Gastroenterology

A pipeline for automated deep learning liver segmentation (PADLLS) from contrast enhanced CT exams.

Multiple studies have created state-of-the-art liver segmentation models using Deep Convolutional Neural Networks (DCNNs) such as the V-net and H-DenseUnet. Oversegmentation however continues to be a problem. We set forth to address these limitations by developing a an automated workflow that leverages the strengths of different DCNN architectures, resulting in a pipeline that enables fully automa...

Towards annotation-efficient segmentation via image-to-image translation.

An important challenge and limiting factor in deep learning methods for medical imaging segmentation is the lack of available of annotated data to properly train models. For the specific task of tumor segmentation, the process entails clinicians labeling every slice of volumetric scans for every patient, which becomes prohibitive at the scale of datasets required to train neural networks to optima...

DAN-PD: Domain adaptive network with parallel decoder for polyp segmentation.

Endoscopy is essential for polyp diagnosis and prevention of colorectal cancer. Many deep learning methods have been proposed to perform automatic sem...

Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database.

Machine learning has been recently used especially in the medical field. In the diagnosis of serious diseases such as cancer, deep learning techniques...

Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method.

According to the limited molecular information reflected by single spectroscopy, and the complementarity of FTIR spectroscopy and Raman spectroscopy, ...

A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features.

(1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring me...

Leveraging Artificial Intelligence to Enhance Peer Review: Missed Liver Lesions on Computed Tomographic Pulmonary Angiography.

PURPOSE: The aim of this study was to use artificial intelligence (AI) to facilitate peer review for detection of missed suspicious liver lesions (SLL...

Single-port robotic ventral mesh rectopexy for recurrent rectal prolapse with the da Vinci SP platform: A video vignette.

Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model.

Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC o...

Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm.

In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during com...

[Robotic Left Hepatectomy Using the Glissonean Pedicle Approach for the Treatment of Caroli's Syndrome].

BACKGROUND: Caroli's syndrome is a rare disease characterised by non-obstructive dilation of intrahepatic bile ducts, hepatic fibrosis, and an increas...

The three horizons model applied to medical science.

The three horizons model is a framework that helps manage an organization's innovation strategy. This model considers three aspects (horizons) that sh...

Comparison of a primary and a recurrent case of full rectal prolapse treated with robotic ventral mesh rectopexy - a video vignette.

Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field.

Segmentation of the liver and tumours from computed tomography (CT) scans is an important task in hepatic surgical planning. Manual segmentation of th...

A systematic review of robot-assisted anti-reflux surgery to examine reporting standards.

Robot-assisted anti-reflux surgery (RA-ARS) is increasingly being used to treat refractory gastro-oesophageal reflux disease. The IDEAL (Idea, Develop...

Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability.

BACKGROUND AND AIMS: Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) researc...

Artificial intelligence for identification of focal lesions in intraoperative liver ultrasonography.

PURPOSE: Intraoperative ultrasonography (IOUS) of the liver is a crucial adjunct in every liver resection and may significantly impact intraoperative ...

Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation.

PURPOSE: To compare liver MRI with AIR Recon Deep Learning™(ARDL) algorithm applied and turned-off (NON-DL) with conventional high-resolution acquisit...

Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks.

Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and m...

A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma.

BACKGROUND: Accurate preoperative identification of the microvascular invasion (MVI) can relieve the pressure from personalized treatment adaptation a...