Clinical Research Directory

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

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.

The Application of Rapid Fresh Digital Pathology on the Diagnosis and Biomarker Testing of Lung Cancer

Through this project, the rapid staining process will be optimized for ex-vivo human lung specimens and integrate advanced ex-vivo mesoscale nonlinear optical gigascope. Initially, this will provide rapid digital pathological diagnosis during lung cancer surgeries. This will help achieve the dual goals of precise and efficient complete resection of lung cancer tissues while preserving tissue functionality. The application of this technology aims to provide higher-quality medical services for patients suffered from lung cancers.