AIMC Topic: Machine Learning

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Non-invasive multi-phase CT artificial intelligence for predicting pre-treatment enlarged lymph node status in colorectal cancer: a prospective validation study.

European radiology
OBJECTIVES: Benign lymph node enlargement can mislead surgeons into overstaging colorectal cancer (CRC), causing unnecessarily extended lymphadenectomy. This study aimed to develop and validate a machine learning (ML) classifier utilizing multi-phase...

Fecal gut microbiota and amino acids as noninvasive diagnostic biomarkers of Pediatric inflammatory bowel disease.

Gut microbes
BACKGROUND AND AIMS: Fecal calprotectin (FCP) has limited specificity as diagnostic biomarker of pediatric inflammatory bowel disease (IBD), leading to unnecessary invasive endoscopies. This study aimed to develop and validate a fecal microbiota and ...

Identification of macrophage-associated diagnostic biomarkers and molecular subtypes in gestational diabetes mellitus based on machine learning.

Artificial cells, nanomedicine, and biotechnology
Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages play a crucial role in its development. This study integrated scRNA-seq and RNA-seq data to explore m...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Computational biology and chemistry
Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked differences in treatment strategies and prognoses for gliomas of different grades and molecular types. However, current glioma molecular subtyping systems ...

Improved analytical workflow towards machine learning supported N-glycomics-based biomarker discovery.

Talanta
The composition and function of glycans are very complex thus manual data interpretation of their structural elucidation is difficult. Capillary electrophoresis is one of the liquid phase separation techniques, which is most frequently used to addres...

Using AI to triage patients without clinically significant prostate cancer using biparametric MRI and PSA.

Abdominal radiology (New York)
OBJECTIVES: To train and evaluate the performance of a machine learning triaging tool that identifies MRI negative for clinically significant prostate cancer and to compare this against non-MRI models.

Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.

The veterinary quarterly
Study advances current diagnostic efficiency of canine/feline (sub-)cutaneous tumors using machine learning and multimodal imaging data. White light (WL), fluorescence (FL) and ultrasound (US) imaging were combined into hybrid approaches to different...

Mapping trait justice sensitivity in the Brain: Whole-brain resting-state functional connectivity as a predictor of other-oriented not self-oriented justice sensitivity.

Cognitive, affective & behavioral neuroscience
Justice sensitivity (JS) reflects personal concern and commitment to the principle of justice, showing considerable heterogeneity among the general population. Despite a growing interest in the behavioral characteristics of JS over the past decades, ...