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NCT Number: NCT06753318

Validation of Joint-AI in Diagnosing Pancreatic Solid Lesions

This clinical trial aims to learn if a multimodal artificial intelligence (AI) model can enhance the diagnosis of pancreatic solid lesions. The main questions it aims to answer are:

1. Does the AI model enhance the diagnostic performance of endoscopists in diagnosing pancreatic solid lesions? 2. Does the addition of interpretability analysis further improve the diagnostic performance of the assisted endoscopists? Researchers will compare the diagnostic performance of endoscopists with or without the assistance of the AI model.

Participants will:

1. Their clinical data will be prospectively collected. 2. They will be randomized to the AI-assist group and the conventional diagnosis group.

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Key information

Conditions

Pancreatic Cancer Autoimmune Diseases Autoimmune Pancreatitis Chronic Disease Digestive System Diseases Digestive System Neoplasms Disease Attributes Endocrine Gland Neoplasms Endocrine System Diseases Immune System Diseases Neoplasms Neoplasms by Site Pancreatic Diseases Pancreatic Neoplasms Pancreatic Neuroendocine Neoplasms (pNETs) Pancreatitis Pancreatitis, Chronic Pathologic Processes Pathological Conditions, Signs and Symptoms Solid Pseudopapillary Neoplasm of the Pancreas

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

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

Wuhan, Hubei, 430030, China

Location contact

Bin Cheng

CONTACT

[email protected]

86-13986097542

About this study

The investigators have previously developed a multimodal AI model (Joint-AI) based on endoscopic ultrasound images and clinical data to diagnose pancreatic solid lesions. This study aims to improve the Joint-AI model's performance with a prospectively collected dataset and validate it through a randomized controlled clinical trial.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Imaging examinations (MRI, CT, B-ultrasound) show a solid mass in the pancreas, which requires endoscopic ultrasound guided-fine needle aspiration/biopsy (EUS-FNA/B) to clarify the nature of the lesion in patients.
  • Written consent provided

Exclusion criteria

  • Age under 18 years old

Treatment and study plan

The assistance of the Joint-AI model

Diagnostic Test

Predictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis

The assistance of the interpretable Joint-AI model

Diagnostic Test

Predictions given by the Joint-AI model and the results of the interpretability analysis will be provided to the endoscopists during their diagnosis

Primary outcomes

  1. Rate of correct diagnostic classification with assistance of the Joint-AI Model

    Time frame: Through study completion, an average of 1 year

    The rate of correct diagnoses in discriminating pancreatic cancer from other non-cancer lesions, determined by comparing endoscopist diagnosis assisted by the Joint-AI model against the final histopathological diagnosis (reference standard).

  2. Rate of correct diagnostic classification with assistance of the Interpretable Joint-AI Model

    Time frame: Through study completion, an average of 1 year

    The rate of correct diagnoses in discriminating pancreatic cancer from other non-cancer lesions, determined by comparing endoscopist assessments assisted by the Interpretable Joint-AI model against the final histopathological diagnosis (reference standard)

Secondary outcomes

  1. Rate of correct diagnostic classification of the Joint-AI model and the interpretable Joint-AI model

    Time frame: Through study completion, an average of 1 year

    Diagnostic accuracy of the AI models in this prospectively collected dataset.

  2. Endoscopist-reported confidence score in diagnosis with AI assistance (the score is on a scale of 0%-100%, where 0 represents "not confident at all" and 100 represents "completely confident")

    Time frame: Through study completion, an average of 1 year

    Endoscopist-reported confidence in diagnosis will be measured on a scale ranging from 0 to 100, where 0 represents "not confident at all" and 100 represents "completely confident." Higher scores indicate greater diagnostic confidence. The confidence scores will be assessed separately for diagnoses made using the Joint-AI model and the interpretable Joint-AI model.

  3. Rate of correct diagnostic classification of endoscopists without AI assistance

    Time frame: Through study completion, an average of 1 year

Study contacts

Contact information is provided by the study sponsor or research team.

Bin Cheng

CONTACT

[email protected]

86-13986097542

Sponsors and collaborators

Lead sponsor

Huazhong University of Science and Technology

Other

Collaborators

  • Affiliated Drum Tower Hospital of Nanjing University Medical School
  • Beijing Friendship Hospital
  • Beijing Union Hosptial
  • Qilu Hospital of Shandong University
  • Shanghai Longhua Hospital
  • Sir Run Run Shaw Hospital

Registry information

Official study title

Validation of a Multimodal Artificial Intelligence Model in in Diagnosing Pancreatic Solid Lesions: a Prospective, Multicenter, Randomized, Controlled Trial

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Dec 31, 2024
Registry last updated
Dec 31, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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