Endocrinology

Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation

Medical ultrasound image segmentation faces significant challenges due to limited labeled data and characteristic imaging artifacts including speckle noise and low-contrast boundaries. While semi-supervised learning (SSL) approaches have emerged to address data scarcity, existing methods suffer from suboptimal unlabeled data utilization and lack robust feature representation mechanisms. In this pa...

Automated Diabetic Screening via Anterior Segment Ocular Imaging: A Deep Learning and Explainable AI Approach

Diabetic retinopathy screening traditionally relies on fundus photography, requiring specialized equipment and expertise often unavailable in primary care and resource limited settings. We developed and validated a deep learning (DL) system for automated diabetic classification using anterior segment ocular imaging a readily accessible alternative utilizing standard photography equipment. The syst...

Deep Learning Based Estimation of Blood Glucose Levels from Multidirectional Scleral Blood Vessel Imaging

Regular monitoring of glycemic status is essential for diabetes management, yet conventional blood-based testing can be burdensome for frequent assess...

Antenatal surveillance of placental function using a wearable near infrared spectroscopy device with machine learning data interpretation

Background Placental dysfunction remains a leading cause of stillbirth and neonatal morbidity, yet current monitoring tools provide only indirect and ...

Detecting and Subtyping Ketoacidosis from Metabolomic Patterns in Forensic Casework

Subtyping of ketoacidosis, a metabolic state characterized by blood acidification due to various causes, remains challenging in forensic casework. Pos...

Co-designing a virtual reality based mindfulness application to address diabetes distress using Artificial Intelligence-informed Experience-Based Co-Design (AI-EBCD): a feasibility study

More than one third of adults with diabetes can experience diabetes distress due to the demands of daily self-care. As a cognitive therapy, mindfulnes...

Reaction-Conditioned Enzyme Discovery with Multimodal Deep Learning

The precise mapping between chemical transformations and enzymatic catalysts underpins the complexity of metabolic networks. Conventional discovery me...

Multi-Kernel Gated Decoder Adapters for Robust Multi-Task Thyroid Ultrasound under Cross-Center Shift

Thyroid ultrasound (US) automation couples two competing requirements: global, geometry-driven reasoning for nodule delineation and local, texture-dri...

Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema

Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of preventable blindness among working-age adults. Traditional approache...

Sleep Disruption Improves Performance in Simple Olfactory and Visual Decision-Making Tasks

Sleep disruption drastically impacts cognitive functions including decision-making and attention across many different species. In this study, we leve...

Thyroid Cancer Risk Prediction from Multimodal Datasets Using Large Language Model

Thyroid carcinoma is one of the most prevalent endocrine malignancies worldwide, and accurate preoperative differentiation between benign and malignan...

dAMN: a genome scale neural-mechanistic hybrid model to predict bacterial growth dynamics

This study presents dAMN, a hybrid neural-mechanistic model that integrates neural networks with genome-scale dynamic flux balance analysis (dFBA) to ...

AI-Driven Generation of Cortisol-Binding Peptides for Non-Invasive Stress Detection

Cortisol is a primary biomarker of stress, released in sweat at concentrations that directly correlate with physiological stress levels. Detecting cor...

Enhancing Prediabetes Diagnosis from Continuous Glucose Monitoring Data via Iterative Label Cleaning and Deep Learning

As of early 2026, over 115 million US adults (more than 1 in 3) have prediabetes, a condition with an annual conversion rate of 5%-10% to type 2 diabe...

Uncertainty-aware Blood Glucose Prediction from Continuous Glucose Monitoring Data

In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 ...

Intelligent Guidance and Diagnostic Assistance for Handheld Ultrasound: Actor-Critic Based Approach for Carotid Artery and Thyroid Examination

Handheld ultrasound devices have revolutionized point-of-care diagnostics, but their effectiveness remains limited by operator dependency and the need...

Weakly Supervised Patch Annotation for Improved Screening of Diabetic Retinopathy

Diabetic Retinopathy (DR) requires timely screening to prevent irreversible vision loss. However, its early detection remains a significant challenge ...

Evaluating Few-Shot Meta-Learning using STUNT for Microbiome-Based Disease Classification

The human gut microbiome is increasingly explored as a diagnostic indicator for disease, yet machine learning models trained on metagenomic data are o...

Crop-OCT: a Fully Integrated Imageomics Pipeline to Identify Regional and Focal Retinopathy in Murine Models

Imageomics uses machine learning to accelerate our understanding of biological traits and human disease processes. Some of the earliest imageomics app...

Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction

While multimodal survival prediction models are increasingly more accurate, their complexity often reduces interpretability, limiting insight into how...