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1. Studies Conducted at Xi'an University of Science and Technology on Psychology and Psychiatry Recently Reported (Micro-expression Recognition Based On Multi-scale 3d Residual Convolutional Neural Network).

Subjects
CONVOLUTIONAL neural networks, FACIAL expression, PSYCHIATRY, TECHNOLOGICAL innovations
Abstract

A recent study conducted at Xi'an University of Science and Technology in Xi'an, People's Republic of China, focused on the accurate recognition of micro-expressions in demanding scenarios such as clinical psychotherapy and criminal interrogation. The study proposed a novel architecture based on a multi-scale 3D residual convolutional neural network, which effectively captured weak and fleeting facial features and improved recognition performance. The algorithm's performance was evaluated using public datasets, and it achieved recognition accuracies of 74.6%, 84.77%, and 91.35% on these datasets. The study concluded that the proposed algorithm could serve as an important reference for researchers working on high-precision micro-expression recognition. [Extracted from the article]

Published
2024

2. Predictive Algorithm for Surgery Recommendation in Thoracolumbar Burst Fractures Without Neurological Deficits.

Author

Dandurand, Charlotte, Fallah, Nader, Öner, Cumhur F., Bransford, Richard J., Schnake, Klaus, Vaccaro, Alexander R., Benneker, Lorin M., Vialle, Emiliano, Schroeder, Gregory D., Rajasekaran, Shanmuganathan, El-Skarkawi, Mohammad, Kanna, Rishi M., Aly, Mohamed, Holas, Martin, Canseco, Jose A, Muijs, Sander, Popescu, Eugen Cezar, Tee, Jin Wee, Camino-Willhuber, Gaston, and Joaquim, Andrei Fernandes

Subjects
CLINICAL prediction rules, DECISION trees, ARTIFICIAL intelligence, REGRESSION trees, ALGORITHMS, DECISION making
Abstract

Study design: Predictive algorithm via decision tree Objectives: Artificial intelligence (AI) remain an emerging field and have not previously been used to guide therapeutic decision making in thoracolumbar burst fractures. Building such models may reduce the variability in treatment recommendations. The goal of this study was to build a mathematical prediction rule based upon radiographic variables to guide treatment decisions. Methods: Twenty-two surgeons from the AO Knowledge Forum Trauma reviewed 183 cases from the Spine TL A3/A4 prospective study (classification, degree of certainty of posterior ligamentous complex (PLC) injury, use of M1 modifier, degree of comminution, treatment recommendation). Reviewers' regions were classified as Europe, North/South America and Asia. Classification and regression trees were used to create models that would predict the treatment recommendation based upon radiographic variables. We applied the decision tree model which accounts for the possibility of non-normal distributions of data. Cross-validation technique as used to validate the multivariable analyses. Results: The accuracy of the model was excellent at 82.4%. Variables included in the algorithm were certainty of PLC injury (%), degree of comminution (%), the use of M1 modifier and geographical regions. The algorithm showed that if a patient has a certainty of PLC injury over 57.5%, then there is a 97.0% chance of receiving surgery. If certainty of PLC injury was low and comminution was above 37.5%, a patient had 74.2% chance of receiving surgery in Europe and Asia vs 22.7% chance in North/South America. Throughout the algorithm, the use of the M1 modifier increased the probability of receiving surgery by 21.4% on average. Conclusion: This study presents a predictive analytic algorithm to guide decision-making in the treatment of thoracolumbar burst fractures without neurological deficits. PLC injury assessment over 57.5% was highly predictive of receiving surgery (97.0%). A high degree of comminution resulted in a higher chance of receiving surgery in Europe or Asia vs North/South America. Future studies could include clinical and other variables to enhance predictive ability or use machine learning for outcomes prediction in thoracolumbar burst fractures. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Findings from Guangzhou University Provide New Insights into Life Science (Location Optimization of Emergency Medical Facilities for Public Health Emergencies In Megacities Based On Genetic Algorithm).

Subjects
LIFE sciences, HEALTH facilities, GENETIC algorithms, MEDICAL emergencies, PUBLIC health
Abstract

A study conducted by researchers at Guangzhou University in China explores the principles and factors that influence the selection of emergency medical facilities for public health emergencies in megacities. The study proposes a logistic regression model and a site selection model for these facilities, using genetic algorithms (GAs) for simulation and analysis. The results show that the improved GA model outperforms traditional models, providing decision-makers with stable technical support and better action plans. The research findings offer a theoretical and decision-making basis for the location of government emergency medical facilities and guidance for enterprises constructing such facilities. [Extracted from the article]

Published
2024