Endocrinology

Parathyroid gland identification and angiography classification using simple machine learning methods.

BACKGROUND: Near-infrared indocyanine green angiography allows experienced surgeons to reliably evaluate parathyroid gland vitality during thyroid and parathyroid operations in order to predict postoperative function. To facilitate equal performance between surgeons, we developed an automatic computational quantification method using computer vision that portrays expert interpretation of visualize...

Machine Learning-Assisted Analysis of Sublingual Microcirculatory Dysfunction for Early Cardiovascular Risk Evaluation and Cardiovascular-Kidney-Metabolic Syndrome Stage in Patients With Type 2 Diabetes Mellitus.

AIMS: To examine whether sublingual microcirculation can be used as an effective and noninvasive method for assessing cardiovascular, kidney, and metabolic risks in patients with type 2 diabetes mellitus (T2DM).

Development and Multicenter, Multiprotocol Validation of Neural Network for Aberrant Right Subclavian Artery Detection.

PURPOSE: This study aimed to develop and validate a convolutional neural network (CNN) that automatically detects an aberrant right subclavian artery ...

Prediction of Poor Glycemic Control in Children with Type 1 Diabetes.

This study developed and validated a machine learning model for predicting glycemic control in children with type 1 diabetes at the time of diagnosis,...

Balancing Acts: Tackling Data Imbalance in Machine Learning for Predicting Myocardial Infarction in Type 2 Diabetes.

Type 2 Diabetes (T2D) is a prevalent lifelong health condition. It is predicted that over 500 million adults will be diagnosed with T2D by 2040. T2D c...

Enhancing Outpatient Wound Care: Applying AI to Optimize Treatment of Patients with Diabetic Foot Syndrome - The EPWUF-KI Project.

Diabetes mellitus (DM) is a significant public health issue in Germany, affecting 8 million individuals, with projections suggesting a substantial inc...

From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis

Recent advances in SSL enabled novel medical AI models, known as foundation models, offer great potential for better characterizing health from dive...

Artificial intelligence for diabetes care: current and future prospects.

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for...

Automated Extraction of Patient-Centered Outcomes After Breast Cancer Treatment: An Open-Source Large Language Model-Based Toolkit.

PURPOSE: Patient-centered outcomes (PCOs) are pivotal in cancer treatment, as they directly reflect patients' quality of life. Although multiple studi...

Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.

OBJECTIVE: If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the rec...

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to for...

[Preoperative Evaluation of Cervical Lymph Node Metastasis in Patients With Hashimoto's Thyroiditis Combined With Thyroid Papillary Carcinoma Using Machine Learning and Radiomics-Based Features: A Preliminary Study].

OBJECTIVE: To analyze the radiomic and clinical features extracted from 2D ultrasound images of thyroid tumors in patients with Hashimoto's thyroiditi...

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors a...

Machine Learning Identifies Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Diabetes Mellitus.

CONTEXT: The presence of metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a...

DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders.

Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimati...

Development and validation of a machine learning-based model to predict isolated post-challenge hyperglycemia in middle-aged and elder adults: Analysis from a multicentric study.

INTRODUCTION: Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes pat...

Through the Looking Glass Darkly: How May AI Models Influence Future Underwriting?

Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditions continue to evolve. Instances provided in this ...

Identifying Prediabetes in Canadian Populations Using Machine Learning.

Prediabetes is a critical health condition characterized by elevated blood glucose levels that fall below the threshold for Type 2 diabetes (T2D) diag...

Predicting Diabetes in Canadian Adults Using Machine Learning.

Rising diabetes rates have led to increased healthcare costs and health complications. An estimated half of diabetes cases remain undiagnosed. Early a...

Advancements in Continuous Glucose Monitoring: Integrating Deep Learning and ECG Signal.

This paper presents a novel approach to noninvasive hyperglycemia monitoring utilizing electrocardiograms (ECG) from an extensive database comprising ...