Endocrinology

Geno-GCN: A Genome-specific Graph Convolutional Network for Diabetes Prediction.

Drawing inspiration from convolutional neural networks, graph convolutional networks (GCNs) have been implemented in various applications. Yet, the integration of GCNs into clinical settings, particularly in the context of complex health conditions like diabetes, remains distant. In this paper, we introduce a genome-specific graph convolutional network (Geno-GCN) with a multi-graph aggregator to p...

Noninvasive detection of diabetes in obstructive sleep apnea based on overnight SpO signal and deep learning.

The prevalence of obstructive sleep apnea comorbid with diabetes is high while the awareness of diabetes is low. There is a strong need for new diagnostic biomarkers to detect diabetes at an early stage. Therefore, we aimed to establish an automatic, deep-learning based model that could be applied to assess diabetes risks using overnight SpO signals. The samples were derived from the Sleep Heart H...

Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning.

In the U.S., over a third of adults are pre-diabetic, with 80% unaware of their status. This underlines the need for better glucose monitoring to prev...

Exploring Biomarker Relationships in Both Type 1 and Type 2 Diabetes Mellitus Through a Bayesian Network Analysis Approach

Understanding the complex relationships of biomarkers in diabetes is pivotal for advancing treatment strategies, a pressing need in diabetes researc...

Attention Networks for Personalized Mealtime Insulin Dosing in People with Type 1 Diabetes

Calculating mealtime insulin doses poses a significant challenge for individuals with Type 1 Diabetes (T1D). Doses should perfectly compensate for e...

From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.

CONTEXT: Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. ...

Development and Validation of Fully Automatic Deep Learning-Based Algorithms for Immunohistochemistry Reporting of Invasive Breast Ductal Carcinoma

Immunohistochemistry (IHC) analysis is a well-accepted and widely used method for molecular subtyping, a procedure for prognosis and targeted therap...

Quantification of follicles in human ovarian tissue using image processing software and trained artificial intelligence†.

Cancer survival rates in prepubertal girls and young women have risen in recent decades due to increasingly efficient treatments. However, many such t...

Detection of Acetone as a Gas Biomarker for Diabetes Based on Gas Sensor Technology

With the continuous development and improvement of medical services, there is a growing demand for improving diabetes diagnosis. Exhaled breath anal...

Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm.

Automated insulin delivery (AID) is now integral to the clinical practice of type 1 diabetes (T1D). The objective of this pilot-feasibility study was...

Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm.

BACKGROUND: Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threaten...

The machine learning-based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis.

BACKGROUND: For medullary thyroid carcinoma (MTC) with no positive findings in the lateral neck before surgery, whether prophylactic lateral neck diss...

Comparing the accuracy of four machine learning models in predicting type 2 diabetes onset within the Chinese population: a retrospective study.

OBJECTIVE: To evaluate the effectiveness of machine learning (ML) models in predicting 5-year type 2 diabetes mellitus (T2DM) risk within the Chinese ...

Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology.

Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whol...

[Personalized glycemic management for patients with diabetic ketoacidosis based on machine learning].

OBJECTIVE: To explore the optimal blood glucose-lowering strategies for patients with diabetic ketoacidosis (DKA) to enhance personalized treatment ef...

A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data.

Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard a...

Artificial Intelligence in Endocrinology: On Track Toward Great Opportunities.

In endocrinology, the types and quantity of digital data are increasing rapidly. Computing capabilities are also developing at an incredible rate, as ...

The Risks and Challenges of Artificial Intelligence in Endocrinology.

Artificial intelligence (AI) holds the promise of addressing many of the numerous challenges healthcare faces, which include a growing burden of illne...

Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

PURPOSE: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This st...

Bibliometric analysis of research trends and hotspots on robot-assisted thyroid surgery.

BACKGROUND: Thyroid surgery involves the partial or complete removal of the thyroid gland and is a frequently performed surgical procedure. The adopti...