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Machine Learning-enhanced X-ray-based Radiomics in the Identification of Post-COVID Patients.
Arch Bronconeumol
December 2024
National Koranyi Institute of Pulmonology, Budapest, Hungary; Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary; Department of Thoracic Surgery, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria.
Automated assessment of endometrial receptivity for screening recurrent pregnancy loss risk using deep learning-enhanced ultrasound and clinical data.
Front Physiol
December 2024
Department of Obstetrics and Gynecology, Deyang People's Hospital, Deyang, Sichuan, China.
Background: Recurrent pregnancy loss (RPL) poses significant challenges in clinical management due to an unclear etiology in over half the cases. Traditional screening methods, including ultrasonographic evaluation of endometrial receptivity (ER), have been debated for their efficacy in identifying high-risk individuals. Despite the potential of artificial intelligence, notably deep learning (DL), to enhance medical imaging analysis, its application in ER assessment for RPL risk stratification remains underexplored.
View Article and Find Full Text PDFMachine learning-enhanced detection of chlorpyrifos using molecularly imprinted polymer-coated optical fibers.
Talanta
January 2025
Department of Physics, Institute of Science, Banaras Hindu University, Varanasi, 221005, India. Electronic address:
This paper explores the use of large core declad optical fibers coated with molecularly imprinted polymers for chlorpyrifos detection, a key marker of organophosphate pesticides. The performance of sensor is evaluated using artificial neural networks and principal component analysis. By varying the declad length, the performance of molecularly imprinted polymer-coated fibers is compared to uncoated fibers, and both are used to identify commercial and pure samples of chlorpyrifos pesticides.
View Article and Find Full Text PDFMachine Learning-Enhanced Bacteria Detection Using a Fluorescent Sensor Array with Functionalized Graphene Quantum Dots.
ACS Appl Mater Interfaces
January 2025
Hefei University of Technology, Hefei, 230009, China.
Pathogenic bacteria are the source of many serious health problems, such as foodborne diseases and hospital infections. Timely and accurate detection of these pathogens is of vital significance for disease prevention, control of epidemic spread, and protection of public health security. Rapid identification of pathogenic bacteria has become a research focus in recent years.
View Article and Find Full Text PDFDeep-learning-enhanced modeling of electrosprayed particle assembly on non-spherical droplet surfaces.
Soft Matter
January 2025
Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY 14260, USA.
Article Synopsis
- Researchers explored how charged colloidal particles assemble at liquid interfaces to enhance the manufacturing of thin film materials.
- The study combined computational simulations and machine learning, using a new algorithm to analyze particle behavior on curved droplet surfaces.
- By optimizing particle and substrate charge densities through a deep learning model, the team achieved a 96.4% similarity between simulated and experimental results, improving prediction accuracy while saving computational time.
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