Colon Cancer

Major colorectal resection is feasible using a new robotic surgical platform: the first report of a case series.

BACKGROUND: The number of abdominal procedures performed via a robotic-assisted approach is increasing as potential advantages of the modality are recognised. We report the first in human case series of major colorectal resection performed using a new system, Versius, and assess the feasibility of its use.

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of in Relation to Hydrographic Conditions.

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species-the bubblegum coral -in response to am...

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, cur...

Development and Validation of a Gene Signature Classifier for Consensus Molecular Subtyping of Colorectal Carcinoma in a CLIA-Certified Setting.

PURPOSE: Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated...

Application of deep learning to predict advanced neoplasia using big clinical data in colorectal cancer screening of asymptomatic adults.

BACKGROUND/AIMS: We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal neoplasia (ACRN) in asymptomatic adul...

Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models.

In vivo diseases such as colorectal cancer and gastric cancer are increasingly occurring in humans. These are two of the most common types of cancer t...

Artificial Intelligence and Its Role in Identifying Esophageal Neoplasia.

Randomized trials have demonstrated that ablation of dysplastic Barrett's esophagus can reduce the risk of progression to cancer. Endoscopic resection...

Real-time unblinding for validation of a new CADe tool for colorectal polyp detection.

A novel humanoid-robot simulator for colonoscopy.

Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer.

BACKGROUND: An artificial intelligence method could accelerate the clinical implementation of tumour-stroma ratio (TSR), which has prognostic relevanc...

Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis.

OBJECTIVE: The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancre...

A review of water exchange and artificial intelligence in improving adenoma detection.

Water exchange (WE) and artificial intelligence (AI) have made critical advances during the past decade. WE significantly increases adenoma detection ...

Unravelling the effect of data augmentation transformations in polyp segmentation.

PURPOSE: Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to m...

Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node.

BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection...

Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan.

BACKGROUND: Introducing deep learning approach to medical images has rendered a large amount of un-decoded information into usage in clinical research...

Convolutional neural networks versus radiologists in characterization of small hypoattenuating hepatic nodules on CT: a critical diagnostic challenge in staging of colorectal carcinoma.

Our objective was to compare the diagnostic performance and diagnostic confidence of convolutional neural networks (CNN) to radiologists in characteri...

Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists.

OBJECTIVES: The microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for compute...

Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine.

OBJECTIVE: The success of treatment planning relies critically on our ability to predict the potential benefit of a therapy. In colorectal cancer (CRC...

Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports.

BACKGROUND AND AIMS: Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a non-stan...

Hospital robotic use for colorectal cancer care.

The use of robotic surgery for colorectal cancer continues to increase. However, not all organizations offer patients the option of robotic interventi...