Multimodal imaging for detection and segmentation of Barrett’s esophagus-related neoplasia using artificial intelligence

Ebigbo, Alanna; Mendel, Robert; Probst, Andreas; Meinikheim, Michael; Byrne, Michael F.; Messmann, Helmut; Palm, Christoph

doi:10.1055/a-1704-7885

Cited by 3 publications

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References 3 publications

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“…28 During the early phase of this research field, even though several AI systems were able to demonstrate promising results in preclinical and pilot-phase clinical studies, AI algorithms were mostly only able to offer a per-image evaluation of BE and BERN. Ebigbo et al 29 were one of the first research groups to differentiate BE from BERN in 3 different imaging modalities in real time (Fig. 2).…”

Section: A a C C B B D Dmentioning

confidence: 99%

Role of artificial intelligence in diagnosing Barrett’s esophagus-related neoplasia

2023

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Barrett’s esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett’s esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett’s esophagus and elaborate on potential artificial intelligence in the future.

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Section: A a C C B B D Dmentioning

confidence: 99%

Role of artificial intelligence in diagnosing Barrett’s esophagus-related neoplasia

2023

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Künstliche Intelligenz in der gastrointestinalen Endoskopie

Ebigbo¹,

Messmann²

2022

Gastro-News

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Forrest Classification for Bleeding Peptic Ulcer: A New Look at the Old Endoscopic Classification

Yen

et al. 2022

Diagnostics

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The management of peptic ulcer bleeding is clinically challenging. For decades, the Forrest classification has been used for risk stratification for nonvariceal ulcer bleeding. The perception and interpretation of the Forrest classification vary among different endoscopists. The relationship between the bleeder and ulcer images and the different stages of the Forrest classification has not been studied yet. Endoscopic still images of 276 patients with peptic ulcer bleeding for the past 3 years were retrieved and reviewed. The intra-rater agreement and inter-rater agreement were compared. The obtained endoscopic images were manually drawn to delineate the extent of the ulcer and bleeding area. The areas of the region of interest were compared between the different stages of the Forrest classification. A total of 276 images were first classified by two experienced tutor endoscopists. The images were reviewed by six other endoscopists. A good intra-rater correlation was observed (0.92–0.98). A good inter-rater correlation was observed among the different levels of experience (0.639–0.859). The correlation was higher among tutor and junior endoscopists than among experienced endoscopists. Low-risk Forrest IIC and III lesions show distinct patterns compared to high-risk Forrest I, IIA, or IIB lesions. We found good agreement of the Forrest classification among different endoscopists in a single institution. This is the first study to quantitively analyze the obtained and explain the distinct patterns of bleeding ulcers from endoscopy images.

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Multimodal imaging for detection and segmentation of Barrett’s esophagus-related neoplasia using artificial intelligence

Cited by 3 publications

References 3 publications

Role of artificial intelligence in diagnosing Barrett’s esophagus-related neoplasia

Role of artificial intelligence in diagnosing Barrett’s esophagus-related neoplasia

Künstliche Intelligenz in der gastrointestinalen Endoskopie

Forrest Classification for Bleeding Peptic Ulcer: A New Look at the Old Endoscopic Classification

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