Endocrinology

Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry.

Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D and tracked 3D ultrasound with an automatic thyroid segmentation based on a deep neural network regarding inter- and intraobserver variability, time, and accuracy. Volume reference was MRI. 28 h...

Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study.

OBJECTIVE: To develop and validate a real-world screening, guideline-based deep learning (DL) system for referable diabetic retinopathy (DR) detection.

An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.

A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis,...

Drug Metabolism: A Half-Century Plus of Progress, Continued Needs, and New Opportunities.

The systematic study of drug metabolism began in the 19th Century, but most of what we know now has been learned in the last 50 years. Drug metabolism...

Identifying endotypes of individuals after an attack of pancreatitis based on unsupervised machine learning of multiplex cytokine profiles.

After an attack of pancreatitis, individuals may develop metabolic sequelae (eg, new-onset diabetes) and/or pancreatic cancer. These new-onset morbidi...

Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation.

BACKGROUND: Diabetes is a public health problem worldwide. Although diabetes is a chronic and incurable disease, measures and treatments can be taken ...

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study.

Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes t...

Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features.

The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical d...

Genetics and Epigenetics in Personalized Nutrition: Evidence, Expectations, and Experiences.

With the presentation of the blueprint of the first human genome in 2001 and the advent of technologies for high-throughput genetic analysis, personal...

Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models.

AIMS: This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models.

Automated image curation in diabetic retinopathy screening using deep learning.

Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require...

Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models.

In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of aug...

Deep learning to diagnose Hashimoto's thyroiditis from sonographic images.

Hashimoto's thyroiditis (HT) is the main cause of hypothyroidism. We develop a deep learning model called HTNet for diagnosis of HT by training on 106...

MB-SupCon: Microbiome-based Predictive Models via Supervised Contrastive Learning.

Human microbiome consists of trillions of microorganisms. Microbiota can modulate the host physiology through molecule and metabolite interactions. In...

Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks.

At present, diabetes is one of the most important chronic noncommunicable diseases, that have threatened human health. By 2020, the number of diabetic...

Planning and Selection of Facility Layout in Healthcare Services.

Facility layout planning (FLP) is an integral part of the hospital layout design. The purpose of this article is to develop and elaborate a FLP method...

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy.

The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence ea...

Autonomous push button-controlled rapid insulin release from a piezoelectrically activated subcutaneous cell implant.

Traceless physical cues are desirable for remote control of the in situ production and real-time dosing of biopharmaceuticals in cell-based therapies....

Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes.

Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applie...

Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach.

There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and...