The development of intelligent blood coagulation diagnoses is awaited to meet the current need for large clinical time-sensitive caseloads due to its efficient and automated diagnoses. Herein, a method is reported and validated to realize it through artificial intelligence (AI)-assisted optical clotting biophysics (OCB) properties identification. The image differential calculation is used for precise acquisition of OCB properties with elimination of initial differences, and the strategy of space-time regulation allows on-demand space time OCB properties identification and enables diverse blood function diagnoses. The integrated applications of smartphones and cloud computing offer a user-friendly automated analysis for accurate and convenient diagnoses. The prospective assays of clinical cases (n = 41) show that the system realizes 97.6%, 95.1%, and 100% accuracy for coagulation factors, fibrinogen function, and comprehensive blood coagulation diagnoses, respectively. This method should enable more low-cost and convenient diagnoses and provide a path for potential diagnostic-markers finding.

由于其高效和自动化的诊断，智能凝血诊断的发展有望满足当前对大型临床时间敏感病例的需求。在此，报告并验证了一种通过人工智能（AI）辅助光学凝血生物物理学（OCB）特性识别来实现它的方法。图像差分计算用于精确获取OCB特性，消除初始差异，时空调节策略实现按需时空OCB特性识别，实现多样化的血液功能诊断。智能手机和云计算的集成应用程序提供了用户友好的自动化分析，以实现准确和方便的诊断。临床病例（n = 41）的前瞻性分析表明，该系统实现了 97.6%、95.1%、凝血因子、纤维蛋白原功能和综合凝血诊断的准确率分别为 100%。这种方法应该能够实现更低成本和更方便的诊断，并为潜在的诊断标志物的发现提供途径。