Clinical trial NCT05321472: Artificial Intelligence-assisted Common Bile Duct Stent Selection in Endoscopic Retrograde Cholangiopancreatography
Official title: Artificial Intelligence-assisted Common Bile Duct Stent Selection in Endoscopic Retrograde Cholangiopancreatography
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Stenosis of Bile Duct
Endoscopic Retrograde Cholangiopancreatography
Lead sponsor: Shandong University
Sponsor class: OTHER
First posted: 2022-04-11
Enrollment: 600
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The eligible images was randomly devided into two groups. One group for training the algorithm and the other group for testing the algorithm.

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.

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.

Eligibility 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)

Primary outcomes:
measure: The accuracy of the calculated length of the stents by the artificial intelligence
timeFrame: 6 months
Secondary outcomes:
measure: The accuracy of the segmentation of the artificial intelligence
timeFrame: 4 months
Other outcomes:
Locations:
city: Jinan
country: China
facility: Department of Gastroenterology,QiLu Hospital,Shandong University
state: Shandong
Study officials:
affiliation: Qilu Hospital, Shandong University
name: Yanqing Li, MD, PhD
role: STUDY_CHAIR
References:
citation: 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. No abstract available.
citation: 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.