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<p>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.</p>

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An end-to-end multifunctional AI platform for intraoperative diagnosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月20日

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  • Xueyi Zheng,
  • Ke Zheng,
  • Yongqin Wen,
  • Jiajia Meng,
  • Xinke Zhang,
  • Xiaobo Wen,
  • Zihan Zhao,
  • Chengyou Zheng,
  • Xiaoxia Cai,
  • Jiliang Lin,
  • Jiewei Chen,
  • Jinling Duan,
  • Liwen Jiang,
  • Wei Yuan,
  • Xiaomei Li,
  • Dan Xie,
  • Yubo Cai,
  • Jiangyu Zhang,
  • Muyan Cai

摘要

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.