Diagnostic investigation has an important role in risk stratification and clinical decision making of patients with suspected and documented Coronary Artery Disease (CAD). However, the majority of existing tools are primarily focused on the selection of gatekeeper tests, whereas only a handful of systems contain information regarding the downstream testing or treatment. We propose a multi-task deep learning model to support risk stratification and down-stream test selection for patients undergoing Coronary Computed Tomography Angiography (CCTA). The analysis included 14,021 patients who underwent CCTA between 2006 and 2017. Our novel multitask deep learning framework extends the state-of-the art Perceiver model to deal with real-world CCTA report data. Our model achieved an Area Under the receiver operating characteristic Curve (AUC) of 0.76 in CAD risk stratification, and 0.72 AUC in predicting downstream tests. Our proposed deep learning model can accurately estimate the likelihood of CAD and provide recommended downstream tests based on prior CCTA data. In clinical practice, the utilization of such an approach could bring a paradigm shift in risk stratification and downstream management. Despite significant progress using deep learning models for tabular data, they do not outperform gradient boosting decision trees, and further research is required in this area. However, neural networks appear to benefit more readily from multi-task learning than tree-based models. This could offset the shortcomings of using single task learning approach when working with tabular data.

翻译：诊断检查在疑似或确诊冠状动脉疾病（CAD）患者的风险分层与临床决策中具有重要作用。然而，现有工具大多侧重于筛查试验的选择，仅有少数系统包含后续检查或治疗的相关信息。我们提出一种多任务深度学习模型，用于支持接受冠状动脉计算机断层扫描血管造影（CCTA）患者的风险分层与后续检查选择。研究纳入2006年至2017年间接受CCTA的14,021名患者。我们创新的多任务深度学习框架扩展了当前最先进的Perceiver模型，以处理真实世界的CCTA报告数据。该模型在CAD风险分层方面受试者工作特征曲线下面积（AUC）达0.76，在预测后续检查方面AUC达0.72。所提出的深度学习模型可基于既往CCTA数据准确估计CAD发生概率，并提供推荐的后续检查方案。在临床实践中，采用该方法可能推动风险分层与后续管理的范式转变。尽管基于深度学习模型的表格数据研究取得显著进展，但其性能仍未能超越梯度提升决策树，该领域仍需进一步探索。不过，神经网络相较于树模型更易从多任务学习中获益，这有望弥补单一任务学习方法在处理表格数据时的不足。