SBIR Phase I: Development of a Novel Diagnostic Test for Pulmonary Embolism Based on Artificial Intelligence and Spectral Analysis of Blood

SBIR 第一阶段：基于人工智能和血液光谱分析开发新型肺栓塞诊断测试

• 批准号: 2014934
• 负责人: Artur Adib
• 金额: $ 20.99万
• 依托单位: BIOCOGNIV INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014934&HistoricalAwards=false
• 关键词: SBIR; Phase; Development; Novel; Diagnostic

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from the development of a fast, non-invasive, and highly accurate test to diagnose pulmonary embolism in the emergency department. In the United States, pulmonary embolism (PE) affects up to 1 million patients per year and is responsible for nearly 100,000 yearly deaths. Its diagnosis is challenging due to the presentation of nonspecific symptoms and the lack of high-accuracy screening methods. While the current standard of care is to rule out PE with an established blood test (D-Dimer), approximately 90% of those results are false positives, causing the test to be used with restraint in the clinic, and leading to both the underdiagnosis of the disease and the overuse of strongly radiative imaging methods like CT pulmonary angiograms. A new, highly specific test for PE could increase patient safety, standardize clinical care processes, reduce costs and save lives.This Small Business Innovation Research (SBIR) Phase I project will develop and validate a new diagnostic tool for PE based on the combination of fast blood spectroscopy and modern machine learning (ML) algorithms. A key aim of the research is demonstrating that ML combined with blood spectroscopy can substantially outperform the D-Dimer biomarker test, which has notoriously low specificity (~40%). An important Phase I milestone will be to show that the specificity of the resulting PE test either (a) already surpasses that of the D-Dimer test when trained on the relatively small dataset used in this Phase I proposal, or (b) substantially increases with the size of the training dataset, so that the test can outperform D-Dimer simply by procuring a larger pool of blood samples. The technical challenges addressed in this phase include evaluating different spectroscopic methods and modalities, minimizing the coefficient of variation for spectra acquisition, as well as designing and optimizing ML models for one-dimensional spectral data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响将来自于开发一种快速，非侵入性和高度准确的测试来诊断急诊室的肺栓塞。在美国，肺栓塞（PE）每年影响多达100万患者，每年造成近10万人死亡。其诊断是具有挑战性的，由于非特异性症状的表现和缺乏高准确性的筛查方法。虽然目前的护理标准是通过既定的血液检查（D-二聚体）排除肺气肿，但其中大约90%的结果是假阳性，导致该测试在临床上的使用受到限制，并导致对该疾病的诊断不足和过度使用强辐射成像方法，如CT肺血管造影。一种新的、高度特异性的PE检测方法可以提高患者安全性、标准化临床护理流程、降低成本并挽救生命。这个小型企业创新研究（SBIR）第一阶段项目将开发和验证一种基于快速血液光谱和现代机器学习（ML）算法相结合的新型PE诊断工具。该研究的一个关键目的是证明ML与血液光谱学相结合可以大大优于D-二聚体生物标志物测试，后者的特异性非常低（约40%）。 一个重要的I期里程碑将表明，所得PE检测的特异性（a）在本I期提案中使用的相对较小的数据集上进行训练时已经超过D-二聚体检测的特异性，或（B）随着训练数据集的大小而显著增加，因此该检测可以通过简单地获取更大的血液样本池来优于D-二聚体。该阶段的技术挑战包括评估不同的光谱方法和模式，最小化光谱采集的变异系数，以及设计和优化一维光谱数据的ML模型。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。