Gastroenterology

Investigation of radiomics and deep convolutional neural networks approaches for glioma grading.

To determine glioma grading by applying radiomic analysis or deep convolutional neural networks (DCN...

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy.

OBJECTIVE: To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frame...

Robotic Right Posterior Sectionectomy by Intrafascial Approach for Pancreas Neuroendocrine Liver Metastasis.

BACKGROUND: Liver resection is indicated for resectable liver metastases of neuroendocrine tumors. M...

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator.

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ...

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review.

BACKGROUND AND AIMS: Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic ...

Hepatic vessels segmentation using deep learning and preprocessing enhancement.

PURPOSE: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients ...

Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.

OBJECTIVE: To investigate the image quality and lesion conspicuity of a deep-learning-based contrast...

Overall mortality risk analysis for rectal cancer using deep learning-based fuzzy systems.

Colorectal cancer is a leading cause of cancer mortality worldwide, with an increasing incidence rat...

Comparative study of convolutional neural network architectures for gastrointestinal lesions classification.

The gastrointestinal (GI) tract can be affected by different diseases or lesions such as esophagitis...

Robotic versus open total pancreatectomy: a systematic review and meta-analysis.

Limited data are available on postoperative outcomes in patients undergoing robotic total pancreatec...

Leverage prior texture information in deep learning-based liver tumor segmentation: A plug-and-play Texture-Based Auto Pseudo Label module.

Segmenting the liver and tumor regions using CT scans is crucial for the subsequent treatment in cli...

[Robotic esophageal surgery].

Robot-assisted minimally invasive esophagectomy (RAMIE) is increasingly becoming established as a st...

Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.

BACKGROUND: Infectious diseases are a major threat to public health, causing serious medical consump...

Robotic transanal minimally invasive local excision for rectal polyps - A video vignette.

Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning.

OBJECTIVES: This study aimed to investigate whether a deep learning (DL) model based on preoperative...

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study.

BACKGROUND: A prognostic assessment method with good sensitivity and specificity plays an important ...

Elective robotic partial colon and rectal resections: series of 170 consecutive robot procedures involving the Da Vinci Xi robot by a community general surgeon.

Robotic colorectal procedures may overcome the disadvantages of laparoscopic surgery. While the lite...

Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation.

OBJECTIVE: We aimed to investigate whether image standardization using deep learning-based computed ...

Evaluation of deep learning based implanted fiducial markers tracking in pancreatic cancer patients.

Real-time target position verification during pancreas stereotactic body radiation therapy (SBRT) is...

EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation.

Although various methods based on convolutional neural networks have improved the performance of bio...

Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images.

Despite being widely utilized to help endoscopists identify gastrointestinal (GI) tract diseases usi...