NCT05147389

Artificial Intelligence for Digital Cholangioscopy Neoplasia Diagnosis

Clinical Validation of an Artificial Intelligence Software for Digital Cholangioscopy Diagnosis: an Observational Trial

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 170 (actual)
• Sponsor: Instituto Ecuatoriano de Enfermedades Digestivas · Academic / Other
• Sex: All
• Age: 18 Years – 79 Years
• Healthy volunteers: —

Summary

Digital single-operator cholangioscopy (DSOC) findings achieve high diagnostic accuracy for neoplastic bile duct lesions. To date, there is not a universally accepted DSOC classification. Endoscopists' Intra and interobserver agreements vary widely. Cholangiocarcinoma (CCA) assessment through artificial intelligence (AI) tools is almost exclusively for intrahepatic CCA (iCCA). Therefore, more AI tools are necessary for assessing extrahepatic neoplastic bile duct lesions. In Ecuador, the investigators have recently proposed an AI model to classify bile duct lesions during real-time DSOC, which accurately detected malignancy patterns. This research pursues a clinical validation of our AI model for distinguishing between neoplastic and non-neoplastic bile duct lesions, compared with high DSOC experienced endoscopists.

Detailed description

Distinguishing neoplastic from non-neoplastic bile duct lesions is a challenge for clinicians. Magnetic resonance (MR) and biopsy guided by endoscopic retrograde cholangiopancreatography (ERCP) reached a negative predictive value (NPV) around 50%. On the other hand, Digital single-operator cholangioscopy (DSOC) findings achieve high diagnostic accuracy for neoplastic bile duct lesions. DSOC could be even better than DSOC-guided biopsy, which is inconclusive in some cases. However, to date, there is no universally accepted DSOC classification for that purpose. Also, endoscopists' Intra and interobserver agreements vary widely. Therefore, a more reproducible system is still needed. With interesting results, Cholangiocarcinoma (CCA) assessment through artificial intelligence (AI) tools has been developed based on imaging radiomics. Nevertheless, CCA AI resources are almost exclusively for intrahepatic CCA (iCCA), with an endoscopic technique. Therefore, more AI tools are necessary for assessing extrahepatic neoplastic bile duct lesions. Perihilar (pCCA) and distal (dCCA) cholangiocarcinoma as the most typical neoplastic bile duct lesions. Both represent 50-60% and 20-30% of all CCA, including secondary malignancies by local extension (hepatocarcinoma, gallbladder, and pancreas cancer). A recent Portuguese proof-of-concept study developed an AI tool based on convolutional neuronal networks (CNNs). It let to differentiate between malignant from benign bile duct lesions or normal tissue with very high accuracy. Still, it needs more external validation, including endoscopists' Intra and interobserver agreement comparison. In Ecuador, the investigators recently proposed an AI model to classify bile duct lesions during real-time DSOC, which has been able to detect malignancy pattern in all cases. This research pursues a clinical validation of our AI model for distinguishing between neoplastic and non-neoplastic bile duct lesions, compared with six endoscopists with high DSOC experience.

Conditions

• Common Bile Duct Neoplasms
• Non-Neoplastic Bile Duct Disorder

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	AI model classification	AIWorks is an artificial intelligence model for real-time cholangioscopic detection of neoplastic and non-neoplastic bile duct lesions. It allows you to choose using a video file or a USB camera input as the detection source. Once the input source has been selected, the software performs real-time detection by surrounding the area of interest (i.e., the area with malignancy features) inside a bounding box. All detections made are displayed on the right side of the screen and can also be reviewed afterwards.
DIAGNOSTIC_TEST	DSOC endoscopist experts' classification	Six endoscopists with high DSOC expertise will observe and classify a set of videos among neoplastic or non-neoplastic bile duct lesions following a Bernoulli distribution; blinded to clinical records and should have never attended said patients. Gastroenterologists from each center, with non-DSOC responsibility, will select DSOC videos and corresponding baseline data. DSOC videos and data will be gathered in one set. Each video represents a full DSOC for a single patient. The patient will be the unit of this study. The neoplastic bile duct criteria are in accordance with the Robles-Medranda et al and the Mendoza classifications (ie. Irregular mucosa surface, Tortuous and dilated vascularity, Irregular nodulations, Polyps, Ulceration, Honeycomb pattern, etc.). The experts will assess neoplastic bile duct by presence or absence of disaggregated criteria. Likewise, by Boolean logical operators, the statistical software will compute disaggregated answers.

Timeline

• Start date: 2020-10-01
• Primary completion: 2021-11-30
• Completion: 2022-05-01
• First posted: 2021-12-07
• Last updated: 2022-11-17

Locations

6 sites across 4 countries: United States, Belgium, Brazil, Ecuador