Barrett esophagus: What to expect from Artificial Intelligence?
Introduction
In the past few years, the application of Artificial Intelligence (AI) in gastrointestinal (GI) endoscopy has been the subject of intensive research; significant progress has been made, especially in the domain of image and pattern recognition [1,2]. Ultimately, the goal of AI in GI endoscopy, as in all other aspects of medicine, is to improve the quality of patient care, by aiding physicians during diagnosis and treatment.
Section snippets
Artificial Intelligence in gastrointestinal endoscopy
AI and Computer-aided Diagnosis (CAD) have been studied in both benign as well as malignant disorders of the gastrointestinal tract (GIT), including Helicobacter pylori infection, peptic ulcer evaluation, detection and characterization of cancers in the esophagus and stomach, as well as in pancreaticobiliary disease [[3], [4], [5], [6], [7], [8], [9], [10], [11], [12]]. The greatest impact of AI in GI endoscopy, up till now, has been made in the detection and characterization of colonic
Technical aspects of Artificial Intelligence
The generic term “Artificial Intelligence” is established for all procedures where input data and a training procedure are used to solve a task, such as the prediction of an object class. In the training procedure, AI uses the input data to “learn”, with the main goal of improving the ability to process new data samples which were not initially part of the training data. This process of “machine learning” (ML) can be supervised, unsupervised or reinforced, depending on whether an algorithm is
Barrett’s esophagus and its challenges in daily practice
The incidence of BE and Barrett’s cancer (BC) has risen significantly in the past decade, and because of its close association with the metabolic syndrome, this trend is expected to continue [26,27]. The early diagnosis of BC is critical for its prognosis, and justifies the need for efficient surveillance, detection and characterization strategies. However, the detection of focal regions of dysplasia or early cancer, and the characterization of abnormalities within BE, can be challenging, even
Current data on the use of artificial intelligence in Barrett’s esophagus
As described above, the detection of focal regions of dysplasia or early adenocarcinoma in BE can be challenging, particularly for non-experts. When a focal lesion has been detected, the further differentiation between non-dysplasia or inflammation, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma can be extremely difficult [34]. Mendel et al. and Ebigbo et al. were able to demonstrate the feasibility of using a deep learning approach in the detection and characterization of focal
What to expect from Artificial Intelligence in Barrett’s esophagus – future perspectives
The applications described above show the immense potential of AI in GI endoscopy, including BE. It seems obvious that more efficient tools are necessary to aid nonexpert endoscopists during the assessment of patients with BE. Computer-aided analysis and AI may be one such instrument. Because of its enormous potential, AI may not only influence the performance of endoscopists during endoscopic assessment of BE but may also improve the quality of post-procedure parameters.
In summary, and based
Practice points
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Endoscopic assessment of BE is a challenge especially for nonexpert endoscopists.
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Artificial intelligence has made significant progress in diagnostic endoscopy, especially in the identification of pathology (detection task).
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Pre-clinical and clinical studies on the application of AI in BE assessment have shown excellent results and enormous future potential.
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AI-assisted BE assessment could improve the quality of performance of endoscopists and also improve patient experience.
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AI could have a
Research agenda
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Large scale clinical trials with randomized and controlled or even tandem design.
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Virtual reconstruction, intra- or postprocedural, of entire BE surface.
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Staging of early neoplasia in BE, and better differentiation between T1a and T1b.
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Cost-effectiveness of AI in BE management.
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Effect of AI on training of nonexpert endoscopists.
Funding
None.
Declaration of competing interest
None.
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