NCT05476978

Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions

Utilization of Artificial Intelligence for the Development of an EUS-convolution Neural Network Model Trained to Differentiate Pancreatic Cancer From Other Pancreatic Solid Lesions

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 130 (actual)
• Sponsor: Huazhong University of Science and Technology · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

We aim to develop an EUS-AI model which can facilitate clinical diagnosis by analyzing EUS pictures and clinical parameters of patients.

Detailed description

EUS is considered to be a more sensitive modality than CT in detecting pancreatic solid lesions due to its high spatial resolution. However, the diagnostic performance is largely dependent on the experience and the technical abilities of the practitioners. Therefore, we aim to develop an objective EUS diagnostic model based on the convolutional neural network, an artificial intelligence technique. In addition, clinical parameters such as risk factors, tumor biomarkers and radiology findings are also added to this artificial intelligence model in order to mimic the actual clinical diagnosis procedures and to increase the performance of this model.

Conditions

• Pancreatic Ductal Adenocarcinoma
• Pancreatitis, Chronic
• Pancreatic Neuroendocrine Tumor
• Autoimmune Pancreatitis

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	EUS-AI model	The test subset (approximately 20% of total patients) is reserved for the final evaluation of the EUS-AI model. Clinical parameters and EUS pictures of each patient in the test subset will be inputed into the trained EUS-AI model, and the most possible diagnosis will be given by the model.

Timeline

• Start date: 2022-07-01
• Primary completion: 2023-06-30
• Completion: 2024-01-24
• First posted: 2022-07-27
• Last updated: 2024-04-03

Locations

1 site across 1 country: China