AIMC Topic: Esophagus

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Endoscopic detection and differentiation of esophageal lesions using a deep neural network.

Gastrointestinal endoscopy Oct 1, 2019
BACKGROUND AND AIMS: Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver variability. Therefore, we developed a computerized image-analysis system to detect and differentia...
Esophageal Squamous Cell Carcinoma Esophageal Diseases Narrow Band Imaging Esophageal Neoplasms Observer Variation Precancerous Conditions Optical Imaging Aged, 80 and over Adult Sensitivity and Specificity Male Image Processing, Computer-Assisted Neoplasm Invasiveness Esophagus Middle Aged Deep Learning Neural Networks, Computer Female Reproducibility of Results Aged Humans
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Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

Artificial intelligence in medicine Jul 4, 2019
OBJECTIVES: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.
Deep Learning Esophagus Models, Biological Reinforcement, Psychology Feedback, Physiological Humans Machine Learning Algorithms Computer Simulation Physics Autonomic Nervous System Peristalsis
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Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation.

International journal of radiation oncology, biology, physics Mar 2, 2019
PURPOSE: Organ-at-risk (OAR) delineation is a key step in treatment planning but can be time consuming, resource intensive, subject to variability, and dependent on anatomical knowledge. We studied deep learning (DL) for automated delineation of mult...
Larynx Submandibular Gland Brain Stem Esophageal Sphincter, Upper Parotid Gland Organs at Risk Radiotherapy Planning, Computer-Assisted Pharyngeal Muscles Humans Esophagus Benchmarking Mouth Deep Learning Head and Neck Neoplasms
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Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy.

Medical image analysis Feb 2, 2019
In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example...
Esophagus Video Recording Datasets as Topic Algorithms Colon Microscopy, Confocal Unsupervised Machine Learning Humans
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Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.

Acta oncologica (Stockholm, Sweden) Nov 6, 2018
BACKGROUND: In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OARs) depicted in computed tomography (CT) images of lung cancer patients, and the results were compar...
Radiography, Abdominal Atlases as Topic Anatomy, Artistic Image Processing, Computer-Assisted Lung Neoplasms Esophagus Spine Organs at Risk Tumor Burden Radiotherapy Planning, Computer-Assisted Pattern Recognition, Automated Radiography, Thoracic Humans Neural Networks, Computer Tomography, X-Ray Computed
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Triple-stapled quadrilateral anastomosis: a new technique for creation of an esophagogastric anastomosis.

Esophagus : official journal of the Japan Esophageal Society Dec 16, 2017
BACKGROUND: Esophagogastric anastomosis performed after esophagectomy is technically complex and often the source of postoperative complications. The best technique for this anastomosis remains a matter of debate. We describe a new all-stapled side-t...
Surgical Flaps Surgical Stapling Esophagus Male Humans Middle Aged Omentum Retrospective Studies Anastomosis, Surgical Blood Loss, Surgical Esophagectomy Female Operative Time Esophageal Neoplasms Aged Thoracoscopy Laparotomy Stomach
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Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.

Medical physics Oct 23, 2017
PURPOSE: Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high ...
Esophagus Neural Networks, Computer Machine Learning Tomography, X-Ray Computed Imaging, Three-Dimensional
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Early Experience of Robot-Assisted Esophagectomy With Circular End-to-End Stapled Anastomosis.

The Annals of thoracic surgery May 4, 2016
BACKGROUND: Surgical resection is a critical element in the treatment of esophageal cancer. Esophagectomy is technically challenging and is associated with high morbidity and mortality rates. Efforts to reduce these rates have spurred the adoption of...
Esophagus Laparoscopy Sutures Esophagectomy Anastomosis, Surgical Middle Aged Humans Suture Techniques Esophageal Neoplasms Aged, 80 and over Retrospective Studies Time Factors Morbidity Massachusetts Adult Follow-Up Studies Female Survival Rate Robotics Aged Male
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Artificial intelligence-assisted endoscopic ultrasound diagnosis of esophageal subepithelial lesions.

Surgical endoscopy Jun 1, 2025
BACKGROUND: Endoscopic ultrasound (EUS) is one of the most accurate methods for determining the originating layer of subepithelial lesions (SELs). However, the accuracy is greatly influenced by the expertise and proficiency of the endoscopist. In thi...
Female Aged Adult Male Humans Artificial Intelligence Esophageal Diseases Middle Aged Esophagus Esophageal Neoplasms Endosonography
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Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer.

World journal of gastroenterology May 21, 2025
BACKGROUND: Esophageal cancer is the sixth most common cancer worldwide, with a high mortality rate. Early prognosis of esophageal abnormalities can improve patient survival rates. The progression of esophageal cancer follows a sequence from esophagi...
Humans Adenocarcinoma Esophagus Neoplasm Staging Esophageal Neoplasms Barrett Esophagus Neural Networks, Computer Prognosis Disease Progression Deep Learning
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