Latest AI and machine learning research in gastroenterology for healthcare professionals.
BackgroundPredicting whether a treatment will demonstrate meaningful clinical benefit before committing to a large-scale trial remains a major unmet need in oncology. Patient-derived organoids (PDOs) recapitulate individual tumor drug sensitivity, but have not been used to forecast population-level trial outcomes. We developed SCOPE (Screening-to-Clinical Outcome Prediction Engine), a platform tha...
Early identification and removal of polyps can reduce the risk of developing colorectal cancer. However, the diverse morphologies, complex backgrounds and often concealed nature of polyps make polyp segmentation in colonoscopy images highly challenging. Despite the promising performance of existing deep learning-based polyp segmentation methods, their perceptual capabilities remain biased toward l...
Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical varia...
Computed tomography (CT) enterography is a primary imaging modality for assessing inflammatory bowel disease (IBD), yet the representational choices t...
Background: Current deep learning models in computational pathology, radiology, and digital pathology produce opaque predictions that lack the explain...
Computer-aided detection (CADe) of early neoplasia in Barrett's esophagus is a low-prevalence surveillance problem in which clinically relevant findin...
Despite recent advancements in the field of medical image analysis with the use of pretrained foundation models, the issue of distribution shifts betw...
The interaction between T cell receptors (TCRs), peptides, and human leukocyte antigens (HLAs) underlies antigen-specific T cell immunity. Despite sub...
In this study, we proposed a deep Swin-Vision Transformer-based transfer learning architecture for robust multi-cancer histopathological image classif...
Cell state diversity drives tissue adaptability, repair, and disease resilience, but fully capturing this cellular complexity remains a challenge. Mos...
Diffusion models have achieved remarkable progress in video generation, but their controllability remains a major limitation. Key scene factors such a...
Accurate segmentation of the future liver remnant (FLR) is critical for surgical planning in colorectal liver metastases (CRLM) to prevent fatal post-...
Gastrointestinal diseases impose a growing global health burden, and endoscopy is a primary tool for early diagnosis. However, routine endoscopic imag...
Deep learning models utilizing longitudinal healthcare data have significantly advanced epidemiological research. However, contemporary transformer-ba...
Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide, yet existing prognostic models incompletely capture its molecular het...
Colorectal cancer (CRC) is a leading cause of cancer-related mortality, highlighting the need for early detection and accurate lesion characterization...
Background: Previous recommendations on screening for prostate cancer relied on ongoing trials of screening with prostate-specific antigen (PSA), whic...
Human diseases and adverse drug reactions are ultimately recognized through clinical symptoms, yet the molecular determinants of most symptoms remain ...
Based on single-cell RNA sequencing data, differentially expressed genes (LMR DEGs) between colorectal cancer liver metastasis epithelium and primary ...
Endoscopic video analysis is essential for early gastrointestinal screening but remains hindered by limited high-quality annotations. While self-super...