AIMC Topic: Algorithms

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Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE): An Artificial Intelligence Scoring System for a Spatial Assessment of Disease Severity in Ulcerative Colitis.

Journal of Crohn's & colitis
BACKGROUND AND AIMS: Validated scoring methods such as the Mayo Clinic Endoscopic Subscore (MCES) evaluate ulcerative colitis (UC) severity at the worst colon segment, without considering disease extent. We present the Ulcerative Colitis Severity Cla...

Artificial Intelligence-assisted Video Colonoscopy for Disease Monitoring of Ulcerative Colitis: A Prospective Study.

Journal of Crohn's & colitis
BACKGROUNDS AND AIMS: The Mayo endoscopic subscore [MES] is the most popular endoscopic disease activity measure of ulcerative colitis [UC]. Artificial intelligence [AI]-assisted colonoscopy is expected to reduce diagnostic variability among endoscop...

MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.

Journal of experimental botany
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we i...

Identification of CXCR4 inhibitory activity in natural compounds using cheminformatics-guided machine learning algorithms.

Integrative biology : quantitative biosciences from nano to macro
Neurodegenerative disorders are characterised by progressive damage to neurons that leads to cognitive impairment and motor dysfunction. Current treatment options focus only on symptom management and palliative care, without addressing their root cau...

Individualized multi-treatment response curves estimation using RBF-net with shared neurons.

Biometrics
Heterogeneous treatment effect estimation is an important problem in precision medicine. Specific interests lie in identifying the differential effect of different treatments based on some external covariates. We propose a novel non-parametric treatm...

EnrichDO: a global weighted model for Disease Ontology enrichment analysis.

GigaScience
BACKGROUND: Disease Ontology (DO) has been widely studied in biomedical research and clinical practice to describe the roles of genes. DO enrichment analysis is an effective means to discover associations between genes and diseases. Compared to hundr...

canSAR 2024-an update to the public drug discovery knowledgebase.

Nucleic acids research
canSAR (https://cansar.ai) continues to serve as the largest publicly available platform for cancer-focused drug discovery and translational research. It integrates multidisciplinary data from disparate and otherwise siloed public data sources as wel...

Automated Breast Density Assessment for Full-Field Digital Mammography and Digital Breast Tomosynthesis.

Cancer prevention research (Philadelphia, Pa.)
Mammographic density is a strong risk factor for breast cancer and is reported clinically as part of Breast Imaging Reporting and Data System (BI-RADS) results issued by radiologists. Automated assessment of density is needed that can be used for bot...

Regulating algorithmic care in the European Union: evolving doctor-patient models through the Artificial Intelligence Act (AI-Act) and the liability directives.

Medical law review
This article argues that the integration of artificial intelligence (AI) into healthcare, particularly under the European Union's Artificial Intelligence Act (AI-Act), poses significant implications for the doctor-patient relationship. While historic...