基于医学本体的头颅CT报告评估方法，提升生成报告的精准度与可控性。
HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation
- 用领域本体对医学实体和关系进行标准化提取
- 能更好识别语义等价报告，区分正常与异常报告
- 支持按临床重点调整评估权重，适合医生协作验证
我们提出头颅CT本体归一化评估（HeadCT-ONE），一种通过本体归一化实体与关系提取来评估头颅CT报告生成的方法。相比现有信息提取类指标（如RadGraph F1），HeadCT-ONE通过领域本体实现实体归一化，缓解放射学语言多样性问题。该方法比较归一化后的实体与关系，并支持对不同实体类型或特定实体进行可控加权。在三个医疗系统的真实头颅CT报告上进行实验表明，其归一化与加权策略提升了对语义等价报告的捕捉能力，更好地区分正常与异常报告，并与放射科医生对临床显著错误的判断高度一致，同时具备灵活优先关注报告特定内容的能力。结果证明，HeadCT-ONE可实现更灵活、可控且细粒度的头颅CT报告自动化评估。
原文摘要 · Abstract (English)
We present Head CT Ontology Normalized Evaluation (HeadCT-ONE), a metric for evaluating head CT report generation through ontology-normalized entity and relation extraction. HeadCT-ONE enhances current information extraction derived metrics (such as RadGraph F1) by implementing entity normalization through domain-specific ontologies, addressing radiological language variability. HeadCT-ONE compares normalized entities and relations, allowing for controllable weighting of different entity types or specific entities. Through experiments on head CT reports from three health systems, we show that HeadCT-ONE's normalization and weighting approach improves the capture of semantically equivalent reports, better distinguishes between normal and abnormal reports, and aligns with radiologists' assessment of clinically significant errors, while offering flexibility to prioritize specific aspects of report content. Our results demonstrate how HeadCT-ONE enables more flexible, controllable, and granular automated evaluation of head CT reports.
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