Introduction: Heart failure with preserved ejection fraction (HFpEF) arises from diverse comorbidities and progresses through prolonged subclinical stages, making early diagnosis and prognosis difficult. Current echocardiography-based Artificial Intelligence (AI) models focus primarily on binary HFpEF detection in humans and do not provide comorbidity-specific phenotyping or temporal estimates of disease progression towards decompensation. We aimed to develop a unified AI framework, CardioMOD-Net, to perform multiclass diagnosis and continuous prediction of HFpEF onset directly from standard echocardiography cine loops in preclinical models. Methods: Mouse echocardiography videos from four groups were used: control (CTL), hyperglycaemic (HG), obesity (OB), and systemic arterial hypertension (SAH). Two-dimensional parasternal long-axis cine loops were decomposed using Higher Order Dynamic Mode Decomposition (HODMD) to extract temporal features for downstream analysis. A shared latent representation supported Vision Transformers, one for a classifier for diagnosis and another for a regression module for predicting the age at HFpEF onset. Results: Overall diagnostic accuracy across the four groups was 65%, with all classes exceeding 50% accuracy. Misclassifications primarily reflected early-stage overlap between OB or SAH and CTL. The prognostic module achieved a root-mean-square error of 21.72 weeks for time-to-HFpEF prediction, with OB and SAH showing the most accurate estimates. Predicted HFpEF onset closely matched true distributions in all groups. Discussion: This unified framework demonstrates that multiclass phenotyping and continuous HFpEF onset prediction can be obtained from a single cine loop, even under small-data conditions. The approach offers a foundation for integrating diagnostic and prognostic modelling in preclinical HFpEF research.

翻译：引言：射血分数保留的心力衰竭（HFpEF）源于多种合并症，并经历漫长的亚临床阶段进展，使得早期诊断和预后评估十分困难。目前基于超声心动图的人工智能（AI）模型主要集中于人类的二元HFpEF检测，无法提供针对特定合并症的表型分型或疾病向失代偿进展的时间估计。我们的目标是开发一个统一的AI框架——CardioMOD-Net，以在临床前模型中直接利用标准超声心动图电影循环进行多类别诊断和HFpEF发病的连续预测。方法：使用了来自四组小鼠的超声心动图视频：对照组（CTL）、高血糖组（HG）、肥胖组（OB）和系统性动脉高血压组（SAH）。采用高阶动态模态分解（HODMD）对二维胸骨旁长轴电影循环进行分解，以提取用于下游分析的时间特征。一个共享的潜在表征支持两个Vision Transformer：一个用于诊断的分类器，另一个用于预测HFpEF发病年龄的回归模块。结果：四组间的总体诊断准确率为65%，所有类别的准确率均超过50%。误分类主要反映了OB或SAH与CTL在早期阶段的重叠。预后模块在预测HFpEF发病时间上实现了21.72周的均方根误差，其中OB和SAH组的预测最为准确。预测的HFpEF发病时间在所有组中均与真实分布高度吻合。讨论：这一统一框架表明，即使在数据量有限的条件下，也能从单一电影循环中获得多类别表型分型和连续的HFpEF发病预测。该方法为在临床前HFpEF研究中整合诊断和预后建模奠定了基础。