SBIR Phase I: A Powerful Clinical Aid in the Diagnosis of Vaginitis to Prevent PTB and Stop STI Transmission

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是提供快速，准确的阴道感染诊断。 阴道炎导致每年1000万次办公室访问，占所有美国妇女健康访问的10%，已知会显着增加对性传播感染的易感性。外部测试是昂贵的，可能需要一天到几周的结果。对于目前的办公室测试，40%的酵母菌感染和30%的细菌感染被误诊，需要进一步的访问。 更重要的是，阴道炎可导致很大一部分自发性早产-这是全球新生儿死亡、情绪困扰和终身残疾的主要原因。在美国，PT B导致每年26 B美元的医疗保健成本，这促使快速准确的测试。 该项目将开发一种先进的测试，使用人工智能（AI）在医生办公室快速准确地研究样本。这个小企业创新研究I期项目推进了一种针对临床表现的阴道炎的新型测试。 该项目利用人工智能（AI）图像分析和高质量荧光显微镜，在医生办公室快速扫描标本，以实现快速诊断。该项目将验证初步研究表明，与在显微镜下观察类似样品相比，图像精度更高。该项目的第1阶段将检查来自多达500名女性的不同患者人群的制备样本，以优化VHA自动数字病理学系统的分析和表征。将所有样本与目前临床上使用的手动评估进行比较。此外，在多种类型的临床研究环境中进行用户测试，如医学院和健康网络，将使患者样本人群多样化。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。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide fast, accurate diagnosis of vaginal infections. Vaginitis results in 10 million office visits a year representing 10% of all US women’s health visits, is known to dramatically increase susceptibility to sexually transmitted infections. External testing is expensive and can take from a day to weeks for results. For current in-office tests, 40% of all yeast infections and 30% of bacterial infections are misdiagnosed and require further visits. More importantly, vaginitis can lead to a large fraction of spontaneous preterm births - a major cause of neonatal mortality, emotional distress and lifelong disability worldwide. In the US, PTB results in healthcare costs of $26 B/year, motivating a fast and accurate test. This project will develop an advanced test that uses artificial intelligence (AI) to study samples quickly and accurately in the doctor's office.This Small Business Innovation Research Phase I project advances a novel test for vaginitis upon clinical presentation. This project leverages Artificial Intelligence (AI) image analysis with a high-quality fluorescence microscope to rapidly scan specimens in the doctor's office for fast diagnosis. This project will validate preliminary studies suggesting higher image accuracy compared to viewing similar samples under a microscope. Phase 1 of this project will examine prepared samples from diverse patient populations of up to 500 women to optimize analysis and characterization of VHA automated digital pathology system. All samples will be compared to manual assessment currently used clinically. Additionally, user testing in multiple types of clinical research environments, such as medical schools and health networks, will be undertaken to diversify patient sample populations.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.