AIMC Topic: Esophagoscopy
Clear Filters Showing 21 to 27 of 27 articles
Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study.
United European gastroenterology journal
Jan 6, 2019
BACKGROUND: Intrapapillary capillary loops (IPCLs) represent an endoscopically visible feature of early squamous cell neoplasia (ESCN) which correlate with invasion depth - an important factor in the success of curative endoscopic therapy. IPCLs visu...
Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus.
Esophagus : official journal of the Japan Esophageal Society
Dec 13, 2018
BACKGROUND AND AIMS: The endocytoscopic system (ECS) helps in virtual realization of histology and can aid in confirming histological diagnosis in vivo. We propose replacing biopsy-based histology for esophageal squamous cell carcinoma (ESCC) by usin...
Artificial intelligence-aided optical biopsy improves the diagnosis of esophageal squamous neoplasm.
World journal of gastroenterology
Apr 7, 2025
BACKGROUND: Early detection of esophageal squamous neoplasms (ESN) is essential for improving patient prognosis. Optical diagnosis of ESN remains challenging. Probe-based confocal laser endomicroscopy (pCLE) enables accurate histological observation...
A deep learning system for detection of early Barrett's neoplasia: a model development and validation study.
The Lancet. Digital health
Dec 1, 2023
BACKGROUND: Computer-aided detection (CADe) systems could assist endoscopists in detecting early neoplasia in Barrett's oesophagus, which could be difficult to detect in endoscopic images. The aim of this study was to develop, test, and benchmark a C...
Artificial intelligence in gastrointestinal endoscopy: general overview.
Chinese medical journal
Feb 5, 2020
Artificial intelligence (AI) is now a trendy subject in clinical medicine and especially in gastrointestinal (GI) endoscopy. AI has the potential to improve the quality of GI endoscopy at all levels. It will compensate for humans' errors and limited ...
CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2019
OBJECTIVE: This article presents a novel method of semisupervised learning using convolutional autoencoders for optical endomicroscopic images. Optical endomicroscopy (OE) is a newly emerged biomedical imaging modality that can support real-time clin...
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