Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review.

Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.

Non-immersive 3D virtual stimulus alter the time production task performance and increase the EEG theta power in dorsolateral prefrontal cortex.

AIM: The study investigated the cortical activity changes and time production task performance induced by changes in motion speed of a non-immersive 3D virtual stimulus. MATERIAL AND METHODS: Twenty-one individuals were participated in the crossover study with the visual-time reproduction task under three-speed conditions: original, slow and fast virtual stimulus. In addition, the electroencephalographic analysis of the theta band power in the dorsolateral prefrontal cortex was done simultaneously with time production task execution. RESULTS: The results demonstrated that in the slow speed condition, there is an increase in the error in the time production task after virtual reality (p < 0.05). There is also increased EEG theta power in the right dorsolateral prefrontal cortex in all speed conditions (p < 0.05). CONCLUSIONS: We propose that the modulations of speed of virtual stimulus may underlie the accumulation of temporal pulses, which could be responsible for changes in the performance of the production task of the time intervals and a substantial increase in right dorsolateral prefrontal cortex activity related to attention and memory, acting in cognitive domains of supraseconds.

Unskilled shooters improve both accuracy and grouping shot having as reference skilled shooters cortical area: An EEG and tDCS study.

Transcranial direct current stimulation (tDCS) has been used as a non-invasive method for enhanced motor and cognitive abilities. However, no previous study has investigated if the tDCS application in unskilled shooters on cortical sites, selected based on the cortical activity of skilled shooters, improves the accuracy and shot grouping. Sixty participants were selected, which included 10 skilled shooters and 50 unskilled shooters. After we identified the right dorsolateral prefrontal cortex (DLPFC) as the area with the highest activity in skilled shooters, we applied anodal tDCS over the right DLPFC in the unskilled shooters under two conditions: sham-tDCS (placebo) and real-tDCS (anodal tDCS). We also analyzed electroencephalography. Our results indicated that anodal tDCS application enhanced the shot accuracy (p = 0.001). Furthermore, the beta power in the EEG recording was higher in the left DLPFC, left and right parietal cortex (p = 0,001) after applying anodal tDCS, while the low-gamma power was higher in the right DLPFC in sham-tDCS (p = 0.001) and right parietal cortex after anodal-tDCS (p = 0.001). Our findings indicate that anodal tDCS can improve accuracy and shot grouping when applied over the unskilled shooters' right DLPFC. Furthermore, beta and low-gamma bands are influenced by anodal tDCS over the right DLPFC, which may be predictive of skill improvement.