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AI Assisted the Diagnosis of Pancreatic Solid Lesions

NCT ID: NCT05706415

Last Updated: 2023-01-31

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-01-21

Study Completion Date

2023-02-21

Brief Summary

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Solid lesions of the pancreas mainly include tumor and non tumor lesions. More than 90% of pancreatic tumor lesions are pancreatic cancer, which is characterized by high mortality and poor prognosis and requires surgical treatment; Non-tumor lesions of the pancreas are mainly inflammatory lesions, which usually do not require surgical treatment, but can be treated with drugs. The common ones are chronic pancreatitis and autoimmune pancreatitis, with a good prognosis. Clinically, the differential diagnosis between them is very difficult. Multi-disciplinary diagnosis and treatment of MDT makes our understanding of pancreatic diseases increasingly rich and in-depth. From disease diagnosis to preoperative evaluation and curative effect evaluation, non-invasive imaging involves almost every link under MDT mode. In view of this, improving the differential diagnosis of pancreatic solid space-occupying lesions on imaging will be more conducive to the diagnosis and treatment under MDT mode, so new technologies such as artificial intelligence should be considered. Our goal is to develop a clinically applicable artificial intelligence system, which uses multiple modes to simulate the routine clinical workflow and assist in the diagnosis of benign and malignant pancreatic solid space-occupying lesions.

Detailed Description

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The diagnosis of solid pancreatic lesions is challenging, MDT is a very effective method, but it has a certain misdiagnosis rate. This is a multi-center, prospective and observational clinical study. Our goal is to develop a clinically applicable artificial intelligence system. On the one hand, our artificial intelligence based on clinical data+CT imaging images can assist MDT doctors to diagnose the nature of pancreatic space-occupying lesions and reduce misdiagnosis; On the other hand, if a patient needs EUS-FNA puncture, the multimodal artificial intelligence system based on clinical data+CT+EUS developed by us can help MDT doctors understand the nature of pancreatic space-occupying lesions and reduce the probability of misdiagnosis or secondary puncture.

Conditions

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AI Assist in the Diagnosis of Pancreatic Solid Lesions

Study Design

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Observational Model Type

CASE_CROSSOVER

Study Time Perspective

PROSPECTIVE

Study Groups

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patients with solid lesions of pancreas

Clinicians will review the suggestions of a hypothetical AI

Intervention Type DIAGNOSTIC_TEST

There is no intervention. Clinicians will review the suggestions of a hypothetical AI

Interventions

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Clinicians will review the suggestions of a hypothetical AI

There is no intervention. Clinicians will review the suggestions of a hypothetical AI

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* pancreatic solid mass in CT and EUS

Exclusion Criteria

* insufficient imaging quality of CT or EUS
* endoscopic ultrasound non accessible lesions
Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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The Third People's Hospital of Chengdu

OTHER

Sponsor Role collaborator

Ruijin Hospital

OTHER

Sponsor Role collaborator

Sun Yat-sen University

OTHER

Sponsor Role collaborator

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

OTHER

Sponsor Role collaborator

Changhai Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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CT and EUS

Shanghai, Shanghai Municipality, China

Site Status RECRUITING

CT and EUS

Shanghai, Shanghai Municipality, China

Site Status RECRUITING

Countries

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China

Facility Contacts

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Zhaoshen Li

Role: primary

Zhaoshen Li

Role: primary

Other Identifiers

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2022-AI and MDT

Identifier Type: -

Identifier Source: org_study_id