Artificial Intelligence-assisted Common Bile Duct Stent Selection in Endoscopic Retrograde Cholangiopancreatography
Study Details
Study Description
Brief Summary
Common bile duct stenosis is an important indication for endoscopic retrograde cholangiopancreatography(ERCP). Appropriate selection of bile duct stent size is not only conducive to successful stent implantation but also to improve the prognosis of patients. Currently, the selection of stent specifications is based on the operator's empirical estimation, which is not only not accurate but also increases the radiation exposure time, causing unnecessary harm to both the operator and the patient. Our objective is to develop an artificial intelligence algorithm to automatically select appropriate stent.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
|
Detailed Description
Endoscopic Retrograde Cholangiopancreatography (ERCP) is an operation with high risk. Common bile duct stone and stenosis are important indications. The quality control of ERCP is the key to improve its success rate and reduce complications, which has received great attention. In 2015, the American Society of Gastrointestinal Endoscopy/American College of Gastroenterology (ASGE/ACG) issued ERCP quality control indicators, among which biliary stent placement and radiographic fluoroscopy time are important intraoperative quality control indicators.
The selection of appropriate biliary stent size is not only conducive to successful stent implantation but also to improve the prognosis of patients. Choose a stent of appropriate length. The proximal side of the stent should be 1cm above the obstruction segment, and the distal tail should be located just outside the nipple. The length of the stent can be determined by measuring the distance between the proximal end of the obstruction and the nipple under X-ray.
Current stent size selection is based on the operator's empirical estimation :(1) estimate the distance by endoscope diameter or cone length or catheter marking; (2) By retracting the guidewire, calculate the distance of the guidewire retracting between two points to estimate the length of the stent.The long radiation exposure time results in unnecessary injuries to both the operator and the patient.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| group for training the algorithm This group of images is used for training the algorithm of the artificial intelligence |
|
| group for testing the algorithm This group of images is used for testing the algorithm of the artificial intelligence |
Outcome Measures
Primary Outcome Measures
- The accuracy of the calculated length of the stents by the artificial intelligence [6 months]
The length of the stent was calculated "the length from the stenosis to the papilla+2cm".The length of the stent selected by experts is the gold standard
Secondary Outcome Measures
- The accuracy of the segmentation of the artificial intelligence [4 months]
The accuracy of the segmentation of the common bile duct, duodenoscopy and stenosis lesions by the artificial intelligence
Eligibility Criteria
Criteria
Inclusion Criteria:
- Patients older than 18 years old who underwent ERCP
Exclusion Criteria:
-
failed cholangiopancreatography caused by failed intubation, gastric retention, duodenal disease and so on
-
patients proved no stenosis in common bile duct
-
poor cholangiograms due to the lack of contrast agent or insufficient filling of contrast agent (cholangiograms without the completed CBD or thumbnails)
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Department of Gastroenterology,QiLu Hospital,Shandong University | Jinan | Shandong | China | 250012 |
Sponsors and Collaborators
- Shandong University
Investigators
- Study Chair: Yanqing Li, MD, PhD, Qilu Hospital, Shandong University
Study Documents (Full-Text)
None provided.More Information
Publications
- Adler DG, Lieb JG 2nd, Cohen J, Pike IM, Park WG, Rizk MK, Sawhney MS, Scheiman JM, Shaheen NJ, Sherman S, Wani S. Quality indicators for ERCP. Gastrointest Endosc. 2015 Jan;81(1):54-66. doi: 10.1016/j.gie.2014.07.056. Epub 2014 Dec 2. Review. Erratum in: Gastrointest Endosc. 2015 Apr;81(4):1060.
- Huang L, Lu X, Huang X, Zou X, Wu L, Zhou Z, Wu D, Tang D, Chen D, Wan X, Zhu Z, Deng T, Shen L, Liu J, Zhu Y, Gong D, Chen D, Zhong Y, Liu F, Yu H. Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study. Endoscopy. 2021 May;53(5):491-498. doi: 10.1055/a-1244-5698. Epub 2020 Nov 9.
- 2021-SDU-QILU-090