AIMC Topic: Humans

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A retrospective cohort study using machine learning to predict coronary artery lesions in children with Kawasaki disease.

BMC pediatrics
BACKGROUND: Kawasaki disease (KD) mainly occurs in children under 5 years old, and the most common complication of KD is coronary artery lesion (CAL). In recent years, the incidence rate of KD has increased year by year worldwide, so it is particular...

A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning.

BMC medical imaging
This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas...

METS-VF as a novel predictor of gallstones in U.S. adults: a cross-sectional analysis (NHANES 2017-2020).

BMC gastroenterology
BACKGROUND AND AIMS: Obesity is a well-established risk factor for gallstone formation, but traditional anthropometric measures (e.g., BMI, waist circumference) inadequately assess metabolically active visceral adiposity. The novel Metabolic Score fo...

Can AI match emergency physicians in managing common emergency cases? A comparative performance evaluation.

BMC emergency medicine
BACKGROUND: Large language models (LLMs) such as ChatGPT are increasingly explored for clinical decision support. However, their performance in high-stakes emergency scenarios remains underexamined. This study aimed to evaluate ChatGPT's diagnostic a...

A successive framework for brain tumor interpretation using Yolo variants.

Scientific reports
Accurate identification and segmentation of brain tumors in Magnetic Resonance Imaging (MRI) images are critical for timely diagnosis and treatment. MRI is frequently used to diagnose these disorders; however medical professionals find it challenging...

Dual prompt personalized federated learning in foundation models.

Scientific reports
Personalized federated learning (PFL) has garnered significant attention for its ability to address heterogeneous client data distributions while preserving data privacy. However, when local client data is limited, deep learning models often suffer f...

Classification accuracy of pain intensity induced by leg blood flow restriction during walking using machine learning based on electroencephalography.

Scientific reports
Pain assessment in clinical practice largely relies on patient-reported subjectivity. Although previous studies using fMRI and EEG have attempted objective pain evaluation, their focus has been limited to resting conditions. This study aimed to class...

A qualitative study on ethical issues related to the use of AI-driven technologies in foreign language learning.

Scientific reports
The current situation in the use of AI-driven technologies in education has seen an unprecedented rise, however, the impact of these technologies from the perspective of ethical issues is largely unknown. The aim of the research is to provide a clear...

Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics.

Scientific reports
This work introduces a hybrid computational approach in which degree-based topological descriptors are harnessed with the aid of advanced regression models and artificial neural networks (ANNs) to predict the crucial physicochemical properties of 17 ...

Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD.

Scientific reports
Steatotic liver disease (SLD), formerly named fatty liver disease, has a prevalence estimated at 30-38% in adults. Detection of SLD is important, since prompt initiation of treatment can stop disease progression, lead to a reduction in adverse outcom...