Gastroenterology

Identification of antigen-presentation related B cells as a key player in Crohn's disease using single-cell dissecting, hdWGCNA, and deep learning.

Crohn's disease (CD) arises from intricate intercellular interactions within the intestinal lamina propria. Our objective was to use single-cell RNA sequencing to investigate CD pathogenesis and explore its clinical significance. We identified a distinct subset of B cells, highly infiltrated in the CD lamina propria, that expressed genes related to antigen presentation. Using high-dimensional weig...

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accu...

Distribution Patterns of Subgroups of Inhibitory Neurons Divided by Calbindin 1.

The inhibitory neurons in the brain play an essential role in neural network firing patterns by releasing γ-aminobutyric acid (GABA) as the neurotrans...

Comparison of quality of life after robotic-transvaginal natural orifice transluminal endoscopic surgery and robot-assisted laparoscopic hysterectomy.

OBJECTIVES: We investigated quality of life (QOL) of patients who underwent total hysterectomy for benign uterine diseases using two surgical approach...

ChatGPT: The transformative influence of generative AI on science and healthcare.

In an age where technology is evolving at a sometimes incomprehensibly rapid pace, the liver community must adjust and learn to embrace breakthroughs ...

Feature-guided deep learning reduces signal loss and increases lesion CNR in diffusion-weighted imaging of the liver.

PURPOSE: This research aims to develop a feature-guided deep learning approach and compare it with an optimized conventional post-processing algorithm...

A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study.

The pathology of animal studies is crucial for toxicity evaluations and regulatory assessments, but the manual examination of slides by pathologists r...

Suitability of DNN-based vessel segmentation for SIRT planning.

PURPOSE: The segmentation of the hepatic arteries (HA) is essential for state-of-the-art pre-interventional planning of selective internal radiation t...

Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy.

OBJECTIVES: To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR...

Does previous endoscopic subureteric injection (STING) effect the outcomes of robot-assisted laparoscopic ureteral reimplantation surgery (RALUR) in children?

BACKGROUND: There is lack of evidence on the success of robot-assisted laparoscopic ureteral reimplantation (RALUR) for the treatment of vesicouretera...

A deep-learning radiomics-based lymph node metastasis predictive model for pancreatic cancer: a diagnostic study.

OBJECTIVES: Preoperative lymph node (LN) status is essential in formulating the treatment strategy among pancreatic cancer patients. However, it is st...

A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration.

. This study aimed to develop a novel method for generating synthetic CT (sCT) from cone-beam CT (CBCT) of the abdomen/pelvis with bowel gas pockets t...

Preoperative planning and intraoperative real-time navigation with indocyanine green fluorescence in robotic liver surgery.

PURPOSE: We aimed at exploring indocyanine green (ICG) fluorescence wide spectrum of applications in hepatobiliary surgery as can result particularly ...

Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions.

BACKGROUND: Artificial intelligence (AI) performed variously among test sets with different diversity due to sample selection bias, which can be stumb...

Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study.

INTRODUCTION: Complex oncological procedures pose various surgical challenges including dissection in distinct tissue planes and preservation of vulne...

Economic analysis of open versus laparoscopic versus robot-assisted versus transanal total mesorectal excision in rectal cancer patients: A systematic review.

OBJECTIVES: Minimally invasive total mesorectal excision is increasingly being used as an alternative to open surgery in the treatment of patients wit...

Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram.

OBJECTIVES: Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus fluorouracil and leucovorin) is a promising optio...

[Robot-assisted visceral surgery in Germany : Analysis of the current status and trends of the last 5 years using data from the StuDoQ|Robotics registry].

Robot-assisted systems have been increasingly used in general surgery for several years. Accordingly, the number of systems installed in Germany has a...

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.

Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity h...

Impact of robotic and open surgery on patient wound complications in gastric cancer surgery: A meta-analysis.

This meta-analysis is intended to evaluate the effect of both robotic and open-cut operations on postoperative complications of stomach carcinoma. Fro...