Inflammatory Bowel Disease

Agentic Automation of BT-RADS Scoring: End-to-End Multi-Agent System for Standardized Brain Tumor Follow-up Assessment

The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response assessment in patients with diffuse gliomas but requires complex integration of imaging trends, medication effects, and radiation timing. This study evaluates an end-to-end multi-agent large language model (LLM) and convolutional neural network (CNN) system for automated BT-RADS classification. A multi-age...

Spectral Rectification for Parameter-Efficient Adaptation of Foundation Models in Colonoscopy Depth Estimation

Accurate monocular depth estimation is critical in colonoscopy for lesion localization and navigation. Foundation models trained on natural images fail to generalize directly to colonoscopy. We identify the core issue not as a semantic gap, but as a statistical shift in the frequency domain: colonoscopy images lack the strong high-frequency edge and texture gradients that these models rely on for ...

Predicting cognitive-behavioral therapy outcomes in obsessive-compulsive disorder from inhibitory control neural activity: A mega-analysis and machine learning study from the ENIGMA-OCD consortium

Objective Cognitive behavioral therapy (CBT) is an effective first-line treatment for obsessive-compulsive disorder (OCD), yet it remains difficult to...

Association of Radiologic PPFE Change with Mortality in Lung Cancer Screening Cohorts

Background: Pleuroparenchymal fibroelastosis (PPFE) is an upper lobe predominant fibrotic lung abnormality associated with increased mortality in esta...

Deep Learning-based Differentiation of Drug-induced Liver Injury and Autoimmune Hepatitis: A Pathological and Computational Approach

Drug-induced liver injury (DILI) is an acute inflammatory liver disease caused not only by prescription and over-the-counter medications but also by h...

BEGA-UNet: Boundary-Explicit Guided Attention U-Net with Multi-Scale Feature Aggregation for Colonoscopic Polyp Segmentation

Accurate polyp segmentation from colonoscopy images is critical for colorectal cancer prevention, yet the generalization of deep learning models under...

A multi-center analysis of deep learning methods for video polyp detection and segmentation

Colonic polyps are well-recognized precursors to colorectal cancer (CRC), typically detected during colonoscopy. However, the variability in appearanc...

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...

Eubiota: Modular Agentic AI for Autonomous Discovery in the Gut Microbiome

The gut microbiome regulates many aspects of human biology, including immunity and inflammatory diseases, yet mechanistic discovery and translation re...

CMSA-Net: Causal Multi-scale Aggregation with Adaptive Multi-source Reference for Video Polyp Segmentation

Video polyp segmentation (VPS) is an important task in computer-aided colonoscopy, as it helps doctors accurately locate and track polyps during exami...

ColoDiff: Integrating Dynamic Consistency With Content Awareness for Colonoscopy Video Generation

Colonoscopy video generation delivers dynamic, information-rich data critical for diagnosing intestinal diseases, particularly in data-scarce scenario...

Structure-to-Image: Zero-Shot Depth Estimation in Colonoscopy via High-Fidelity Sim-to-Real Adaptation

Monocular depth estimation (MDE) for colonoscopy is hampered by the domain gap between simulated and real-world images. Existing image-to-image transl...

Multi-Modal Monocular Endoscopic Depth and Pose Estimation with Edge-Guided Self-Supervision

Monocular depth and pose estimation play an important role in the development of colonoscopy-assisted navigation, as they enable improved screening by...

Metagenomics AI powered prediction of Inflammatory Bowel Disease and Probiotic Recommendation

Background and Objective The dysbiosis of human gut microbiome has been increasingly seen to have a relation in the development of autoimmune diseases...

A ML-framework for the discovery of next-generation IBD targets using a harmonized single-cell atlas of patient tissue

Target discovery for IBD has traditionally relied on genetic associations, which lack the cellular resolution needed to identify novel, actionable, ce...

A Causal Machine Learning Framework for Treatment Personalization in Clinical Trials: Application to Ulcerative Colitis

Randomized controlled trials estimate average treatment effects, but treatment response heterogeneity motivates personalized approaches. A critical qu...

RealSynCol: a high-fidelity synthetic colon dataset for 3D reconstruction applications

Deep learning has the potential to improve colonoscopy by enabling 3D reconstruction of the colon, providing a comprehensive view of mucosal surfaces ...

Long-term Cardiac Autonomic Effects of Prenatal Steroid Exposure: A Machine Learning Approach Integrating Heart Rate Variability and ECG Foundation Models

Background: Prenatal glucocorticoid administration is standard care for threatened preterm birth, but long-term cardiac autonomic effects remain incom...

Selective Glucocorticoid Receptor Modulators of Immune Checkpoint Function

Glucocorticoids (GCs) coordinate immunity, inflammation and metabolism through allosteric regulation of the glucocorticoid receptor (GR) transcription...

Enabling Real-Time Colonoscopic Polyp Segmentation on Commodity CPUs via Ultra-Lightweight Architecture

Early detection of colorectal cancer hinges on real-time, accurate polyp identification and resection. Yet current high-precision segmentation models ...