Efficacy of biofeedback rehabilitation based on visual evoked potentials analysis in patients with advanced age-related macular degeneration

Age-related macular degeneration (AMD) is a progressive and degenerative disorder of the macula. In advanced stages, it is characterized by the formation of areas of geographic atrophy or fibrous scars in the central macula, which determines irreversible loss of central vision. These patients can benefit from visual rehabilitation programmes with acoustic "biofeedback" mechanisms that can instruct the patient to move fixation from the central degenerated macular area to an adjacent healthy area, with a reorganization of the primary visual cortex. In this prospective, comparative, non-randomized study we evaluated the efficacy of visual rehabilitation with an innovative acoustic biofeedback training system based on visual evoked potentials (VEP) real-time examination (Retimax Vision Trainer, CSO, Florence), in a series of patients with advanced AMD compared to a control group. Patients undergoing training were subjected to ten consecutive visual training sessions of 10min each, performed twice a week. Patients in the control group did not receive any training. VEP biofeedback rehabilitation seems to improve visual acuity, reading performances, contrast sensitivity, retinal fixation and sensitivity and quality of life in AMD patients

WATTSBurning: design and evaluation of an innovative eco-feedback system

Abstract. This paper reports a 15 weeks study of artistic eco-feedback deployed in six houses with an innovative sensing infrastructure and visualization strategy. The paper builds on previous work that showed a significant decrease in user awareness after a short period with a relapse in consumption. In this study we aimed to investigate if new forms of feedback could overcome this issue, maintaining the users awareness for longer periods of time. The study presented here aims at understanding if people are more aware of their energy consumption after the installation of a new, art inspired eco-feedback. The research question was then: does artistic eco-feedback provide an increased awareness over normal informative feedback? And does that awareness last longer? To answer this questions participants were interviewed and their consumption patterns analyzed. The main contribution of the paper is to advance our knowledge about the effectiveness of eco-feedback and provide guidelines for implementation of novel eco-feedback visualizations that overcome the relapse behavior pattern

Biofeedback Low Vision Rehabilitation with Retimax Vision Trainer in Patients with Advanced Age-related Macular Degeneration: A Pilot Study

Purpose: To evaluate the effectiveness of Visual Evoked Potential (VEP) biofeedback rehabilitation in selected low vision patients with advanced age-related macular degeneration (AMD). Design: Retrospective observational cohort study. Methods: Patients affected by advanced AMD, central macular atrophy with unstable fixation and best corrected visual acuity (BCVA) between 20/100 and 20/320 were considered. Selected patients underwent fundus photography and microperimetry with fixation analysis for the selected eye (highest BCVA). Ten consecutive training sessions of 10 min each were performed twice a week in the selected eye with Retimax Vision Trainer (CSO, Florence). BCVA, reading acuity and reading speed, contrast sensitivity, fixation, retinal sensitivity and quality of life questionnaire (VFQ-25) were evaluated at baseline and 7 days following the final session. Results: Significant improvements in terms of BCVA [p = .011], reading speed [p = .007], VFQ-25 score [p = .007], retinal sensitivity [p = .021] and fixation stability in the central 2° and 4° [p = .048; p = .037] post-treatment were observed for the 9 patients enrolled, with insignificant improvements observed in reading acuity and contrast sensitivity [p = .335; p = .291]. Conclusions: Preliminary results support VEP biofeedback rehabilitation improvements for visual function and quality of life in advanced AMD patients with low vision

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data