Endocrinology

Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images.

OBJECTIVES: To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.

Predicting thyroid cancer recurrence using supervised CatBoost: A SHAP-based explainable AI approach.

Recurrence prediction in well-differentiated thyroid cancer remains a clinical challenge, necessitating more accurate and interpretable predictive models. This study investigates the use of a supervised CatBoost classifier to predict recurrence in well-differentiated thyroid cancer patients, comparing its performance against other ensemble models and employing Shapley Additive Explanations (SHAP) ...

Deep learning based motion correction in ultrasound microvessel imaging approach improves thyroid nodule classification.

To address inter-frame motion artifacts in ultrasound quantitative high-definition microvasculature imaging (qHDMI), we introduced a novel deep learni...

Can Copulas Be Used for Feature Selection? A Machine Learning Study on Diabetes Risk Prediction

Accurate diabetes risk prediction relies on identifying key features from complex health datasets, but conventional methods like mutual information ...

NeuroScale: evolutional scale-based protein language models enable prediction of neuropeptides.

BACKGROUND: Neuropeptides (NPs) are critical signaling molecules involved in various physiological and behavioral processes, including development, me...

Unsupervised Latent Pattern Analysis for Estimating Type 2 Diabetes Risk in Undiagnosed Populations

The global prevalence of diabetes, particularly type 2 diabetes mellitus (T2DM), is rapidly increasing, posing significant health and economic chall...

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach

The COVID-19 pandemic has significantly increased the incidence of post-infection cardiovascular events, particularly myocardial infarction, in indi...

Quantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models

Effective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improving pa...

Progress in the application of fludeoxyglucose positron emission tomography computed tomography in biliary tract cancer.

Biliary tract cancer (BTC) is a group of heterogeneous sporadic diseases, including intrahepatic, hilar, and distal cholangiocarcinoma, as well as gal...

Machine learning based predictive model of the risk of Tourette syndrome with SHAP value interpretation: a retrospective observational study.

Tourette syndrome is a relatively prevalent neurological condition, particularly among children, characterized by sudden, involuntary, repetitive move...

Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning.

Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who is most at risk remains challenging due to the no...

Continually Self-Improving Language Models for Bariatric Surgery Question--Answering

While bariatric and metabolic surgery (MBS) is considered the gold standard treatment for severe and morbid obesity, its therapeutic efficacy hinges...

A machine learning-based risk prediction model for diabetic oral ulceration.

BACKGROUND: Diabetic oral ulceration (DOU) is a prevalent and debilitating complication among diabetic patients, significantly impairing their quality...

Whole-body CT-to-PET synthesis using a customized transformer-enhanced GAN.

. Positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F-FDG PET-CT) is a multi-modality m...

Predicting Neo-Adjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images

Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human e...

Molecular Insights into the Binding of Organophosphate Flame Retardant Key Metabolites with Mineralocorticoid and Estrogen Receptors.

Flame retardants (FR) encompass a wide range of chemicals designed to inhibit and reduce the spread of fire by forming protective layers on materials....

A circadian behavioral analysis suite for real-time classification of daily rhythms in complex behaviors.

Long-term analysis of animal behavior has been limited by reliance on real-time sensors or manual scoring. Existing machine learning tools can automat...

Unveiling lipoprotein subfractions signature in high-FNPO PCOS: implications for PCOM diagnosis and risk assessment using advanced machine learning models.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common reproductive and metabolic disorder in the reproductive-age women. The international evidence...

Integrating bioinformatics and machine learning to unravel shared mechanisms and biomarkers in chronic obstructive pulmonary disease and type 2 diabetes.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) are on the rise. While there is evidence of a link betwee...

Sociodemographic Profile of People with Diagnosed Pancreatic Cancer in the UK: Retrospective Sentinel Network Cohort Study.

Pancreatic cancer is a devasting disease which is an increasing cause of cancer mortality. The aim of this study was to characterise, using descriptiv...