Autoimmune Pancreatitis, Pancreatic Ductal Adenocarcinoma, Pancreatic Neuroendocrine Tumor, Pancreatitis, Chronic
Conditions
Keywords
artificial intelligence, endoscopic ultrasound
Brief 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.
Interventions
DIAGNOSTIC_TESTEUS-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.
Sponsors
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
LanZhou University
Huazhong University of Science and Technology
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion | After the training process of the EUS-AI model is completed | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET | After the training process of the EUS-AI model is completed | 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. |
Countries
China
Outcome results
None listed