AIMC Topic: Feces
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Impact of Blastocystis carriage and colonization intensity on gut microbiota composition in a non-westernized rural population from Colombia.
PLoS neglected tropical diseases
May 1, 2025
BACKGROUND: The role of Blastocystis, a common intestinal parasitic protist of humans and other animals, in human health and disease remains elusive. Recent studies suggest a connection between Blastocystis colonization, healthier lifestyles, and hig...
Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis.
World journal of gastroenterology
Apr 28, 2025
BACKGROUND: Hepatic fibrosis (HF) represents a pivotal stage in the progression and potential reversal of cirrhosis, underscoring the importance of early identification and therapeutic intervention to modulate disease trajectory.
Artificial Intelligence Models Could Enhance the Diagnostic Accuracy (DA) of Fecal Immunochemical Test (FIT) in the Detection of Colorectal Adenoma in a Screening Setting.
Anticancer research
Jan 1, 2025
BACKGROUND/AIM: This study evaluated the diagnostic accuracy (DA) for colorectal adenomas (CRA), screened by fecal immunochemical test (FIT), using five artificial intelligence (AI) models: logistic regression (LR), support vector machine (SVM), neur...
Evaluation of alarm notification of artificial intelligence in automated analyzer detection of parasites.
Medicine
Sep 27, 2024
To evaluate the alarm notification of artificial intelligence in detecting parasites on the KU-F40 Fully Automatic Feces Analyzer and provide a reference for clinical diagnosis in parasite diseases. A total of 1030 fecal specimens from patients in ou...
A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.
La Clinica terapeutica
Jan 1, 2024
BACKGROUND: The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has present...
Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.
Cell reports
Apr 13, 2021
Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-fre...
Benchmarking
Microbiota
Allergens
Epigenome
Datasets as Topic
Gene Expression Regulation
Atlases as Topic
Nasal Cavity
Feces
Proteomics
Cluster Analysis
Unsupervised Machine Learning
Humans
Case-Control Studies
Precision Medicine
Adolescent
Female
Child, Preschool
Adult
Child
Asthma
Male
Middle Aged
Gene Expression Profiling
Transcriptome
Developing a Neural Network Model for a Non-invasive Prediction of Histologic Activity in Inflammatory Bowel Diseases.
The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology
Mar 1, 2021
BACKGROUND: Colonoscopy with biopsy is the "gold" standard for evaluating disease activity in inflammatory bowel diseases (IBD). Current research is geared toward finding non-invasive, cost-efficient methods that estimate disease activity. We aimed t...
Machine Learning Supports Automated Digital Image Scoring of Stool Consistency in Diapers.
Journal of pediatric gastroenterology and nutrition
Feb 1, 2021
BACKGROUND/AIMS: Accurate stool consistency classification of non-toilet-trained children remains challenging. This study evaluated the feasibility of automated classification of stool consistencies from diaper photos using machine learning (ML).
Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease Linked.
The Journal of clinical endocrinology and metabolism
Sep 1, 2020
AIMS: The purpose of this work is to find the gut microbial fingerprinting of pediatric patients with type 1 diabetes.
Predicting culturable enterococci exceedances at Escambron Beach, San Juan, Puerto Rico using satellite remote sensing and artificial neural networks.
Journal of water and health
Feb 1, 2019
Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteri...
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