Colonoscopy, Deep Learning, Ulcerative Colitis
Conditions
Brief summary
This project will use deep learning to classify colonoscopy images of different severity of ulcerative colitis, so as to assist clinicians in the accurate diagnosis of ulcerative colitis.
Detailed description
In this project, artificial intelligence was used to colonoscopic images of patients with ulcerative colitis with different disease activity levels and classify them according to the evaluation standard Mayo score to assist endoscopists in identifying disease activity levels of patients with ulcerative colitis during colonoscopy. It can help clinical endoscopists to accurately identify, and the visualization technology of artificial intelligence category response map can comprehensively display the areas with high importance for deep network classification results, and visualize the experimental lesion sites, thus effectively verifying the reliability and interpretability of deep network. This study can provide strong support for accurate identification of disease activity in clinical ulcerative colitis, effectively reduce the workload of clinicians, and provide a convenient, effective and practical clinical teaching tool.
Interventions
None listed
Sponsors
Third Military Medical University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 72 Years
Healthy volunteers
Yes
Inclusion criteria
1. Subjects were 18-72 years old, male and female; 2. Clinical diagnosis of ulcerative colitis; 3. The subjects underwent colonoscopy and the colonoscopy report was complete.
Exclusion criteria
1. Subjects are younger than 18 years old or older than 72 years old; 2. Subjects underwent colectomy, ileostomy, colostomy, ileostomy, or other intestinal resection; 3. subjects with ambiguous diagnosis.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The accuracy of deep learning model in the training and validation datasets assessment of Mayo score in ulcerative colitis patients. | Through study completion, an average of 1 year. | In the training and validation datasets, we plotted the AUC (area under curve) for Mayo 0, Mayo 1, Mayo 2, and Mayo 3 to evaluate our model objectively. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The accuracy and time efficiency of endoscopists assessment of Mayo score in ulcerative colitis patients. | Through study completion, an average of 1 year. | The dataets were randomly assigned to endoscopists. All endoscopists were trained in diagnostic studies, finished both clinical and specific endoscopic training, and were not involved in the enrollment and labeling of the patients and images. During the comparison test, all data were randomized and deidentified beforehand. The average time spent by 10 endoscopists in diagnosing the test dataset in the deep learning model and the number of correct cases were analyzed. |
Countries
China
Contacts
Primary ContactYanling Wei, professor
Outcome results
None listed