Latest AI and machine learning research in inflammatory bowel disease for healthcare professionals.
Endoscopic video analysis is essential for gastrointestinal diagnosis and computer-assisted interventions, but video sequences are routinely degraded by specular reflections, motion artifacts, and missing frames. These transient corruptions can distract clinicians, reduce image interpretability, and disrupt downstream tasks such as 3D reconstruction and navigation. Effective restoration therefore ...
Federated Learning (FL) in surgical video AI enables collaborative model training without sharing sensitive data. However, standard evaluation practices - selecting the "best" global model based only on validation data from participating hospitals - can lead to suboptimal deployment choices. We identify this critical failure mode as performance leakage, where the selected model overfits internal f...
Active feature acquisition (AFA) sequentially selects which features to observe to reach a classification or ranking decision. Its central limitation ...
Active feature acquisition (AFA) sequentially selects which features to observe to reach a classification or ranking decision. Its central limitation ...
Accurate vision-based navigation in monocular endoscopy is difficult due to limited depth cues, weak tissue texture, non-rigid deformation, and substa...
In clinical trials for ulcerative colitis (UC), pathologists assess disease severity through standardized histological indices, including the Geboes S...
Background: Colon capsule endoscopy (CCE) has been proposed as a non-invasive alternative to colonoscopy for colorectal cancer (CRC) screening, offeri...
Medical video diagnosis involves inferring clinical decisions from dynamic tissue responses throughout examination processes. Existing methods rely on...
Microbiome-based machine learning classifiers show increasing promise for disease identification across gastrointestinal, metabolic, and immune-mediat...
Surgical Video Question Answering (VideoQA) provides a promising paradigm for dynamic intraoperative interpretation, enabling real-time decision suppo...
Covalent chemistry has transformed small-molecule drug discovery, yet analogous strategies for proteins remain largely inaccessible because covalent w...
Learning robust representations of polyp tracklets is key to enabling multiple AI-assisted colonoscopy applications, from polyp characterization to au...
Objectives To describe the design, operational safeguards, and early use of ChatIBD, a specialty-specific generative AI platform for inflammatory bowe...
Abstract Objective: Structured extraction from clinical free-text depends on human annotators whose labels are susceptible to errors and knowledge-dri...
Biomedical knowledge graphs have emerged as foundational infrastructure for AI-driven drug discovery, yet their translational impact on novel target i...
Background: Emerging evidence suggests that the oral microbiome may contribute to aberrant gut immune responses in Inflammatory Bowel Disease (IBD). M...
Controllable medical video generation has achieved remarkable progress, but it still lacks interpretability, which requires the alignment of generated...
Background: Current medical large language model (LLM) evaluations largely rely on small collections of cases, whereas rigorous safety testing require...
Early identification and removal of polyps can reduce the risk of developing colorectal cancer. However, the diverse morphologies, complex backgrounds...
Computed tomography (CT) enterography is a primary imaging modality for assessing inflammatory bowel disease (IBD), yet the representational choices t...