98%
921
2 minutes
20
With the development of artificial intelligence(AI) in computer science and statistics, it has been further applied to the medical field. These applications include the management of infectious diseases, in which machine learning has created inroads in clinical microbiology, radiology, genomics, and the analysis of electronic health record data. Especially, the role of machine learning in microbiology has gradually become prominent, and it is used in etiological diagnosis, prediction of antibiotic resistance, association between human microbiome characteristics and complex host diseases, prognosis judgment, and prevention and control of infectious diseases. Machine learning in the field of microbiology mainly adopts supervised learning and unsupervised learning, involving algorithms from classification and regression to clustering and dimensionality reduction. This Review explains crucial concepts in machine learning for unfamiliar readers, describes machine learning's current applications in clinical microbiology and infectious diseases, and summarizes important approaches clinicians must be aware of when evaluating research using machine learning.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078339 | PMC |
http://dx.doi.org/10.3389/fcimb.2025.1545646 | DOI Listing |
J Clin Invest
September 2025
The University of Texas at Austin, Austin, United States of America.
Background: Following SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which debilitating symptoms persist for at least three months. Elucidating biologic underpinnings of LC could identify therapeutic opportunities.
Methods: We utilized machine learning methods on biologic analytes provided over 12-months after hospital discharge from >500 COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor", trained on patient-reported physical function survey scores.
Proc Natl Acad Sci U S A
September 2025
Max Planck Institute for Solar System Research, Göttingen 37077, Germany.
Turbulent convection governs heat transport in both natural and industrial settings, yet optimizing it under extreme conditions remains a significant challenge. Traditional control strategies, such as predefined temperature modulation, struggle to achieve substantial enhancement. Here, we introduce a deep reinforcement learning (DRL) framework that autonomously discovers optimal control policies to maximize heat transfer in turbulent Rayleigh-Bénard convection.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
September 2025
Division of Plastic and Reconstructive Surgery, Neonatal and Pediatric Craniofacial Airway Orthodontics, Department of Surgery, Stanford University School of Medicine, 770 Welch Road, Palo Alto, CA, 94394, USA.
Background: Alveolar molding plate treatment (AMPT) plays a critical role in preparing neonates with cleft lip and palate (CLP) for the first reconstruction surgery (cleft lip repair). However, determining the number of adjustments to AMPT in near-normalizing cleft deformity prior to surgery is a challenging task, often affecting the treatment duration. This study explores the use of machine learning in predicting treatment duration based on three-dimensional (3D) assessments of the pre-treatment maxillary cleft deformity as part of individualized treatment planning.
View Article and Find Full Text PDFHepatol Int
September 2025
Department of Biomedical Informatics and Data Science, Yale School of Medicine, PO Box 208009, New Haven, CT, 06520-8009, USA.
Int J Cardiovasc Imaging
September 2025
Klinikum Fürth, Friedrich-Alexander-University Erlangen- Nürnberg, Fürth, Germany.
Myocarditis is an inflammation of heart tissue. Cardiovascular magnetic resonance imaging (CMR) has emerged as an important non-invasive imaging tool for diagnosing myocarditis, however, interpretation remains a challenge for novice physicians. Advancements in machine learning (ML) models have further improved diagnostic accuracy, demonstrating good performance.
View Article and Find Full Text PDF