e., within 12 h) management of selected neurosurgical emergencies in terms of clinical and radiological outcomes. Methods: 26 patients affected by sellar/parasellar pathologies with rapid progression of symptoms were managed with EEA within 12 h from diagnosis in three Italian tertiary referral Centers from January 2016 to December 2019. Both clinical and radiological data have been collected preoperatively as well as post-operatively in order to perform retrospective analysis. Results: The average time from admission to the operating room was 5.5 h (±2.3). The extent of resection was gross-total in 20 (76.9%), subtotal in 6 (23.1%) patients. One patient experienced re-bleeding after a subtotal removal of a hemorrhagic lesion. Patients with a longer time from admission (>4 h) to the operatory room (OR) experienced stable impairment of the visual acuity (p = 0.033) and visual field (p = 0.029) in the post-operative setting. Conclusions: The Endoscopic Endonasal Approach represents a safe, effective technique that can be efficiently used with good results in the management of selected neurosurgical emergencies in centers with adequate experience

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

BACKGROUND No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. METHODS Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. RESULTS Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). CONCLUSIONS Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models

3D-printed boluses for radiotherapy: influence of geometrical and printing parameters on dosimetric characterization and air gap evaluation

The work investigates the implementation of personalized radiotherapy boluses by means of additive manufacturing technologies. Boluses materials that are currently used need an excessive amount of human intervention which leads to reduced repeatability in terms of dosimetry. Additive manufacturing can solve this problem by eliminating the human factor in the process of fabrication. Planar boluses with fixed geometry and personalized boluses printed starting from a computed tomography scan of a radiotherapy phantom were produced. First, a dosimetric characterization study on planar bolus designs to quantify the effects of print parameters such as infill density and geometry on the radiation beam was made. Secondly, a volumetric quantification of air gap between the bolus and the skin of the patient as well as dosimetric analyses were performed. The optimization process according to the obtained dosimetric and airgap results allowed us to find a combination of parameters to have the 3D-printed bolus performing similarly to that in conventional use. These preliminary results confirm those in the relevant literature, with 3D-printed boluses showing a dosimetric performance similar to conventional boluses with the additional advantage of being perfectly conformed to the patient geometry

Head and neck radiotherapy amid the COVID‑19 pandemic: practice recommendations of the Italian Association of Radiotherapy and Clinical Oncology (AIRO)

Abstract Management of patients with head and neck cancers (HNCs) is challenging for the Radiation Oncologist, especially in the COVID-19 era. The Italian Society of Radiotherapy and Clinical Oncology (AIRO) identified the need of practice recommendations on logistic issues, treatment delivery and healthcare personnel’s protection in a time of limited resources. A panel of 15 national experts on HNCs completed a modified Delphi process. A five-point Likert scale was used; the chosen cut-offs for strong agreement and agreement were 75% and 66%, respectively. Items were organized into two sections: (1) general recommendations (10 items) and (2) special recommendations (45 items), detailing a set of procedures to be applied to all specific phases of the Radiation Oncology workflow. The distribution of facilities across the country was as follows: 47% Northern, 33% Central and 20% Southern regions. There was agreement or strong agreement across the majority (93%) of proposed items including treatment strategies, use of personal protection devices, set-up modifications and follow-up re-scheduling. Guaranteeing treatment delivery for HNC patients is well-recognized in Radiation Oncology. Our recommendations provide a flexible tool for management both in the pandemic and post-pandemic phase of the COVID-19 outbreak

Quality assurance for automatically generated contours with additional deep learning

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model’s use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours. Methods: The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions. Results: Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time. Conclusion: We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior

Young Neurosurgeons and Technology: Survey of Young Neurosurgeons Section of Italian Society of Neurosurgery (Società Italiana di Neurochirurgia, SINch)

