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Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions

H

Huazhong University of Science and Technology

Status

Completed

Conditions

Autoimmune Pancreatitis
Pancreatitis, Chronic
Pancreatic Neuroendocrine Tumor
Pancreatic Ductal Adenocarcinoma

Treatments

Diagnostic Test: EUS-AI model

Study type

Observational

Funder types

Other

Identifiers

NCT05476978
EUS-AI 2022

Details and patient eligibility

About

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

Full 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.

Enrollment

130 patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

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.

Trial design

130 participants in 1 patient group

Pancreas-EUS
Description:
Patients since 2014 with EUS pictures of normal pancreas or pancreatic solid lesions have been included in this cohort.
Treatment:
Diagnostic Test: EUS-AI model

Trial contacts and locations

1

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Data sourced from clinicaltrials.gov

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