Gastroenterology

Artificial intelligence for colonoscopic polyp detection: High performance versus human nature.

Artificial intelligence in gastric cancer: Application and future perspectives.

Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes...

[Risk factors for postoperative intestinal obstruction in patients undergoing robot-assisted laparoscopic radical prostatectomy].

To analyze the risk factors of postoperative intestinal obstruction (POI) in patients undergoing robot-assisted laparoscopic radical prostatectomy (R...

Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis.

BACKGROUND: Efforts should be made to develop a deep-learning diagnosis system to distinguish pancreatic cancer from benign tissue due to the high mor...

Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol.

PURPOSE: To evaluate whether a three-phase dynamic contrast-enhanced CT protocol, when combined with a deep learning model, has similar accuracy in di...

Robotics in flexible endoscopy: current status and future prospects.

PURPOSE OF REVIEW: Advanced endoscopy procedures are technically challenging and require extensive training. Recent technological advances made in com...

Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer.

Delineation of organs at risk (OARs) is important but time consuming for radiotherapy planning. Automatic segmentation of OARs based on convolutional ...

Automated Measurements of Muscle Mass Using Deep Learning Can Predict Clinical Outcomes in Patients With Liver Disease.

INTRODUCTION: There is increasing recognition of the central role of muscle mass in predicting clinical outcomes in patients with liver disease. Muscl...

Artificial intelligence in transplantation (machine-learning classifiers and transplant oncology).

PURPOSE OF REVIEW: To highlight recent efforts in the development and implementation of machine learning in transplant oncology - a field that uses li...

Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.

PURPOSE OF REVIEW: Classifiers based on artificial intelligence can be useful to solve decision problems related to the inclusion or removal of possib...

Machine Learning Prognostic Models for Gastrointestinal Bleeding Using Electronic Health Record Data.

Risk assessment tools for patients with gastrointestinal bleeding may be used for determining level of care and informing management decisions. Develo...

'I'm over the moon!': patient-perceived outcomes of hepatitis C treatment.

Understanding patient-perceived outcomes is crucial for assessing the effectiveness and acceptability of hepatitis C virus (HCV) treatment. This study...

Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer: A Tell-Tale Sign to Early Detection.

Pancreatic cancer continues to be one of the deadliest malignancies and is the third leading cause of cancer-related mortality in the United States. B...

Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images.

OBJECTIVE: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the ...

Colon capsule endoscopy versus CT colonography after incomplete colonoscopy. Application of artificial intelligence algorithms to identify complete colonic investigations.

BACKGROUND: Guidelines suggest computed tomography colonography (CTC) following incomplete optical colonoscopy (OC). Colon capsule endoscopies (CCE) h...

Prediction of immune checkpoint inhibition with immune oncology-related gene expression in gastrointestinal cancer using a machine learning classifier.

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of gastrointestinal cancer. However, biomarkers correlated with the ...

Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality.

BACKGROUND: Colonoscopy is the gold standard for polyp detection, but polyps may be missed. Artificial intelligence (AI) technologies may assist in po...

[Establishment and clinical testing of pancreatic cancer Faster R-CNN AI system based on fast regional convolutional neural network].

To investigate the effectiveness of an enhanced CT automatic recognition system based on Faster R-CNN for pancreatic cancer and its clinical value. ...

Using Computer-Aided Polyp Detection During Colonoscopy.

Computer-Aided Diagonosis for Colorectal Cancer using Deep Learning with Visual Explanations.

Detection, diagnosis, and removal of colorectal neoplasms are well-accepted colorectal cancer prevention methods. Although promising endoscopic imagin...