Background: Technological advancement in neurosurgery is a continuous process aimed at improving existing devices and implementing innovative ones. Recently, artificial intelligence (AI)-derived technologies (i.e., machine learning and virtual or augmented reality) have been entering this field, promising to significantly change its future. The acquisition of technological skills should be a goal of training for young neurosurgeons. The aim of this study is the analysis of competence and attitude toward intraoperative devices of young neurosurgeons. Methods: An online electronic survey was sent to 256 members of the Young Neurosurgeons Section of the Italian Society of Neurosurgery (Società Italiana di Neurochirurgia, SINch), inquiring about their competences and attitude toward surgical technologies and AI-derived devices. Results: A total of 152 neurosurgeons participated in the survey. Most participants reported sufficient skills in autonomously setting up and using the optic neuronavigator (93.4% and 92.1%, respectively), advanced microscope (80.3% and 76.3%), magnetic neuronavigator (75% and 72.4%), ultrasonography (63.2% and 60.5%) and high-definition endoscope (55.3% and 46%). Most (92.1%) considered operative devices useful and 89.5% reported a high motivation to acquire technological skills. AI-derived devices have already been used by 56.6% of participants but only 31.6% received proper dedicated training. Conclusions: Italian young neurosurgeons have acquired technical skills sufficient for the autonomous use of the most common operative devices, reporting a positive attitude toward technology with high motivation to learn and awareness of their potential harmfulness. A promising number of participants had already used AI-derived technologies, although only a few had received focused training for these devices

Real time step length estimation on smartphone

Smartphones are particularly suitable for health related applications during daily living, given their diffusion into society and computational capabilities. We proposed a smartphone application for real-time step length estimation, using inverted pendulum model. We tested the proposed solution on 5 healthy subjects, comparing the smartphone estimation with a stereophotogrammetric system. © 2016 IEEE

Adjuvant radiotherapy in grossly total resected grade II atypical meningiomas: a protective effect on recurrence?

Introduction: Management of grade II atypical meningiomas (AM) remains controversial. Conflicting evidence exist on the possible protective effect of adjuvant radiotherapy (ART) on recurrence in grossly resected AMs. The aim of this meta-analysis is to evaluate the role of ART in grossly resected (Simpson grades 1-3) AMs on the recurrence and survival. Evidence acquisition: Literature review was performed by the study investigators who handily queried the MEDLINE database using keywords and MeSH terms in different combinations using the Boolean operators "AND" or "OR," and database-related filters to maximize the chance to identify articles focusing on role of radiotherapy in atypical (WHO grade II) meningiomas. Data were retrieved from comparative studies of AMs undergone surgical resection alone vs. surgery + ART. Only grossly total resected AMs (Simpson grades 1-3) were included. The individual and pooled odds ratio (OR) for the crude recurrence, progression free survival (PFS) at 1, 3 and 5-years, as well as for the overall survival (OS) at 5-years were calculated by using the Mantel-Haenszel model in surgery alone vs. surgery + ART. Evidence synthesis: Eleven studies were considered eligible. 8 were included for the outcome "crude recurrence;" 6 for PFS at 1-3 years, 7 for PFS at 5-years; 6 for the OS at 5-years. Results suggest that surgery + ART might have a protective role on recurrence in gross-totally resected AMs (OR:1.66). Specifically, surgery + ART slightly improved PFS at 1-year (OR:0.92) and more consistently at 3- and 5-years (OR:0.31 and 0.35 respectively) hence favoring a combined approach. Conclusions: Current literature on the impact of ART after gross total resection of AM are still heterogeneous and not systematically reported. The present meta-analysis suggests a possible protective role of postoperative RT against long-term recurrence as compared to surgical resection alone

The prevalence of imposter syndrome among young neurosurgeons and residents in neurosurgery: a multicentric study

Objective: Imposter syndrome (IS) occurs when high-achieving individuals have a pervasive sense of self-doubt combined with fear of being exposed as a fraud, despite objective measures of success. This is one of the main causes of burnout among professionals, threatening their mental health and general well-being. The prevalence and severity of IS among neurosurgery residents and young neurosurgeons has not been yet studied. The primary outcomes of this study were the prevalence and severity of IS. Methods: An anonymous cross-sectional survey including both a demographic questionnaire (Clance Imposter Phenomenon Survey) and compensatory mechanisms was distributed to young neurosurgeons and residents in neurosurgery in Italy. Results: A total of 103 responses were collected. The prevalence rate was 81.6%. Among the respondents with IS, 42.7% showed moderate signs, 27.2% frequent, and only 11.7% had an intense symptomatology. Level of education, female sex, and academic achievements were all identified as predictive factors of IS. Conclusions: A total of 81.6% of respondents reported potentially significant levels. The implications of IS on both the outcomes in patients and the well-being of neurosurgeons should be evaluated in future studies