INTRODUCTION Heterogeneous MRI manifestations restrict the efficiency and consistency of neuroradiologists in diagnosing hypoxic-ischemic encephalopathy (HIE) due to complex injury patterns. This study aimed to develop and validate an intelligent HIE identification model (termed as DLCRN, deep learning clinical-radiomics nomogram) based on conventional structural MRI and clinical characteristics. METHODS In this retrospective case-control study, full-term neonates with HIE and healthy controls were collected in two different medical centers from January 2015 to December 2020. Multivariable logistic regression analysis was implemented to establish the DLCRN model based on conventional MRI sequences and clinical characteristics. Discrimination, calibration, and clinical applicability were used to evaluate the model in the training and validation cohorts. Grad-class activation map algorithm was implemented to visualize the DLCRN. RESULTS 186 HIE patients and 219 healthy controls were assigned to the training, internal validation, and independent validation cohorts. Birthweight was incorporated with deep radiomics signatures to create the final DLCRN model. The DLCRN model achieved better discriminatory power than simple radiomics models, with an area under the curve (AUC) of 0.868, 0.813, and 0.798 in the training, internal validation, and independent validation cohorts, respectively. The DLCRN model was well calibrated and has clinical potential. Visualization of the DLCRN highlighted the lesion areas that conformed to radiological identification. CONCLUSION Visualized DLCRN may be a useful tool in the objective and quantitative identification of HIE. Scientific application of the optimized DLCRN model may save time for screening early mild HIE, improve the consistency of HIE diagnosis, and guide timely clinical management.

中文翻译：

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使用基于 MRI 的深度学习模型对缺氧缺血性脑病进行图形智能诊断。

引言 由于复杂的损伤模式，不同的 MRI 表现限制了神经放射科医生诊断缺氧缺血性脑病 (HIE) 的效率和一致性。本研究旨在开发和验证基于常规结构 MRI 和临床特征的智能 HIE 识别模型（称为 DLCRN，深度学习临床放射组学列线图）。方法 在这项回顾性病例对照研究中，收集2015年1月至2020年12月两个不同医疗中心的HIE足月新生儿和健康对照者。采用多变量logistic回归分析，建立基于常规MRI序列和临床数据的DLCRN模型。特征。区分度、校准和临床适用性用于评估训练和验证队列中的模型。实施 Grad 级激活图算法来可视化 DLCRN。结果 186 名 HIE 患者和 219 名健康对照者被分配到训练组、内部验证组和独立验证组。出生体重与深度放射组学特征相结合，创建了最终的 DLCRN 模型。DLCRN 模型比简单的放射组学模型具有更好的区分能力，在训练组、内部验证组和独立验证组中的曲线下面积 (AUC) 分别为 0.868、0.813 和 0.798。DLCRN模型经过良好校准，具有临床潜力。DLCRN 的可视化突出显示了符合放射学识别的病变区域。结论 可视化 DLCRN 可能是客观、定量识别 HIE 的有用工具。科学应用优化后的DLCRN模型，可以节省早期轻度HIE的筛查时间，提高HIE诊断的一致性，指导临床及时治疗。