Endocrinology

Application of interpretable machine learning algorithms to predict macroangiopathy risk in Chinese patients with type 2 diabetes mellitus.

Macrovascular complications are leading causes of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM), yet early diagnosis of cardiovascular disease (CVD) in this population remains clinically challenging. This study aims to develop a machine learning model that can accurately predict diabetic macroangiopathy in Chinese patients. A retrospective cross-sectional analytical stud...

Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study.

BACKGROUND: Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. However, existing diagnostic criteria often lack the necessary sensitivity and specificity because of the complex etiology of the disorder. Hence, this study aims to employ machine learning techniques to develop an interpretable predictive mod...

Predicting Diabetes Using Machine Learning: A Comparative Study of Classifiers

Diabetes remains a significant health challenge globally, contributing to severe complications like kidney disease, vision loss, and heart issues. T...

The march to harmonized imaging standards for retinal imaging.

The adoption of standardized imaging protocols in retinal imaging is critical to overcoming challenges posed by fragmented data formats across devices...

Effectiveness of AI-driven interventions in glycemic control: A systematic review and meta-analysis of randomized controlled trials.

This systematic review aims to assess the effectiveness of AI-Driven Decision Support Systems in improving glycemic control, measured by Time in Range...

CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications

New methods of CGM data analysis are emerging that are valuable for interpreting CGM patterns and underlying metabolic physiology. These new methods...

A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data.

While continuous glucose monitoring (CGM) has revolutionized metabolic health management, widespread adoption remains limited by cost constraints and ...

Machine learning for the prediction of diabetes-related amputation: a systematic review and meta-analysis of diagnostic test accuracy.

Although machine learning is frequently used in medicine for predictive purposes, its accuracy in diabetes-related amputation (DRA) remains unclear. F...

Intra- and Peritumoral Radiomics Based on Ultrasound Images for Preoperative Differentiation of Follicular Thyroid Adenoma, Carcinoma, and Follicular Tumor With Uncertain Malignant Potential.

OBJECTIVE: Differentiating between follicular thyroid adenoma (FTA), carcinoma (FTC), and follicular tumor with uncertain malignant potential (FT-UMP)...

Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition

Diabetic macular edema (DME) significantly contributes to visual impairment in diabetic patients. Treatment responses to intravitreal therapies vary...

The iCARE-DM Model for Five-Year T2DM Risk Prediction in the Elderly Population from Chinese Routine Public Health Services - China, 2017-2024.

INTRODUCTION: Risk assessment for high-risk populations is critical for preventing Type 2 Diabetes Mellitus (T2DM). Although China's public health ser...

Targeting Metabolic Reprogramming in Bladder Cancer Immunotherapy: A Precision Medicine Approach.

Bladder cancer, as a highly heterogeneous malignant tumor of the urinary system, is significantly affected by tumor metabolic reprogramming in its res...

Development of a 5-Year Risk Prediction Model for Transition From Prediabetes to Diabetes Using Machine Learning: Retrospective Cohort Study.

BACKGROUND: Diabetes has emerged as a critical global public health crisis. Prediabetes, as the transitional phase with 5%-10% annual progression to d...

Noninvasive blood glucose monitoring using a dual band microwave sensor with machine learning.

The potential for continuous non-invasive blood glucose monitoring has attracted a lot of interest in the field of medical diagnostics. This paper pro...

Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning.

BACKGROUND: Stress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuations, which correlate with adve...

Endometrial tumorigenesis involves epigenetic plasticity demarcating non-coding somatic mutations and 3D-genome alterations.

BACKGROUND: The incidence and mortality of endometrial cancer (EC) is on the rise. Eighty-five percent of ECs depend on estrogen receptor alpha (ERα) ...

Dynamic AI Ultrasound-Assisted Diagnosis System to Reduce Unnecessary Fine Needle Aspiration of Thyroid Nodules.

OBJECTIVES: This study aims to compare the diagnostic efficiency of the American College of Radiology-Thyroid Imaging, Reporting, and Data System (ACR...

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights

This study presents a web-based interactive health risk prediction tool designed to assess diabetes risk using machine learning models. Built on the...

A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction

The comorbidities of hypertension impose a heavy burden on patients and society. Early identification is necessary to prompt intervention, but it re...

Molecular Association Assay Systems for Imaging Protein-Protein Interactions in Mammalian Cells.

Molecular imaging probes play a pivotal role in assaying molecular events in various physiological systems. In this study, we demonstrate a new genre ...