Gastroenterology

A Generalizable Machine Learning Framework for cfDNA based Early Detection of Hepatocellular Carcinoma: a Feasibility Study with Preclinical Validation

Early detection of hepatocellular carcinoma (HCC) is critical for improving patient outcomes, yet current screening tools lack sensitivity and specificity. We demonstrate a flexible machine learning framework for HCC detection using methylation profiles from bisulfite sequencing across multiple assay platforms and sample types. The framework supports a “split-and-filter” approach that routes each ...

Exploiting pair correlation function to describe biological tissue structure

Multiplexed imaging technologies now enable the simultaneous profiling of hundreds to thousands of molecular targets in intact tissues, providing unprecedented insight into cellular heterogeneity and spatial organization. While data generation has rapidly matured, the quantitative analysis of spatial structure remains challenging and poorly standardized, particularly across biological length scale...

Characterisation of 3000 patient reported outcomes with predictive machine learning to develop a scientific platform to study fatigue in Inflammatory Bowel Disease

Fatigue is commonly identified by IBD patients as major issue that affects their wellbeing. This presentation, however, is complex, multifactorial and...

Predicting ADC Map Quality from T2-Weighted MRI: A Deep Learning Approach for Early Quality Assessment to Assist Point-of-Care

Poor quality prostate MRI images, especially ADC maps, can lead to missed lesions and unnecessary repeat scans. To address this issue, we aimed to dev...

Development of a Claims-Based Computable Phenotype for Ulcerative Colitis Flares

Several conditions exist that do not have their own unique diagnosis code in widely-used clinical terminologies, making them difficult to track and st...

Artificial Intelligence in Pancreatic Intraductal Papillary Mucinous Neoplasm Imaging: A Systematic Review

Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) l...

Comparative Analysis of Machine Learning Models for Cancer Diagnosis

Pancreatic cancer is one of the most deadly cancers, with early detection being critical for improving patient outcomes. This study evaluates the perf...

Deep representation learning for clustering longitudinal survival data from electronic health records

Precision medicine requires accurate identification of clinically relevant patient subgroups. Electronic health records provide major opportunities fo...

Alterations in the hepatic microenvironment following direct-acting antiviral therapy for chronic hepatitis C

Background and aims. The first direct-acting antivirals (DAAs) to treat the viral hepatitis C (HCV) became available in 2011. Despite numerous clinica...

Probing Large Language Model Hidden States for Adverse Drug Reaction Knowledge

Large language models (LLMs) integrate knowledge from diverse sources into a single set of internal weights. However, these representations are diffic...

Galar - a large multi-label video capsule endoscopy dataset

Video capsule endoscopy (VCE) is an important technology with many advantages (non-invasive, representation of small bowel), but faces many limitation...

RNAseq-Based Machine Learning Models for Prognostication of Multiple Myeloma

Multiple myeloma (MM) is characterized by abnormal plasma cell proliferation in the bone marrow, leading to symptoms like osteolytic lesions, anemia, ...

irAE-GPT: Leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets

Large language models (LLMs) have emerged as transformative technologies, revolutionizing natural language understanding and generation across various...

Pancreatic cancer risk prediction using deep sequential modeling of longitudinal diagnostic and medication records

Pancreatic ductal adenocarcinoma (PDAC) is a rare, aggressive cancer often diagnosed late with low survival rates, due to the lack of population-wide ...

Application of Generative Artificial Intelligence to Utilise Unstructured Clinical Data for Acceleration of Inflammatory Bowel Disease Research

Inflammatory bowel disease (IBD) research is a dynamic field. However, the growing volume of electronic health records (EHRs) and research data presen...

Unmet Needs in Acute Hepatic Porphyria Diagnosis: A Comparative Big Data Analysis of an AI-based Human-in-the-Loop Screening Versus Standard of Care

Acute Hepatic Porphyria (AHP) is a rare genetic disease characterized by unpredictable life-threatening attacks. There is no reliable biochemical scre...

Comprehensive Evaluation of Human Donor Liver Viability with Polarization-Sensitive Optical Coherence Tomography

Human liver transplantation is severely constrained by a critical shortage of donor livers, with approximately one quarter of patients on the waiting ...

Predicting the stage of gastric cancer after gastrectomy based on machine learning algorithms

Gastric cancer (GC) is the fourth most common cause of cancer death worldwide, with a 5-year survival rate of less than 40%. One of the most important...

AI portal tract detection and characterisation for a regional analysis of steatosis and inflammation in MASLD, MASH, and AIH

Annotation of liver biopsies, for disease staging is increasingly aided by digital pathology, however existing systems do not quantify inflammation an...

iMDPath: Interpretable Multi-task Digital Pathology Model for Clinical Pathological Image Prediction and Interpretation

Deep learning (DL)-based pathological image modelling and analysis approaches offer transformative potential for early cancer diagnostics, yet limited...