开发一种用于从传统心血管磁共振（CMR）图像中诊断心脏病的自动化、可靠且具有临床意义的人工智能（AI）工具
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images
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中文总结 AI 辅助
研究旨在开发用于CMR图像心脏病诊断的AI工具，通过整合开源LLMs和预处理多模态数据，对三种视觉基础模型两阶段微调，在独立测试集上有高诊断性能，集成策略进一步提升准确性和鲁棒性，代码和模型权重公开。
中文摘要 AI 辅助
目的：心血管磁共振（CMR）成像可对心肌结构、功能和病理进行无创评估，但解读CMR图像需要丰富经验，人工智能（AI）模型可提供支持。然而，用于增强CMR解读的AI模型受数据处理繁琐、模型性能欠佳及实施途径不明等限制。方法与结果：我们开发了用于基于CMR的心血管疾病（CVD）诊断的自动化数据处理流程，整合开源本地运行的大语言模型（LLMs）从CMR叙述报告中提取诊断标签并预处理多模态成像数据，包括电影和延迟钆增强（LGE）CMR序列。通过两阶段方法对三种视觉基础模型（DINO、VST、UMedPT）进行微调。数据集包括肥厚型心肌病（HCM）、扩张型心肌病（DCM）、缺血性心肌病（ICM）、心脏淀粉样变性（CA）和正常对照（NOR）。988个整理病例随机分为742个用于训练，246个用于验证。微调后的AI模型在包含1067名患者的独立测试集上实现了高判别诊断性能，正确诊断HCM的个体AUC-ROC值高达0.937，心脏淀粉样变性为0.945。结合多种模型和模态的集成策略进一步提高了基于AI的诊断准确性和鲁棒性，HCM（AUC=0.959，CI [0.936-0.978]）、CA（AUC=0.966，CI [0.939-0.986]）、NOR（AUC=0.872，CI [0.852-0.894]）、DCM（AUC=0.848，CI [0.808-0.885]）和ICM（AUC=0.840，CI [0.809-0.868]）达到了最高总体诊断性能。所有训练和推理代码以及训练后的模型权重可在该https URL上公开获取。
英文摘要
Aims: Cardiovascular magnetic resonance (CMR) imaging enables non-invasive assessment of myocardial structure, function, and pathology, but requires substantial experience in interpretation of CMR images that could be supported by artificial intelligence (AI)-based models. However, use of AI models for enhanced CMR reading is limited by labor-intensive data curation, suboptimal model performance, and unclear implementation pathways. Methods and results: We developed an automated data curation pipeline for CMR-based cardiovascular disease (CVD) diagnosis, integrating open-source locally-run large language models (LLMs) to extract diagnostic labels from narrative CMR reports and preprocessing multimodal imaging data, including cine and late-gadolinium-enhancement (LGE) CMR sequences. Three vision foundation models (DINO, VST, UMedPT) were fine-tuned across these modalities in a two-stage approach. The dataset comprised hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), cardiac amyloidosis (CA), and normal controls (NOR). A total of 988 curated cases were randomly divided into 742 for training and 246 for validation. Fine-tuned AI-models achieved high discriminative diagnostic performance on an independent test set comprising 1067 patients , with individual AUC-ROC values of up to 0.937 for the correct diagnosis of HCM and 0.945 for cardiac amyloidosis. Ensemble strategies combining multiple models and modalities further improved AI-based diagnostic accuracy and robustness, achieving the highest overall diagnostic performance for HCM (AUC=0.959, CI [0.936-0.978]), CA (AUC=0.966, CI [0.939-0.986]), NOR (AUC=0.872, CI [0.852-0.894]), DCM (AUC=0.848, CI [0.808-0.885]) and ICM (AUC=0.840, CI [0.809-0.868]). All training and inference code, along with the trained model weights, are publicly available on https://github.com/sinaamirrajab/CMR_CVD.
