Medical Diagnosis using Raman Spectrographs and Machine Learning

MedMira是快速垂直流（RVF）技术的开发商和所有者。该公司基于裂谷热技术的快速测试应用程序为医院、实验室、诊所和个人提供即时诊断疾病，如艾滋病毒和丙型肝炎，只需三个简单的步骤。与加拿大二级研究主席，博士。Medmira的Christa Brosseau根据代血样本的拉曼光谱图中最陡的峰和之前的波谷确定了规则。应用这些规则进行数据分析，可以准确地区分阳性样本和阴性样本。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；最多半小时。拟议的项目计划自动化数据处理，并探索使用机器学习来自动生成由专家化学家确定的规则的可能性。机器学习方法对于将测试从实验室生成的替代样本扩展到复杂的人类血液样本尤为重要。与实验室生成的替代样本不同，人类血液样本的拉曼光谱仪可以因人而异。异质血液样本可能有额外的波峰和波谷，这可能使解释复杂化。更复杂的机器学习技术更有可能复制专家促进的诊断过程。该项目的产出将使MedMira能够启动工具的开发，使非专家能够在不牺牲质量的情况下在非传统卫生保健环境中获得诊断结果。

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.