AI-OCT系统比聊天机器人和新手医生更准地指导心脏支架手术决策。
COMPARE: Clinical Optimization with Modular Planning and Assessment via RAG-Enhanced AI-OCT: Superior Decision Support for Percutaneous Coronary Intervention Compared to ChatGPT-5 and Junior Operators
- 用专用于血管成像的AI模型分析OCT图像做手术规划
- 在术前规划中准确率超聊天机器人30%以上，比新手医生高20%
- 特别适合复杂病例，能帮医生减少误判
背景：光学相干断层扫描（OCT）可提升经皮冠状动脉介入治疗（PCI）效果，但其解读依赖操作者。通用人工智能虽有潜力，但缺乏领域可靠性。本文评估了部署于AI-OCT系统的新型大模型CA-GPT，在OCT引导的PCI规划与评估中，相较于通用模型ChatGPT-5及初级医生的表现。方法：在单中心96例接受OCT引导PCI的患者中，比较CA-GPT、ChatGPT-5及初级医生生成的术式决策与专家记录的一致性，采用10项预设指标评估术前与术后阶段。结果：术前规划中，CA-GPT中位一致分达5（IQR 3.75–5），显著高于ChatGPT-5（3[2–4]，P<0.001）和初级医生（4[3–4]，P<0.001）。CA-GPT在所有术前指标上均优于ChatGPT-5，且在支架直径选择（90.3% vs. 72.2%，P<0.05）和长度选择（80.6% vs. 52.8%，P<0.01）上优于初级医生。术后评估中，CA-GPT整体一致性为5（4.75–5），显著高于ChatGPT-5（4[4–5]，P<0.001）和初级医生（5[4–5]，P<0.05）。亚组分析显示其在复杂情况中优势更明显。结论：基于CA-GPT的AI-OCT系统在术前规划与术后评估中均显著优于通用大模型和初级医生，提供标准化、可靠的血管内成像解读方案，具显著潜力辅助临床决策，优化OCT引导的PCI。
原文摘要 · Abstract (English)
Background: While intravascular imaging, particularly optical coherence tomography (OCT), improves percutaneous coronary intervention (PCI) outcomes, its interpretation is operator-dependent. General-purpose artificial intelligence (AI) shows promise but lacks domain-specific reliability. We evaluated the performance of CA-GPT, a novel large model deployed on an AI-OCT system, against that of the general-purpose ChatGPT-5 and junior physicians for OCT-guided PCI planning and assessment. Methods: In this single-center analysis of 96 patients who underwent OCT-guided PCI, the procedural decisions generated by the CA-GPT, ChatGPT-5, and junior physicians were compared with an expert-derived procedural record. Agreement was assessed using ten pre-specified metrics across pre-PCI and post-PCI phases. Results: For pre-PCI planning, CA-GPT demonstrated significantly higher median agreement scores (5[IQR 3.75-5]) compared to both ChatGPT-5 (3[2-4], P<0.001) and junior physicians (4[3-4], P<0.001). CA-GPT significantly outperformed ChatGPT-5 across all individual pre-PCI metrics and showed superior performance to junior physicians in stent diameter (90.3% vs. 72.2%, P<0.05) and length selection (80.6% vs. 52.8%, P<0.01). In post-PCI assessment, CA-GPT maintained excellent overall agreement (5[4.75-5]), significantly higher than both ChatGPT-5 (4[4-5], P<0.001) and junior physicians (5[4-5], P<0.05). Subgroup analysis confirmed CA-GPT's robust performance advantage in complex scenarios. Conclusion: The CA-GPT-based AI-OCT system achieved superior decision-making agreement versus a general-purpose large language model and junior physicians across both PCI planning and assessment phases. This approach provides a standardized and reliable method for intravascular imaging interpretation, demonstrating significant potential to augment operator expertise and optimize OCT-guided PCI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。