Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability‐adjusted life‐years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer‐aided diagnosis of stroke is performed using SMF based multi‐modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano‐assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi‐modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single‐modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.

中文翻译：

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基于多模态识别的血清代谢指纹图谱的中风快速计算机辅助诊断

中风是全球死亡和致残的主要原因，预计到 2020 年将导致 6100 万伤残调整生命年。快速诊断是中风早期预防和医疗管理的核心。血清代谢指纹 (SMF) 反映了潜在的疾病进展，可预测患者的表型。使用临床指标编码 SMF 的深度学习 (DL) 优于单一生物标志物，但对通过特征选择进行解释的预测较差提出了挑战。在此，使用基于 SMF 的 DL 多模态识别来进行中风的快速计算机辅助诊断，将自适应机器学习与新颖的特征选择方法相结合。SMF 通过纳米辅助激光解吸/电离质谱 (LDI MS) 提取，几秒钟内消耗 100 nL 血清。与仅通过 SMF 或临床指标进行单模态诊断相比，通过整合 SMF 和临床指标构建多模态识别，用于中风筛查的曲线下面积 (AUC) 高达 0.845。通过显着图方法选择具有差异调节的 20 个关键代谢物特征来预测 DL，从而揭示中风的分子机制。该方法强调了深度学习在精准医学中的新兴作用，并提出了在中风筛查中扩展 SMF 计算分析的实用性。