超越显微镜:人工智能和光共聚焦数字成像在儿科外科病理学
Donatella Di Fabrizio1, Gloria Daziani2, Ilir Qose3
1Pediatric Surgery Unit, Salesi Children's Hospital, Polytechnic University of Marche, 60123 Ancona, Italy.
Children (Basel, Switzerland)
|December 30, 2025
概括
大型语言模型 (LLM) 在儿科数字病理学中表现有前途,以光共聚焦显微镜 (FCM) 图像帮助快速诊断. 添加免疫组织化学 (IHC) 数据可显著提高复杂病例的诊断准确性.
科学领域:
- 病理学 病理学 病理学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 数字病理学 (DP) 和光共聚焦显微镜 (FCM) 能够快速进行样本保存组织评估.
- 人工智能和LLM在儿科外科病理诊断中的应用仍然在很大程度上未被探索.
- 这项研究研究了LLMs在手术内儿科外科病理学中的实用性.
研究的目的:
- 通过使用ex vivo FCM图像在儿科外科病理学中评估两种商用LLM (GPT-4V和Claude 3.7 Sonnet) 的诊断性能.
- 评估免疫组织化学 (IHC) 数据对LLM诊断准确性的影响.
- 探索人工智能辅助工作流程在增强儿科外科病理诊断方面的潜力.
主要方法:
- 这是一项针对20例儿科手术病例的前性单中心研究,使用了手术内外活体FCM成像.
- 两个LLM,AnPathology-Gpt (GPT-4V) 和AnPathology Project (Claude 3.7 Sonnet),被要求生成具有和没有IHC数据的诊断报告.
- 研究结果与儿科病理学家的黄金标准诊断进行了比较,评估了准确性,敏感性,特异性和科恩的卡帕.
主要成果:
- 在所有病例中,AnPathology-Gpt获得了85%的准确性 (κ=0.78),AnPathology Project获得了80%的准确性 (κ=0.63).
- 两种模型都显示了对瘤病例的100%灵敏度,主要在非瘤病变中出现错误.
- 在5个案例的子集中,将IHC数据纳入两个模型的准确性从40%提高到80%,敏感性从50%提高到100%.
结论:
- 多模式LLM可以支持儿科数字病理学中的准确和快速诊断.
- 整合IHC数据可以提高在诊断上具有挑战性的儿科外科病理病例中的LLM性能.
- 需要进一步的多中心研究来验证这些发现,并确定AI在儿科外科病理学工作流程中的作用.
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