Medical Diagnosis using Raman Spectrographs and Machine Learning

MedMira是快速垂直流动(RVF)技术的开发商和所有者。该公司基于RVF技术的快速检测应用程序只需三个简单的步骤即可为医院、实验室、诊所和个人提供艾滋病毒和丙型肝炎等疾病的即时诊断。MedMira与Tier II加拿大研究主席Christa Brosseau博士一起，根据代孕血液样本拉曼光谱分析中最尖锐的峰和之前的低谷确定了规则。应用这些规则进行数据分析，可以准确地区分正样本和负样本。这样的分析需要有经验的化学家来处理拉曼光谱仪的结果；长达半个小时。拟议的项目计划使数据处理自动化，并探索使用机器学习自动生成专家化学家确定的规则的可能性。机器学习方法对于将检测从实验室产生的代理样本扩展到复杂的人类血液样本尤为重要。与实验室产生的替代样本不同，人类血液样本的拉曼光谱图可能因人而异。不均匀的血液样本可能有额外的峰和谷，这可能会使解释变得复杂。更复杂的机器学习技术更有可能复制专家推动的诊断过程。该项目的成果将使MedMira能够启动开发工具，使非专家能够在不牺牲质量的情况下获得非传统保健环境中的诊断结果。

MedMira is the developer and owner of Rapid Vertical Flow (RVF) Technology. The Company's rapid testapplications built on RVF Technology provide hospitals, labs, clinics and individuals with instant diagnosis fordiseases such as HIV and hepatitis C in just three easy steps. Together with a Tier II Canada Research Chair,Dr. Christa Brosseau, Medmira has identified rules based on sharpest peaks and preceding troughs in Ramanspectrographs of surrogate blood samples. Data analysis via application of these rules can accuratelydistinguish positive samples from negative samples. Such an analysis requires an experienced chemist toprocess the results from a Raman spectrometer; up to half an hour. The proposed project plans to automate thedata processing and explore the possibility of using machine learning to automatically generate the rules thatwere identified by the expert chemists. The machine learning approach is especially important for extendingtest from laboratory generated surrogate samples to complex human blood samples. Unlike the laboratorygenerated surrogate samples, Raman spectrographs of human blood samples can vary significantly from personto person. The heterogeneous blood samples may have additional peaks and troughs which could complicateinterpretation. More sophisticated machine learning techniques are more likely to replicate the diagnosticprocess facilitated by experts. The outputs of the project will enable MedMira to initiate development of toolsallowing non-experts to obtain diagnostics results in non-traditional health care settings without sacrificingquality.