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

小型企业创新研究(SBIR)第一阶段项目的广泛影响/商业潜力解决了远程医疗缺乏即时、远程医疗测试的问题。该项目可以开发一个准确的基于机器学习的链球菌咽喉预测模型。该业务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，所有远程医疗保健点检测(链球菌性咽喉炎以外)的总目标市场为104亿美元。这一解决方案影响了抗生素的过量使用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，而远程医疗的情况更糟。链球菌性咽喉炎的远程护理预测可能会减少每年2200万美元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，远程医疗治疗咽炎可以为患者每次临床就诊节省1-3个小时，为医疗保险公司每次就诊节省100-400美元。这个小型企业创新研究(SBIR)第一阶段项目将智能手机图像分析和深度学习相结合，推动了机器学习领域的发展。这些策略被应用于数字健康中的一个新用例，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，以达到临床可接受的灵敏度和特异度，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，并试验多个预测模型，直到达到目标性能。该项目还将包括设计一种验证系统，以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，使患者能够进行一致的记录。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 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.