AIMC Topic: Feces
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Validation of Vetscan Imagyst, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.
Parasites & vectors
Nov 12, 2024
BACKGROUND: Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intell...
A lightweight deep-learning model for parasite egg detection in microscopy images.
Parasites & vectors
Nov 6, 2024
BACKGROUND: Intestinal parasitic infections are still a serious public health problem in developing countries, and the diagnosis of parasitic infections requires the first step of parasite/egg detection of samples. Automated detection can eliminate t...
Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.
Frontiers in cellular and infection microbiology
Nov 4, 2024
BACKGROUND: This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in d...
A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol.
PloS one
Oct 28, 2024
BACKGROUND: Manual screening of a Kato-Katz (KK) thick stool smear remains the current standard to monitor the impact of large-scale deworming programs against soil-transmitted helminths (STHs). To improve this diagnostic standard, we recently design...
Artificial intelligence-based digital pathology for the detection and quantification of soil-transmitted helminths eggs.
PLoS neglected tropical diseases
Sep 30, 2024
BACKGROUND: Conventional microscopy of Kato-Katz (KK1.0) thick smears, the primary method for diagnosing soil-transmitted helminth (STH) infections, has limited sensitivity and is error-prone. Artificial intelligence-based digital pathology (AI-DP) m...
A machine learning-based electronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples.
Clinica chimica acta; international journal of clinical chemistry
Sep 24, 2024
BACKGROUND: Neonatal sepsis is a global health threat, contributing to high morbidity and mortality rates among newborns. Recognizing the profound impact of neonatal sepsis on long-term health outcomes emphasizes the critical need for timely detectio...
Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes.
BMC gastroenterology
Sep 17, 2024
BACKGROUND: Colorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have show...
Diagnostic application of the ColonFlag AI tool in combination with faecal immunochemical test in patients on an urgent lower gastrointestinal cancer pathway.
BMJ open gastroenterology
Sep 16, 2024
OBJECTIVE: Colorectal cancer (CRC) is the fourth most common cancer in the UK. Patients with symptoms suggestive of CRC should be referred for urgent investigation. However, gastrointestinal symptoms are often non-specific and there is a need for sui...
The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications.
Analytical biochemistry
Aug 24, 2024
Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimen...
Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis.
The American journal of gastroenterology
Jul 25, 2024
INTRODUCTION: Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation.
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