Endocrinology

Latest AI and machine learning research in endocrinology for healthcare professionals.

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An ultrasonography of thyroid nodules dataset with pathological diagnosis annotation for deep learning.

Ultrasonography (US) of thyroid nodules is often time consuming and may be inconsistent between obse...

Artificial intelligence-based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus.

AIM/INTRODUCTION: We assess the efficacy of artificial intelligence (AI)-based, fully automated, vol...

Artificial Intelligence to Diagnose Complications of Diabetes.

Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artif...

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes.

Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, re...

Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.

This paper addresses the pressing issue of diabetes, which is a widespread condition affecting a hug...

Automatic pituitary adenoma segmentation and identification of cavernous sinus invasion via multitask learning.

AIM: This study aimed to develop a multitask deep learning model for pituitary macroadenoma (PMA) se...

Shortcomings in the Evaluation of Blood Glucose Forecasting.

OBJECTIVE: Recent years have seen an increase in machine learning (ML)-based blood glucose (BG) fore...

TIRADS-based artificial intelligence systems for ultrasound images of thyroid nodules: protocol for a systematic review.

PURPOSE: The thyroid imaging reporting and data system (TIRADS) was developed as a standard global t...

AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review.

BACKGROUND: Current blood glucose monitoring (BGM) methods are often invasive and require repetitive...

Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: Cervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastas...

Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm.

OBJECTIVES: To evaluate the value of radiomics based on ultrasonography in differentiating follicula...

Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.

Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal r...

The study on ultrasound image classification using a dual-branch model based on Resnet50 guided by U-net segmentation results.

In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonogr...

SDC4 protein action and related key genes in nonhealing diabetic foot ulcers based on bioinformatics analysis and machine learning.

Diabetic foot ulcers (DFU) is a complication associated with diabetes characterised by high morbidit...

Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device.

Detecting glucose levels is crucial for diabetes patients as it enables timely and effective managem...

Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers.

BACKGROUND: To utilize machine learning for identifying treatment response genes in diabetic foot ul...

A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.

BACKGROUND AND AIMS: To prevent end-stage renal disease caused by diabetic kidney disease, we create...

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