Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions

Huazhong University of Science and Technology130 enrolled

Overview

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

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.

Study Type

OBSERVATIONAL

Enrollment

130

Conditions

Pancreatic Ductal Adenocarcinoma Pancreatitis, Chronic Pancreatic Neuroendocrine Tumor Autoimmune Pancreatitis

Interventions

EUS-AI modelDIAGNOSTIC_TEST

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.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Patients who underwent EUS using a curved line array echoendoscope (GF-UCT260; Olympus Medical Systems) since 2014 in our affiliation. * For each patient, all available native EUS pictures are included. * Patients' diagnosis are validated by surgical outcomes or fine-needle aspiration (FNA) findings and have a compatible clinical course with a follow-up period of more than 6 months. Exclusion Criteria: * The image is of poor quality. * The images contain unique marks which can potentially bias the model, such as the biopsy needle.

Locations (1)

Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, China

Outcomes

Primary Outcomes

The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion

Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model.

Time frame: After the training process of the EUS-AI model is completed

Secondary Outcomes

The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET

Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model.

Time frame: After the training process of the EUS-AI model is completed

Data from ClinicalTrials.gov

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