AIMC Topic: Esophagus
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Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm.
Sensors (Basel, Switzerland)
Jul 2, 2021
One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate...
Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning.
Nature medicine
Apr 15, 2021
Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how to optimally combine them with expert knowledge and existing clinical decision pathways is still an open challenge. This question is particularly impo...
Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy.
Scientific reports
Mar 3, 2021
Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett's esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between path...
Propensity matched analysis of short term oncological and perioperative outcomes following robotic and thoracolaparoscopic esophagectomy for carcinoma esophagus- the first Indian experience.
Journal of robotic surgery
Feb 20, 2021
Thoracolaparoscopic esophagectomy (TLE) for carcinoma esophagus has better short-term outcomes compared to open esophagectomy. The precise role of robot-assisted laparoscopic esophagectomy (RALE) is still evolving. Single center retrospective analysi...
The Potential and the Limitations of Esophageal Robotic Surgery in Children.
European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie
Dec 30, 2020
INTRODUCTION:  There have been numerous reports of robotic pediatric surgery in the literature, particularly regarding urological procedures for school-aged children. Thoracic procedures appear to be less common, despite the fact that encouraging res...
Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.
Physics in medicine and biology
Nov 27, 2020
Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatm...
Esophagus segmentation from planning CT images using an atlas-based deep learning approach.
Computer methods and programs in biomedicine
Aug 7, 2020
BACKGROUND AND OBJECTIVE: One of the main steps in the planning of radiotherapy (RT) is the segmentation of organs at risk (OARs) in Computed Tomography (CT). The esophagus is one of the most difficult OARs to segment. The boundaries between the esop...
The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future.
Medicina (Kaunas, Lithuania)
Jul 21, 2020
In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implem...
Achalasia subtypes can be identified with functional luminal imaging probe (FLIP) panometry using a supervised machine learning process.
Neurogastroenterology and motility
Jul 1, 2020
BACKGROUND: Achalasia subtypes on high-resolution manometry (HRM) prognosticate treatment response and help direct management plan. We aimed to utilize parameters of distension-induced contractility and pressurization on functional luminal imaging pr...
An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy.
Scientific reports
Feb 17, 2020
We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art comput...
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