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.