An end-to-end multifunctional AI platform for intraoperative diagnosis
Intraoperative frozen section diagnosis provides essential, real-time histological insights to guide surgical decisions. However, the quality of these time-sensitive sections is often suboptimal, posing significant diagnostic challenges for pathologists. To address these limitations, we utilized over 6700 whole slide images to develop GAS, a comprehensive platform comprising three modules: Generation, Assessment, and Support modules. The Generation module, based on a GAN-driven multimodal network guided by FFPE-style text descriptions, demonstrated effective enhancement of frozen section quality across various organs. The Assessment module, which fine-tuned quality control models using pathological foundation models, showed substantial improvements in microstructural quality for the generated images. Validated through a prospective study (ChiCTR2300076555) on the human-AI collaboration software, the Support module demonstrated that GAS significantly boosted diagnostic confidence for pathologists. In summary, this study highlights the clinical utility of the GAS platform in intraoperative diagnosis and establishes a new paradigm for integrating end-to-end AI solutions into clinical workflows.
Authors
- Muyan Cai (ORCID: https://orcid.org/0000-0002-4646-4391)
- Jia Lin (ORCID: https://orcid.org/0000-0003-4396-3571)
- Yubo Cai
- Liwen Jiang
- Xiaoxia Cai (ORCID: https://orcid.org/0000-0003-0865-7958)
- Xiaobo Wen (ORCID: https://orcid.org/0000-0001-9244-6269)
- Chao Zheng (ORCID: https://orcid.org/0000-0002-7349-262X)
- Jin‐Ling Duan (ORCID: https://orcid.org/0000-0002-8297-3003)
- Dan Xie (ORCID: https://orcid.org/0000-0003-2242-3138)
- Jie-Wei Chen
- Jiangyu Zhang (ORCID: https://orcid.org/0000-0003-1714-3684)
- Wei Yuan (ORCID: https://orcid.org/0000-0002-3158-3486)
- Xueyi Zheng
- Ke Zheng (ORCID: https://orcid.org/0000-0002-4558-6039)
- Zihan Zhao (ORCID: https://orcid.org/0000-0002-5871-8440)
- Xinke Zhang (ORCID: https://orcid.org/0000-0002-3527-2140)
- Yu Wen (ORCID: https://orcid.org/0000-0002-0288-0684)
- Xiaomei Li (ORCID: https://orcid.org/0000-0003-2845-9545)
- Jiajia Meng (ORCID: https://orcid.org/0000-0002-1001-3417)
Institutions
- Sun Yat-sen University (CN)
- ShenZhen People’s Hospital (CN)
- Huizhou Central People's Hospital (CN)
- Sun Yat-sen University Cancer Center (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-07-20
- DOI
- https://doi.org/10.1038/s41746-025-01808-7
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- Sun Yat-sen University
- China Postdoctoral Science Foundation
- Sun Yat-sen University Cancer Center
- Basic and Applied Basic Research Foundation of Guangdong Province