Artificial Intelligence, Confocal Laser Endomicroscopy, Ulcerative Colitis
Conditions
Brief summary
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables to evaluate the inflammation activity of ulcerative colitis with excellent correlation with histopathology. However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.
Interventions
DIAGNOSTIC_TESTThe diagnosis of Artificial Intelligence and endoscopist
When the colon mucosa is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI.
Sponsors
Shandong University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* aged between 18 and 80; diagnosed as UC
Exclusion criteria
* Patients under conditions unsuitable for performing CLE including coagulopathy , impaired renal or hepatic function, pregnancy or breastfeeding, and known allergy to fluorescein sodium; Inability to provide informed consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The diagnosis efficiency of Artificial Intelligence | 3 months | The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for evaluation of inflammation activity of UC on pCLE examination. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists | 3 months | The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV ) between Artificial Intelligence and endoscopists. |
Countries
China
Contacts
Primary ContactYanqing Li
Outcome results
None listed