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The evaluation of Acne using ordinal scales reflects the clinical perception of severity but has shown low reproducibility both intra- and inter-rater. In this study, we investigated if Artificial Intelligence trained on images of Acne patients could perform acne grading with high accuracy and reliabilities superior to those of expert physicians.

International audience; Object recognition is essential to enable robots to interact with their environment. Robots should be capable, on one hand of recognizing previously experienced objects, and on the other, of using the experienced objects for learning novel objects, i.e. objects for which training data are not available. Recognition of such novel objects can be achieved with Zero-Shot Learning (ZSL). In this work, we show the potential of ZSL for haptic recognition. First, we design a zero-shot haptic recognition algorithm and, using the extensive PHAC-2 database [1] as well as our own, we adapt, analyze and optimize the ZSL for the challenges and constraints characteristic of haptic recognition. Finally, we apply the optimized algorithm for haptic recognition of daily-life objects using an anthropomorphic robot hand. Our algorithm enables the robot to recognize eight of the ten novel objects handed to it.

Progressive functional decline in the epilepsies is largely unexplained. We formed the ENIGMA-Epilepsy consortium to understand factors that influence brain measures in epilepsy, pooling data from 24 research centres in 14 countries across Europe, North and South America, Asia, and Australia. Structural brain measures were extracted from MRI brain scans across 2149 individuals with epilepsy, divided into four epilepsy subgroups including idiopathic generalized epilepsies (n =367), mesial temporal lobe epilepsies with hippocampal sclerosis (MTLE; left, n = 415; right, n = 339), and all other epilepsies in aggregate (n = 1026), and compared to 1727 matched healthy controls. We ranked brain structures in order of greatest differences between patients and controls, by meta-Analysing effect sizes across 16 subcortical and 68 cortical brain regions. We also tested effects of duration of disease, age at onset, and age-by-diagnosis interactions on structural measures. We observed widespread patterns of altered subcortical volume and reduced cortical grey matter thickness. Compared to controls, all epilepsy groups showed lower volume in the right thalamus (Cohen's d = â '0.24 to â '0.73; P < 1.49 × 10 â '4), and lower thickness in the precentral gyri bilaterally (d = â '0.34 to â '0.52; P < 4.31 × 10 â '6). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = â '1.73 to â '1.91, P < 1.4 × 10 â '19), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = â '0.36 to â '0.52; P < 1.49 × 10 â '4). Thickness differences of the ipsilateral temporopolar, parahippocampal, entorhinal, and fusiform gyri, contralateral pars triangularis, and bilateral precuneus, superior frontal and caudal middle frontal gyri were observed in left, but not right, MTLE (d = â '0.29 to â '0.54; P < 1.49 × 10 â '4). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyrus, were observed in right, but not left, MTLE (d = â '0.27 to â '0.51; P < 1.49 × 10 â '4). Lower subcortical volume and cortical thickness associated with a longer duration of epilepsy in the all-epilepsies, all-other-epilepsies, and right MTLE groups (beta, b < â '0.0018; P < 1.49 × 10 â '4). In the largest neuroimaging study of epilepsy to date, we provide information on the common epilepsies that could not be realistically acquired in any other way. Our study provides a robust ranking of brain measures that can be further targeted for study in genetic and neuropathological studies. This worldwide initiative identifies patterns of shared grey matter reduction across epilepsy syndromes, and distinctive abnormalities between epilepsy syndromes, which inform our understanding of epilepsy as a network disorder, and indicate that certain epilepsy syndromes involve more widespread structural compromise than previously assumed.

Giovanni Morone,1,2 Stefano Paolucci,1,2 Andrea Cherubini,3 Domenico De Angelis,1 Vincenzo Venturiero,1 Paola Coiro,1 Marco Iosa1,2 1Private Inpatient Unit, 2Clinical Laboratory of Experimental Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy; 3Department of Robotics, LIRMM UM-CNRS, Montpellier, France Abstract: In this review, we give a brief outline of robot-mediated gait training for stroke patients, as an important emerging field in rehabilitation. Technological innovations are allowing rehabilitation to move toward more integrated processes, with improved efficiency and less long-term impairments. In particular, robot-mediated neurorehabilitation is a rapidly advancing field, which uses robotic systems to define new methods for treating neurological injuries, especially stroke. The use of robots in gait training can enhance rehabilitation, but it needs to be used according to well-defined neuroscientific principles. The field of robot-mediated neurorehabilitation brings challenges to both bioengineering and clinical practice. This article reviews the state of the art (including commercially available systems) and perspectives of robotics in poststroke rehabilitation for walking recovery. A critical revision, including the problems at stake regarding robotic clinical use, is also presented. Keywords: exoskeleton, neurorehabilitation, robot-assisted walking training, wearable robot, activities of daily living, motor learning, plasticity

International audience; In this review, we give a brief outline of robot-mediated gait training for stroke patients, as an important field emerging in rehabilitation. Technological innovations are allowing rehabilitation to move towards more integrated processes, with improved efficiency and less long-term impairments. In particular, robot-mediated neurorehabilitation is a rapidly advancing field, which uses robotic systems, often coupled with virtual reality haptic interfaces and emerging theories in neuroscience, to define new methods for treating neurological injuries such as stroke, spinal cord injury, and traumatic brain injury. The use of robots in gait training can enhance rehabilitation, following neuroscientific principles that justify the use of the robot. The field of robot-mediated neurorehabilitation brings challenges to both bioengineering and clinical practice. This paper reviews the state of art (including commercially available systems) and perspectives of robotics in post-stroke rehabilitation for walking recovery. A critical revision, including the problems at stake regarding robotic clinical use will also be presented.