SBIR Phase I: Deep learning diagnosis and platform for at-home ear evaluations in children

SBIR 第一阶段：深度学习诊断和儿童家庭耳部评估平台

• 批准号: 2036021
• 负责人: Courtney Hill
• 金额: $ 24.76万
• 依托单位: COHI GROUP L.L.C.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036021&HistoricalAwards=false
• 关键词: SBIR; Phase; Deep; learning; diagnosis

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I Project will improve pediatric health. Pediatric ear infections often manifest at night and the child must wait until the next day for an evaluation and treatment. The intention of this project is to create a deep-learning at-home ear infection diagnostic system. This Small Business Innovation Research (SBIR) Phase I will advance translation of a pediatric ear-imaging system. The objectives include: generating a training data set with images labeled with correct diagnoses from a pediatric clinical setting; creating a deep learning algorithm with transfer learning from well-established convolutional neural networks for prediction; creating a prototype software interface to label a new image presented to the system; and designing a speculum for at-home image acquisition.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的影响/商业潜力将改善儿科健康。 小儿耳部感染通常在夜间出现，儿童必须等到第二天进行评估和治疗。该项目的目的是创建一个深度学习的家庭耳部感染诊断系统。 这个小企业创新研究（SBIR）第一阶段将推进儿科耳成像系统的翻译。这些目标包括：生成训练数据集，该训练数据集具有标记有来自儿科临床环境的正确诊断的图像;创建深度学习算法，该深度学习算法具有来自成熟的卷积神经网络的用于预测的转移学习;创建原型软件界面以标记呈现给系统的新图像;设计一个窥器该奖项反映了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进行评估，被认为值得支持审查标准。