Tic Detection in Tourette Syndrome Patients Based on Unsupervised Visual Feature Learning

Tourette Syndrome: A Catalyst for Operationalizing Neurodiversity

Our goal, taking Tourette syndrome as a case example, is to introduce neurologists to, and motivate discussion on, the neurodiversity paradigm. This philosophical construct considers some neurologic conditions in diversity, instead of simply disease. Moving from philosophical idea to empirical construct draws from patient and family perspectives on (1) quality of life and discrimination, (2) disability pride, and (3) unique profiles of different patient cohorts. Listening to patient voices, attending to family, advocacy group, and societal views on neurologic disorders can strengthen precision neurology practice. Dialogs on neurodiversity, including antitherapy sentiments, offer to enhance neurologic care, patient agency, and autonomy; encourage respectful communications with patients who challenge the idea their condition is pathologic; and to set the stage for future empirical investigations and practice guidelines.

Video-based evaluation system for tic action in Tourette syndrome: modeling, detection, and evaluation

Behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in Tourette syndrome. Detecting tic symptoms plays an important role in patient treatment and evaluation; accurate tic identification is the key to clinical diagnosis and evaluation. In this study, we proposed a tic action detection method using face video feature recognition for tic and control groups. Through facial ROI extraction, a 3D convolutional neural network was used to learn video feature representations, and multi-instance learning anomaly detection strategy was integrated to construct the tic action analysis and discrimination framework. We applied this tic recognition framework in our video dataset. The model evaluation results achieved average tic detection accuracy of 91.02%, precision of 77.07% and recall of 78.78%. And the tic score curve with postprocessing provided information of how the patient's twitches change over time. The detection results at the individual level indicated that our method can effectively detect tic actions in videos of Tourette patients without the need for fine labeling, which is significant for the long-term evaluation of patients with Tourette syndrome.

Light-efficient channel attention in convolutional neural networks for tic recognition in the children with tic disorders

Tic is a combination of a series of static facial and limb movements over a certain period in some children. However, due to the scarcity of tic disorder (TD) datasets, the existing work on tic recognition using deep learning does not work well. It is that spatial complexity and time-domain variability directly affect the accuracy of tic recognition. How to extract effective visual information for temporal and spatial expression and classification of tic movement is the key of tic recognition. We designed the slow-fast and light-efficient channel attention network (SFLCA-Net) to identify tic action. The whole network adopted two fast and slow branch subnetworks, and light-efficient channel attention (LCA) module, which was designed to solve the problem of insufficient complementarity of spatial-temporal channel information. The SFLCA-Net is verified on our TD dataset and the experimental results demonstrate the effectiveness of our method.

Tourette syndrome research highlights from 2021

We summarize selected research reports from 2021 relevant to Tourette syndrome that the authors consider most important or interesting. The authors welcome article suggestions and thoughtful feedback from readers.

Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis

The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of "smart healthcare." This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.