Endocrinology

Interpretable AI-driven multi-objective risk prediction in heart failure patients with thyroid dysfunction.

INTRODUCTION: Heart Failure (HF) complicated by thyroid dysfunction presents a complex clinical challenge, demanding more advanced risk stratification tools. In this study, we propose an AI-driven machine learning (ML) approach to predict mortality and hospitalization risk in HF patients with coexisting thyroid disorders.

Development of an artificial intelligence-based convolutional neural network for sellar barrier classification using magnetic resonance imaging.

BACKGROUND: This study aims to develop an artificial intelligence (AI) model using convolutional neural networks and transfer learning to classify sellar barriers as strong, mixed, or weak based on magnetic resonance imaging (MRI). Accurate classification is essential for surgical planning in endoscopic endonasal approaches for pituitary adenomas, as variations in the sellar barrier can lead to co...

Sex-Specific Ensemble Models for Type 2 Diabetes Classification in the Mexican Population.

BACKGROUND: Type 2 diabetes (T2D) is considered a global pandemic by the World Health Organization (WHO), with a growing prevalence, particularly in M...

Machine learning with decision curve analysis evaluates nutritional metabolic biomarkers for cardiovascular-kidney-metabolic risk: an NHANES analysis.

BACKGROUND: The American Heart Association recently introduced the concept of Cardiovascular-Kidney-Metabolic Syndrome (CKM), emphasizing the interpla...

The value of a combined model based on ultra-radiomics and multi-modal ultrasound in the benign-malignant differentiation of C-TIRADS 4A thyroid nodules: a prospective multicenter study.

OBJECTIVE: To establish a combined model based on ultrasound radiomics combined with multimodal ultrasound and evaluate its value in diagnosing benign...

The diagnostic value of convolutional neural networks in thyroid cancer detection using ultrasound images.

OBJECTIVE: To extract and analyze the image features of two-dimensional ultrasound images and elastic images of four thyroid nodules by radiomics, and...

Exploring the effect of the triglyceride-glucose index on bone metabolism in prepubertal children, a retrospective study: insights from traditional methods and machine-learning-based bone remodeling prediction.

BACKGROUND: Childhood obesity poses a significant risk to bone health, but the impact of insulin resistance (IR) on bone metabolism in prepubertal chi...

Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.

OBJECTIVE: To develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM...

Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification.

OBJECTIVE: Accurate evaluation of thyroid nodules is crucial for effective management; however, methods such as ultrasonography and Fine Needle Aspira...

Machine learning-based coronary heart disease diagnosis model for type 2 diabetes patients.

BACKGROUND: To establish a classification model for assisting the diagnosis of type 2 diabetes mellitus (T2DM) complicated with coronary heart disease...

SMART (artificial intelligence enabled) DROP (diabetic retinopathy outcomes and pathways): Study protocol for diabetic retinopathy management.

INTRODUCTION: Delayed diagnosis of diabetic retinopathy (DR) remains a significant challenge, often leading to preventable blindness and visual impair...

Using Machine Learning and Artificial Intelligence to Predict Diabetes Mellitus among Women Population.

BACKGROUND: Diabetes Mellitus is a chronic health condition (long-lasting) due to inadequate control of blood levels of glucose. This study presents a...

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.

BACKGROUND: The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to ...

Continuous glucose monitoring using machine learning models and IoT device data: A meta-analysis.

BACKGROUND: Machine learning offers diverse options for effectively managing blood glucose levels in diabetes patients. Selecting the right ML algorit...

SwinDFU-Net: Deep learning transformer network for infection identification in diabetic foot ulcer.

BACKGROUND: The identification of infection in diabetic foot ulcers (DFUs) is challenging due to variability within classes, visual similarity between...

Measuring Cell Dimensions in Fission Yeast Using Machine Learning.

In fission yeast (Schizosaccharomyces pombe), cell length is a crucial indicator of cell cycle progression. Microscopy screens that examine the effect...

Using machine learning to predict patients with polycystic ovary disease in Chinese women.

OBJECTIVE: With an estimated global frequency ranging from5 % to 21 %, polycystic ovary syndrome (PCOS) is one of the most prevalent hormonal disorder...

Brain tumor classification using MRI images and deep learning techniques.

Brain tumors pose a significant medical challenge, necessitating early detection and precise classification for effective treatment. This study aims t...

Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging

The AUTO-PCOS Classification Challenge seeks to advance the diagnostic capabilities of artificial intelligence (AI) in identifying Polycystic Ovary ...

Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain...