AIMC Topic: Reproducibility of Results

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ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

BMC cardiovascular disorders
A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an increasing mortality rate in recent years. Regular monitoring is essential for effective management, as early detection and timely treatment greatly imp...

Patient education strategies in pediatric orthopaedics: using ChatGPT to answer frequently asked questions on scoliosis.

Spine deformity
PURPOSE: Patients increasingly rely on online resources to better understand their health conditions. ChatGPT could satisfy the demand for reliable and accessible online health education resources, yet few studies have applied this to pediatric ortho...

Predator crow search optimization with explainable AI for cardiac vascular disease classification.

Scientific reports
The proposed framework optimizes Explainable AI parameters, combining Predator crow search optimization to refine the predictive model's performance. To prevent overfitting and enhance feature selection, an information acquisition-based technique is ...

Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy.

Scientific reports
Monitoring and tracking of cell motion is a key component for understanding disease mechanisms and evaluating the effects of treatments. Time-lapse optical microscopy has been commonly employed for studying cell cycle phases. However, usual manual ce...

Evaluation of a deep learning segmentation tool to help detect spinal cord lesions from combined T2 and STIR acquisitions in people with multiple sclerosis.

European radiology
OBJECTIVE: To develop a deep learning (DL) model for the detection of spinal cord (SC) multiple sclerosis (MS) lesions from both sagittal T2 and short tau inversion recovery (STIR) sequences and to investigate whether such a model could improve the p...

Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques.

Oncotarget
Recent advances in deep learning models have transformed medical imaging analysis, particularly in radiology. This editorial outlines how uncertainty quantification through embedding-based approaches enhances diagnostic accuracy and reliability in he...

Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and experiments.

Frontiers in immunology
INTRODUCTION: Early diagnosis of Ewing sarcoma (ES) is critical for improving patient prognosis. However, the accurate diagnosis of ES remains challenging, underscoring the need for novel diagnostic biomarkers to enhance diagnostic precision and reli...

Comparing large Language models and human annotators in latent content analysis of sentiment, political leaning, emotional intensity and sarcasm.

Scientific reports
In the era of rapid digital communication, vast amounts of textual data are generated daily, demanding efficient methods for latent content analysis to extract meaningful insights. Large Language Models (LLMs) offer potential for automating this proc...

Evaluation of correctness and reliability of GPT, Bard, and Bing chatbots' responses in basic life support scenarios.

Scientific reports
Timely recognition and initiation of basic life support (BLS) before emergency medical services arrive significantly improve survival rates and neurological outcomes. In an era where health information-seeking behaviors have shifted toward online sou...