时空调控智能凝血诊断成像分析仪。

Space-time-regulated imaging analyzer for smart coagulation diagnosis.

机构信息

Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Renmin Hospital, Wuhan University, Wuhan 430060, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China.

Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China.

出版信息

Cell Rep Med. 2022 Oct 18;3(10):100765. doi: 10.1016/j.xcrm.2022.100765. Epub 2022 Oct 6.

DOI:10.1016/j.xcrm.2022.100765

PMID:36206751

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9589004/

Abstract

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.

摘要

智能凝血诊断的发展有望满足当前对大量临床时间敏感病例的需求，因为它可以实现高效和自动化诊断。在此，报告并验证了一种通过人工智能 (AI) 辅助的光学凝血生物物理学 (OCB) 特性识别来实现这一目标的方法。图像差分计算用于精确获取 OCB 特性，同时消除初始差异，时空调节策略允许按需进行时空 OCB 特性识别，并实现多种血液功能诊断。智能手机和云计算的集成应用为准确、方便的诊断提供了用户友好的自动化分析。对 41 例临床病例的前瞻性检测表明，该系统在凝血因子、纤维蛋白原功能和综合凝血诊断方面的准确率分别达到 97.6%、95.1%和 100%。该方法应该能够实现更具成本效益和更方便的诊断，并为潜在的诊断标志物发现提供途径。