Colon Cancer

The predictive power of artificial intelligence on mediastinal lymphnode metastasis.

OBJECTIVE: The aim of this study was to create the preoperative predictive model on mediastinal lymph-node metastasis based on artificial intelligence in surgically resected lung adenocarcinoma.

Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer.

PURPOSE: Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC) using data from two independent datasets.

Current status and limitations of artificial intelligence in colonoscopy.

BACKGROUND: Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinica...

A Preliminary Study of Deep-Learning Algorithm for Analyzing Multiplex Immunofluorescence Biomarkers in Body Fluid Cytology Specimens.

INTRODUCTION: Multiplex biomarker analysis of cytological body fluid specimens is often used to assist cytologists in distiguishing metastatic cancer ...

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer.

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endange...

A robust real-time deep learning based automatic polyp detection system.

Colorectal cancer (CRC) is globally the third most common type of cancer. Colonoscopy is considered the gold standard in colorectal cancer screening a...

Power of big data to improve patient care in gastroenterology.

Big data is defined as being large, varied or frequently updated, and usually generated from real-world interaction. With the unprecedented availabili...

Improved perioperative outcomes and reduced inflammatory stress response in malignant robot-assisted colorectal resections: a retrospective cohort study of 298 patients.

BACKGROUND: Robot-assisted surgery is increasingly implemented for the resection of colorectal cancer, although the scientific evidence for adopting t...

Enabling Autonomous Colonoscopy Intervention Using a Robotic Endoscope Platform.

OBJECTIVE: Robotic endoscopes have the potential to dramatically improve endoscopy procedures, however current attempts remain limited due to mobility...

AI based colorectal disease detection using real-time screening colonoscopy.

Colonoscopy is an effective tool for early screening of colorectal diseases. However, the application of colonoscopy in distinguishing different intes...

Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task.

PURPOSE: A three-dimensional (3D) structure extraction technique viewed from a two-dimensional image is essential for the development of a computer-ai...

Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction.

PURPOSE: To evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic low-dose computed tomography...

Cytotoxicity and cell imaging of six types of carbon nanodots prepared through carbonization and hydrothermal processing of natural plant materials.

In this study we prepared six types of carbon nanodots (CNDs) from natural plant materials - through carbonization of two species of bamboo (Bamboo-I,...

Intraoperative and postoperative complications in colorectal procedures: the role of continuous updating in medicine.

Accepting surgical complications, especially those related to the learning curve, as unavoidable events in colorectal procedures, is like accepting to...

A frogspawn-like Ag@C core-shell structure for an ultrasensitive label-free electrochemical immunosensing of carcinoembryonic antigen in blood plasma.

A three-dimensional (3D) frogspawn-like structure was achieved by simply coating nano-carbon outside silver nanospheres (Ag@C NFS) and used as a probe...

Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review.

This short article describes the method of digital cytopathology using Z-stack scanning with or without extended focusing. This technology is suitable...

Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer.

BACKGROUND: Tumor mutational burden-high (TMB-H), which is detected with gene panel testing, is a promising biomarker for immune checkpoint inhibitors...

Gastric polyp detection in gastroscopic images using deep neural network.

This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. Gastric polyps...

Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer.

High levels of microsatellite instability (MSI-H) occurs in about 15% of sporadic colorectal cancer (CRC) and is an important predictive marker for re...

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential.

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients f...