Gastroenterology

Temporally-Aware Supervised Contrastive Learning for Polyp Counting in Colonoscopy

Automated polyp counting in colonoscopy is a crucial step toward automated procedure reporting and quality control, aiming to enhance the cost-effectiveness of colonoscopy screening. Counting polyps in a procedure involves detecting and tracking polyps, and then clustering tracklets that belong to the same polyp entity. Existing methods for polyp counting rely on self-supervised learning and pri...

Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion

Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging to implement for highly mobile gastrointestinal (GI) organs. To address this, patient-specific digital twins (DT) modeling temporally varying motion were created to assess the accuracy of DIR methods. Approach: 21 mot...

Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation

Polyp segmentation in colonoscopy images is crucial for early detection and diagnosis of colorectal cancer. However, this task remains a significant...

Multitask Deep Learning Based on Longitudinal CT Images Facilitates Prediction of Lymph Node Metastasis and Survival in Chemotherapy-Treated Gastric Cancer.

UNLABELLED: Accurate preoperative assessment of lymph node metastasis (LNM) and overall survival (OS) status is essential for patients with locally ad...

Topology-Constrained Learning for Efficient Laparoscopic Liver Landmark Detection

Liver landmarks provide crucial anatomical guidance to the surgeon during laparoscopic liver surgery to minimize surgical risk. However, the tubular...

Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.

Since the discovery of the life-saving hormone insulin in 1921 by Dr. Frederick Banting in 1921, there have been many critical discoveries and technic...

Image-based detection of the internal carotid arteries and sella turcica in endoscopic endonasal transsphenoidal surgery.

OBJECTIVE: Endoscopic endonasal transsphenoidal surgery (EETS) is a minimally invasive procedure that accesses the sellar and parasellar regions. Vari...

Artificial Intelligence-Assisted Daily Quality Control System for the Histologic Diagnosis of Gastrointestinal Endoscopic Biopsies: A 1-Year Experience.

CONTEXT.—: Seegene Medical Foundation, one of the major clinical laboratories in South Korea, developed SeeDP, an artificial intelligence (AI)-based p...

DETECTION OF HYPOVOLEMIA BY THE HYPOTENSION PREDICTION INDEX IS ASSOCIATED WITH GASTROINTESTINAL MICROCIRCULATION DYSFUNCTION IN A PORCINE MODEL OF HEMORRHAGE.

Background: Postoperative gastrointestinal (GI) dysfunction is a common complication following critical illness. The splanchnic circulation is sensiti...

Differentiating benign and hepatocellular carcinoma cirrhotic nodules: radiomics analysis of water restriction patterns with diffusion MRI.

OBJECTIVES: Current study aimed to investigate radiomics features derived from 2-centre diffusion-MRI to differentiate benign and hepatocellular carci...

Deep Learning Reveals Liver MRI Features Associated With PNPLA3 I148M in Steatotic Liver Disease.

BACKGROUND: Steatotic liver disease (SLD) is the most common liver disease worldwide, affecting 30% of the global population. It is strongly associate...

Liver Fat Fraction and Machine Learning Improve Steatohepatitis Diagnosis in Liver Transplant Patients.

Machine learning identifies liver fat fraction (FF) measured by H MR spectroscopy, insulinemia, and elastography as robust, non-invasive biomarkers fo...

Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology.

The spatial structure of various cell types in the tumour microenvironment (TME) can provide valuable insights into disease progression. However, iden...

A Deep Learning Model Based on High-Frequency Ultrasound Images for Classification of Different Stages of Liver Fibrosis.

BACKGROUND AND AIMS: To develop a deep learning model based on high-frequency ultrasound images to classify different stages of liver fibrosis in chro...

Integration of Metabolomics, Lipidomics, and Machine Learning for Developing a Biomarker Panel to Distinguish the Severity of Metabolic-Associated Fatty Liver Disease.

Metabolic-associated fatty liver disease (MAFLD), a global health challenge linked to metabolic syndrome, requires accurate severity stratification fo...

Digital Pathology Quantification of the Continuum of Cirrhosis Severity in Human Liver Biopsies.

BACKGROUND AND AIMS: Liver biopsy is the gold standard for assessing fibrosis in cirrhotic livers, yet cirrhosis is spatially heterogeneous and contin...

The Interplay of Cross-Organ Immune Regulation in Inflammation and Cancer.

Organs dynamically interact with each other through immunomodulation to create a systemic immune response and influence disease progression. While tra...

Medical management of pancreatic cancer: from personalization to broadening treatment strategies.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most heterogeneous and deadly cancers. This review examines recently implemented strategies to i...

Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival of under 10 % despite current therapies...

Advances in pancreatic cancer diagnosis: from DNA methylation to AI-assisted imaging.

INTRODUCTION: Pancreatic Cancer (PC) is a highly aggressive tumor that is mainly diagnosed at later stages. Various imaging technologies, such as CT, ...