SBIR Phase I: Development of a Machine Learning System to Identify Streptococcal Pharyngitis with a Smartphone Image

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力解决了远程医疗缺乏即时，远程医疗测试的问题。该项目可以为链球菌性咽喉炎开发一个准确的基于机器学习的预测模型。该商业模式提供基于人工智能（AI）的临床决策支持系统作为紧急护理远程医疗服务的软件即服务订阅。在紧急护理和初级护理中，所有远程医疗点护理测试（不包括链球菌性咽喉炎）的总目标市场为104亿美元。这种解决方案影响了抗生素的过度处方和卫生服务的经济性。目前，34%的儿童和75%的成人咽炎患者接受了不必要的抗生素治疗，而远程医疗的情况要糟糕10-21%。链球菌性咽喉炎的远程护理预测可以潜在地减少2200万美元/年的不必要抗生素成本，并减少耐药细菌的驱动因素。与急诊室或紧急护理设施相比，通过远程医疗治疗咽炎，每次临床就诊可为患者节省1-3小时，每次就诊可为医疗保险公司节省高达100 -400美元。这个小型企业创新研究（SBIR）第一阶段项目通过结合智能手机图像分析和深度学习，推进机器学习领域。这些策略被应用于数字健康的一个新用例，作为临床决策支持的远程筛查。技术挑战是开发一种预测模型，以达到临床采用可接受的灵敏度和特异性，目标为80%（类似于快速抗原链球菌检测）。应对这一挑战的策略是增加数据集的大小，并使用多个预测模型进行实验，直到达到目标性能。该项目还将包括设计一个验证系统，验证由未经培训的患者记录的足够图像，并创建一个直观的用户界面，使患者能够一致记录。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project addresses the lack of instant, remote medical tests for telehealth. This project could develop an accurate machine learning-based predictive model for strep throat. The business model delivers an artificial intelligence (AI)-based clinical decision support system as a Software as a Service subscription to urgent care telehealth services. The total addressable market for all telehealth point of care tests (beyond strep throat) in urgent care and primary care is $10.4 billion. This solution impacts antibiotic overprescribing and economics of health services. Currently, 34% of children and 75% of adults with pharyngitis receive unnecessary antibiotics, and this is 10-21% worse with telehealth. A remote point of care prediction for strep throat can potentially reduce the $22 million/year costs in unnecessary antibiotics and reduce drivers for drug-resistant bacteria. When pharyngitis is treated on telehealth it saves patients up to 1-3 hours per clinical visit and saves health insurance companies up to $100-400 per visit, compared to an emergency room or urgent care facility.This Small Business Innovation Research (SBIR) Phase I project advances the field of machine learning by combining smartphone image analysis and deep learning. These strategies are applied to a novel use case in digital health as remote screening for clinical decision support. The technical challenge is the development of a predictive model to achieve sensitivity and specificity acceptable for clinical adoption, at a target of 80% (similar to the rapid antigen strep test). The strategy to meet this challenge is to increase the size of the dataset and experiment with multiple prediction models until goal performance is achieved. The project will also include designing an authentication system that validates sufficient images as recorded by an untrained patient and creating an intuitive user interface that enables consistent recordings by patients.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.