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Development and Validation of an AI Foundation Model for Frozen-Section Pathology

NCT07708207 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 33000

Last updated 2026-07-16

No results posted yet for this study

Summary

This multicenter observational study aims to develop and validate an artificial intelligence foundation model for frozen-section pathology.

The study includes a retrospective phase and a prospective validation phase. Retrospective frozen-section pathology data will be used for model development, internal validation, and external validation. A prospective multicenter cohort of patients undergoing intraoperative frozen-section examination will then be enrolled to evaluate the model in a real-world clinical setting.

The model will analyze digitized frozen-section whole-slide images and will be evaluated for prespecified frozen-section pathology diagnostic tasks across multiple organ systems. Its performance will be assessed using pathological reference standards. The primary outcome is the area under the receiver operating characteristic curve. Secondary outcomes include accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.

This study is observational and will not require research-mandated changes to routine clinical care.

Conditions

  • Cancer
  • Intraoperative Pathology
  • Artificial Intelligence (AI)

Sponsors & Collaborators

  • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-04-27
Primary Completion
2026-10-01
Completion
2026-12-01

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07708207 on ClinicalTrials.gov