Utilizing artificial intelligence in endoscopy: a clinician’s guide

ABSTRACT

Introduction

Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research and development (R &D) is being actively conducted. With the development of endoscopic diagnosis, there is a shortage of specialists who can perform high-precision endoscopy. We will examine whether AI with excellent image recognition ability can overcome this problem.

Areas covered

Since 2016, papers on artificial intelligence using convolutional neural network (CNN in other word Deep Learning) have been published. CNN is generally capable of more accurate detection and classification than conventional machine learning. This is a review of papers using CNN in the gastrointestinal endoscopy area, along with the reasons why AI is required in clinical practice. We divided this review into four parts: stomach, esophagus, large intestine, and capsule endoscope (small intestine).

Expert opinion

Potential applications for the AI include colorectal polyp detection and differentiation, gastric and esophageal cancer detection, and lesion detection in capsule endoscopy. The accuracy of endoscopic diagnosis will increase if the AI and endoscopist perform the endoscopy together.

KEYWORDS:

• Artificial intelligence
• gastric cancer
• helicobacter pylori
• esophageal squamous cell carcinoma
• colon polyp
• narrow band imaging
• magnified endoscopy
• esophagogastroduodenoscopy
• colonoscopy
• capsule endoscopy

Article highlights

• AI is effective in image analysis, where in it outperforms the manual interpretations.

• The introduction of the AI in the image analysis will support medical diagnosis.

• AI-based gastric investigations were focused on the detection and diagnosis of the gastric cancers.

• AI used for anatomical classification of the stomach, will monitor unnoticeable regions and reduce the number of undetected cancers.

• AI-based investigations in the esophagus include esophageal squamous cell carcinoma and adenocarcinoma.

• AI was employed in the detection and classification of the colon polyps in the GIT endoscopy.

• AI-based capsule endoscopy would accurately detect the lesions and reduce the interpretation time.

Acknowledgments

We would like to thank Mitsue Takahashi and Motoi Miura for providing assistance in editing manuscript.

We would like to thank Editage (www.editage.com) for English language editing.

Declaration of interest

Tada T is a shareholder of AI Medical Service Inc. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose

Additional information

Funding

This paper was not funded.