Gastroenterology

Performance of a deep learning-based identification system for esophageal cancer from CT images.

BACKGROUND: Because cancers of hollow organs such as the esophagus are hard to detect even by the expert physician, it is important to establish diagnostic systems to support physicians and increase the accuracy of diagnosis. In recent years, deep learning-based artificial intelligence (AI) technology has been employed for medical image recognition. However, no optimal CT diagnostic system employi...

Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer.

OBJECTIVE: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists.

R-JaunLab: Automatic Multi-Class Recognition of Jaundice on Photos of Subjects with Region Annotation Networks.

Jaundice occurs as a symptom of various diseases, such as hepatitis, the liver cancer, gallbladder or pancreas. Therefore, clinical measurement with s...

Artificial intelligence for colonoscopy: the new Silk Road.

Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network.

BACKGROUND: Colorectal cancer is harmful to the patient's life. The treatment of patients is determined by accurate preoperative staging. Magnetic res...

A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound.

BACKGROUND: Detailed evaluation of bile duct (BD) is main focus during endoscopic ultrasound (EUS). The aim of this study was to develop a system for ...

Prediction of Recurrence in Patients with Stage III Colon Cancer Using Conventional Clinicopathological Factors and Peripheral Blood Test Data: A New Analysis with Artificial Intelligence.

BACKGROUND: Survival rate may be predicted by tumor-node-metastasis staging systems in colon cancer. In clinical practice, about 20 to 30 clinicopatho...

Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies.

Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist's wor...

Performance of a convolutional neural network for automatic detection of blood and hematic residues in small bowel lumen.

Colorectal polyp characterization with standard endoscopy: Will Artificial Intelligence succeed where human eyes failed?

The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive co...

Propensity matched analysis of short term oncological and perioperative outcomes following robotic and thoracolaparoscopic esophagectomy for carcinoma esophagus- the first Indian experience.

Thoracolaparoscopic esophagectomy (TLE) for carcinoma esophagus has better short-term outcomes compared to open esophagectomy. The precise role of rob...

Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.

BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic...

Surgical training model and safe implementation of robotic pancreatoduodenectomy in Japan: a technical note.

BACKGROUND: Growing evidence for the advantages of robotic pancreatoduodenectomy (RPD) has been demonstrated internationally. However, there has been ...

A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution.

Colon carcinoma is one of the leading causes of cancer-related death in both men and women. Automatic colorectal polyp segmentation and detection in c...

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided dete...

A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images.

OBJECTIVES: Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing co...

Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models.

BACKGROUND: To validate and compare various MRI-based radiomics models to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) of recta...

Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis.

Prospective randomized trials and observational studies have revealed that early detection, classification, and removal of neoplastic colorectal polyp...

Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis.

Inflammatory bowel disease (IBD) is a complex disease which leads to life-threatening complications and decreased quality of life. The dextran sulfate...

Proposed training pathway with initial experience to set up robotic hepatobiliary and pancreatic service.

Although robot-assisted hepatobiliary and pancreatic (HPB) surgery has gained momentum over the last 2 decades, only a handful of units in the world p...