AIMC Topic: Retrospective Studies

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Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods.

Scientific reports
COVID-19 has posed a significant global health challenge, affecting individuals across all age groups. While extensive research has focused on adults, pediatric patients exhibit distinct clinical characteristics that necessitate specialized predictiv...

MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.

International journal of colorectal disease
PUROPOSE: Preoperative T-staging in rectal cancer is essential for treatment planning, yet conventional MRI shows limited accuracy (~ 60-78). Our study investigates whether radiomic analysis of high-resolution T2-weighted MRI can non-invasively impro...

Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study.

PloS one
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide, affecting over 30% of the global general population. Its progressive nature and association with other chronic diseases make...

Machine Learning-Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study.

JMIR medical informatics
BACKGROUND: The risk of developing atherosclerotic cardiovascular disease (ASCVD) varies among individuals and is related to a variety of lifestyle factors in addition to the presence of chronic diseases.

TRI-PLAN: A deep learning-based automated assessment framework for right heart assessment in transcatheter tricuspid valve replacement planning.

International journal of cardiology
BACKGROUND: Efficient and accurate preoperative assessment of the right-sided heart structural complex (RSHSc) is crucial for planning transcatheter tricuspid valve replacement (TTVR). However, current manual methods remain time-consuming and inconsi...

Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.

Scientific reports
Rare diseases, such as Mucopolysaccharidosis (MPS), present significant challenges to the healthcare system. Some of the most critical challenges are the delay and the lack of accurate disease diagnosis. Early diagnosis of MPS is crucial, as it has t...

A Machine Learning-Based Prognostication Model Enhances Prediction of Early Hepatic Encephalopathy in Patients With Noncancer-Related Cirrhosis: Multicenter Longitudinal Cohort Study in Taiwan.

JMIR medical informatics
BACKGROUND: Hepatic encephalopathy (HE) contributes significantly to mortality among patients with liver cirrhosis. Early prediction of HE is essential for clinical decision-making, yet remains challenging-particularly in noncancer-related cirrhosis ...

Bridging the predictive divide: A hybrid early warning system for scalable and real-time dengue surveillance in LMICs.

Acta tropica
The global resurgence of dengue presents an ongoing challenge for public health systems, particularly in low- and middle-income countries (LMICs) where conventional early warning systems (EWS) often suffer from reporting delays and under-detection. W...

Retrospective Benchmarking and Novel Shape-Pharmacophore Based Implementation of the MORLD Method for the Autonomous Optimization of 3-Aroyl-1,4-diarylpyrroles (ARDAP).

Journal of chemical information and modeling
The use of artificial intelligence (AI) is increasingly integral to the drug-discovery process, and among AI-driven methodologies, deep generative models stand out as one of the most promising approaches for hit identification and optimization. Here,...