Gastroenterology

A deep neural network improves endoscopic detection of laterally spreading tumors.

BACKGROUND: Colorectal cancer (CRC) is the malignant tumor of the digestive system with the highest incidence and mortality rate worldwide. Laterally spreading tumors (LSTs) of the large intestine have unique morphological characteristics, special growth patterns and higher malignant potential. Therefore, LSTs are a precancerous lesion of CRC that could be easily missed.

The potential of AI-assisted gastrectomy with dual highlighting of pancreas and connective tissue.

BACKGROUND: Standard gastrectomy with D2 lymph node (LN) dissection for gastric cancer involves peripancreatic lymphadenectomy [1]. This technically demanding procedure requires meticulous dissection within the dissectable layers of connective tissue, while identifying and preserving the pancreas [2]. Our previous study demonstrated the proficiency of Eureka, a surgical artificial intelligence (AI...

Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy.

This study introduces a novel approach for the early detection of pancreatic cancer through biofluid spectroscopy, leveraging a unique machine learnin...

In-context learning enables multimodal large language models to classify cancer pathology images.

Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundati...

Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress.

BACKGROUND: Oxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies inve...

Deep learning enabled integration of tumor microenvironment microbial profiles and host gene expressions for interpretable survival subtyping in diverse types of cancers.

The tumor microbiome, a complex community of microbes found in tumors, has been found to be linked to cancer development, progression, and treatment o...

Predictive model of in-hospital mortality in liver cirrhosis patients with hyponatremia: an artificial neural network approach.

Hyponatremia can worsen the outcomes of patients with liver cirrhosis. However, it remains unclear about how to predict the risk of death in cirrhotic...

Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach.

Yinchenhao Decoction (YCHD), a classic formula in traditional Chinese medicine, is believed to have the potential to treat liver diseases by modulatin...

Accurate non-invasive detection of MASH with fibrosis F2-F3 using a lightweight machine learning model with minimal clinical and metabolomic variables.

BACKGROUND: There are no known non-invasive tests (NITs) designed for accurately detecting metabolic dysfunction-associated steatohepatitis (MASH) wit...

CIMIL-CRC: A clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images.

BACKGROUND AND OBJECTIVE: Treatment approaches for colorectal cancer (CRC) are highly dependent on the molecular subtype, as immunotherapy has shown e...

Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy.

BACKGROUND: Intraoperative hemorrhage during laparoscopic hepatectomy (LH) is a risk factor for negative postoperative outcomes. Ensuring appropriate ...

Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model : Prediction of esophageal fistula in radiotherapy/chemoradiotherapy patients.

BACKGROUND: Esophageal fistula (EF), a rare and potentially fatal complication, can be better managed with predictive models for personalized treatmen...

Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.

Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic ...

In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning.

OBJECTIVES: One of the widely used optical biopsy methods for monitoring cellular and tissue metabolism is time-resolved fluorescence. The use of this...

Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection.

Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. ...

Automatic TNM staging of colorectal cancer radiology reports using pre-trained language models.

BACKGROUND AND OBJECTIVE: Colorectal cancer is one of the major causes of cancer death worldwide. Essential for prognosis and treatment planning, TNM ...

Response prediction for neoadjuvant treatment in locally advanced rectal cancer patients-improvement in decision-making: A systematic review.

BACKGROUND: Predicting pathological complete response (pCR) from pre or post-treatment features could be significant in improving the process of makin...

Preoperative prediction of post hepatectomy liver failure after surgery for hepatocellular carcinoma on CT-scan by machine learning and radiomics analyses.

INTRODUCTION: No instruments are available to predict preoperatively the risk of posthepatectomy liver failure (PHLF) in HCC patients. The aim was to ...

An artificial intelligence-based recognition model of colorectal liver metastases in intraoperative ultrasonography with improved accuracy through algorithm integration.

BACKGROUND/PURPOSE: Contrast-enhanced intraoperative ultrasonography (CE-IOUS) is crucial for detecting colorectal liver metastases (CLM) during surge...

Automatic localization and deep convolutional generative adversarial network-based classification of focal liver lesions in computed tomography images: A preliminary study.

BACKGROUND AND AIM: Computed tomography of the abdomen exhibits subtle and complex features of liver lesions, subjectively interpreted by physicians. ...