Endocrinology

Machine Learning Predicts Patients With New-onset Diabetes at Risk of Pancreatic Cancer.

BACKGROUND: New-onset diabetes represent a high-risk cohort to screen for pancreatic cancer.

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.

Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It poses a significant public health concern, as individuals with MetS are at an increased risk of developing cardiovascular diseases and type 2 diabete...

Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this stu...

Optimizing postprandial glucose prediction through integration of diet and exercise: Leveraging transfer learning with imbalanced patient data.

BACKGROUND: In recent years, numerous methods have been introduced to predict glucose levels using machine-learning techniques on patients' daily beha...

Identification of Key Efferocytosis-Related Genes and Mechanisms in Diabetic Retinopathy.

This study aimed to explore the key efferocytosis-related genes in diabetic retinopathy (DR) and their regulatory mechanisms. Public DR-related gene e...

Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes.

Several studies published so far used highly selective image datasets from unclear sources to train computer vision models and that may lead to overes...

Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples.

The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological ...

Predicting recurrent gestational diabetes mellitus using artificial intelligence models: a retrospective cohort study.

BACKGROUND: We aimed to develop novel artificial intelligence (AI) models based on early pregnancy features to forecast the likelihood of recurrent ge...

Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy.

PURPOSE: This study aimed to determine the generalizability of an artificial intelligence (AI) algorithm trained on an ethnically diverse dataset to s...

Artificial intelligence chatbots for the nutrition management of diabetes and the metabolic syndrome.

BACKGROUND: Recently, there has been a growing interest in exploring AI-driven chatbots, such as ChatGPT, as a resource for disease management and edu...

Potential Shared Mitochondrial-Related Gene Signatures and Molecular Mechanisms Between Polycystic Ovary Syndrome (PCOS) and Major Depressive Disorder (MDD): Evidence from Transcriptome Data and Machine Learning.

Polycystic ovary syndrome (PCOS) is strongly associated with major depressive disorder (MDD), but the shared pathophysiological mechanisms between the...

CT-based artificial intelligence prediction model for ocular motility score of thyroid eye disease.

PURPOSE: Thyroid eye disease (TED) is the most common orbital disease in adults. Ocular motility restriction is the primary complaint of patients, whi...

Computational approaches for clinical, genomic and proteomic markers of response to glucagon-like peptide-1 therapy in type-2 diabetes mellitus: An exploratory analysis with machine learning algorithms.

INTRODUCTION: In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like ...

A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.

BACKGROUND: Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ sta...

DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation.

Genes, expressed as sequences of nucleotides, are susceptible to mutations, some of which can lead to cancer. Machine learning and deep learning metho...

Interpretable machine learning models for detecting peripheral neuropathy and lower extremity arterial disease in diabetics: an analysis of critical shared and unique risk factors.

BACKGROUND: Diabetic peripheral neuropathy (DPN) and lower extremity arterial disease (LEAD) are significant contributors to diabetic foot ulcers (DFU...

Integrated image-based deep learning and language models for primary diabetes care.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care phys...

Effect of dexamethasone pretreatment using deep learning on the surgical effect of patients with gastrointestinal tumors.

To explore the application efficacy and significance of deep learning in anesthesia management for gastrointestinal tumors (GITs) surgery, 80 elderly ...

A machine learning algorithm for stratification of risk of cardiovascular disease in metabolic dysfunction-associated steatotic liver disease.

BACKGROUND: Steatotic liver disease (SLD) is associated with adverse cardiac events. Metabolic dysfunction-associated steatotic liver disease (MASLD) ...

Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer.

Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation...