采用机器学习方法对缺血性和非缺血性心肌病进行心脏磁共振电影成像鉴别。

OBJECTIVE: This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR). METHODS: This retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM. RESULTS: A total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47-1.00]) and lowest Brier score (0.19, 95% CI [0.13-0.27]), showing competitive diagnostic accuracy and calibration. At the Youden's index, sensitivity was 0.72 (95% CI [0.68-0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients. CONCLUSION: The current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy. CLINICAL RELEVANCE STATEMENT: A machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition. KEY POINTS: • The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols. • Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies. • Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.

目的：本研究旨在利用非对比心血管磁共振（CMR）建立机器学习（ML）模型，对缺血性心肌病（ICM）和非缺血性心肌病（NICM）进行鉴别诊断。

方法：本回顾性研究纳入了 107 例连续患者（49 例 ICM，58 例 NICM）的 CMR 扫描资料，包括心房和心室应变参数。我们使用这些数据比较了可解释树基梯度提升加性模型与四种传统 ML 模型在 ICM 和 NICM 鉴别诊断中的应用。根据判别和校准情况，采用重复交叉验证对模型进行训练和内部验证。此外，我们还研究了区分 ICM 和 NICM 的重要变量。

结果：共纳入 107 例患者和 38 个变量进行分析。其中，49 例为 ICM（34 例男性，平均年龄 60±9 岁），58 例为 NICM（38 例男性，平均年龄 56±19 岁）。经过 10 次 10 折交叉验证，提出的模型获得了最高的曲线下面积（0.82，95%CI [0.47-1.00]）和最低的 Brier 评分（0.19，95%CI [0.13-0.27]），显示出有竞争力的诊断准确性和校准度。在约登指数处，敏感性为 0.72（95%CI [0.68-0.76]），为所有指标中最高。预测分析表明，心房和心室应变 CMR 参数对 ICM 患者的识别均很重要。

结论：本研究表明，使用 ML 模型和非对比 CMR 参数可实现多腔室心肌应变和功能的区分，从而以有竞争力的诊断准确性对 ICM 和 NICM 进行鉴别诊断。

临床相关性声明：基于非对比心血管磁共振参数的机器学习模型可鉴别缺血性和非缺血性心肌病，使更多患者能够接受成本更低、成像采集速度更快的心血管磁共振检查。

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