Deep Learning Algorithm for Recognition of Colonic Segments.

Shandong University

Status: Unknown

Conditions: Colonic Diseases

Treatments: Device: AI assisted recognition of colonic segments

Study type: Interventional

Funder types: Other

Identifiers

NCT04087824

2019SDU-QILU-G003

Details and patient eligibility

About

The purpose of this study is to develop and validate a deep learning algorithm to realize automatic recognition of colonic segments under conventional colonoscopy. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.

Full description

Colonoscopy is recommended as a routine examination for colorectal cancer screening. Complete inspection of all colon segments is the basis of colonoscopy quality control, and furthermore improves the detection rates of small adenomas. Recently, deep learning algorithm based on central neural networks (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. However, there is still a blank in recognition of anatomic sites, which restricts the realization of AI-aided lesions detection and disease severity scoring. This study aim to train an algorithm to recognize key colonic segments, and testify the accuracy of each segments recognition as compared to endoscopic physicians.

Enrollment: 60 estimated patients

Sex: All

Ages: 18 to 70 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients aged 18-70 years undergoing conventional colonoscopy

Exclusion criteria

• Known or suspected bowel obstruction, stricture or perforation
• Compromised swallowing reflex or mental status
• Severe chronic renal failure(creatinine clearance < 30 ml/min)
• Severe congestive heart failure (New York Heart Association class III or IV)
• Uncontrolled hypertension (systolic blood pressure > 170 mm Hg, diastolic blood pressure > 100 mm Hg)
• Dehydration
• Disturbance of electrolytes
• Pregnancy or lactation
• Hemodynamically unstable
• Unable to give informed consent

Trial design

Primary purpose: Health Services Research

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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60 participants in 1 patient group

AI monitoring colonoscopy

Experimental group

Description:

Patients in this group go through colonoscopy under the AI monitoring device.

Treatment:

Device: AI assisted recognition of colonic segments