AIMC Topic: Machine Learning

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Dynamics of Th1/Th17 responses and antimicrobial pathways in leprosy skin lesions.

The Journal of clinical investigation
BACKGROUNDReversal reactions (RRs) in leprosy are acute immune episodes marked by inflammation and bacterial clearance, offering a model to study the dynamics of host responses to Mycobacterium leprae. These episodes are often severe and difficult to...

Functional Consequences of Tinnitus in Military Service Members.

American journal of audiology
PURPOSE: Numerous individuals in the United States are bothered enough by tinnitus that it affects normal daily activities, including sleep and concentration. There is a high prevalence of self-reported bothersome tinnitus in the U.S. military, and t...

Rapid and quantitative detection of Botryosphaeria dothidea by surface-enhanced Raman spectroscopy with size-controlled spherical metal nanoparticles combined with machine learning.

International journal of food microbiology
Botryosphaeria dothidea infection has become a major factor affecting the quality of postharvest fruits, so detection of B. dothidea infection is very important to control the spread of infection and ensure food safety. In this study, we built a moni...

Development of a Machine Learning Model Integrating Pathomics and Clinical Data to Predict Axillary Lymph Node Metastasis in Breast Cancer: A Two-Center Study.

Cancer reports (Hoboken, N.J.)
BACKGROUND: Accurately assessing the status of axillary lymph nodes (ALNs) is essential for devising optimal surgical plans and making informed treatment decisions in breast cancer (BC) patients.

Using Artificial Intelligence and Machine Learning to Promote Child Health Equity.

Pediatrics
Artificial intelligence (AI) and machine learning (ML), used injudiciously, have the potential to exacerbate health inequalities. Conversely, there is a potential to use ML to give insight into the impact of socioeconomic factors, which allows us to ...

Longitudinal evaluation of workflow optimization in radiotherapy: A 4-year retrospective study.

Journal of applied clinical medical physics
BACKGROUND: Efficient workflows are essential for timely, high-quality radiotherapy. In 2020, an internal audit identified key workflow bottlenecks, including long patient wait times, suboptimal treatment planning, and inadequate quality control. Acc...

[Artificial intelligence-enhanced ECG interpretation: a new era for electrocardiography?].

Giornale italiano di cardiologia (2006)
Artificial intelligence (AI) is redefining ECG interpretation, transforming it from a static diagnostic tool into a dynamic, predictive, and integrative instrument. Although widespread, traditional rule-based ECG analysis has limitations in accuracy ...

Complexity-based unsupervised machine learning for patient-specific VMAT quality assurance.

Medical physics
BACKGROUND: Patient-specific quality assurance (PSQA) is essential to guarantee the requested accuracy and safety of high-precision radiotherapy treatments. With the widespread adoption of modulated-intensity techniques, there is a growing need for i...

Machine Learning in Rugby Union: Predicting and Identifying Key Performance Indicators for Professional Rugby Union Players in Match Play Based Workload.

European journal of sport science
Rugby union is an intermittent high-intensity contact sport requiring the analysis of various training and match metrics. Time-motion analysis and video analysis have enhanced the understanding of the interplay between these two factors. However, lim...

A Novel Deep Siamese Convolution Network for Detecting Fentanyl Analogs From Mass Spectra.

Journal of mass spectrometry : JMS
Mortality rates have risen dramatically in recent years due to the misuse of fentanyl and its analogs. Due to the easy synthesis and rapid emergence of various fentanyl analogs, an accurate detection model is particularly desirable. The existing clas...