STTR Phase I: A Machine Learning Framework for Comprehensive Dental Caries Detection

STTR 第一阶段：用于全面龋齿检测的机器学习框架

• 批准号: 2013846
• 负责人: Daniel Lee
• 金额: $ 22.5万
• 依托单位: DENTUIT, 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: 2020-07-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2013846&HistoricalAwards=false
• 关键词: STTR; Phase; Machine; Learning; Framework

The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project will be the development of an artificial intelligence software solution that enables automated detection of dental cavities in digital X-rays. Routine misdiagnosis of dental cavities (tooth decay) is a global challenge; cavities alone account for over 5% of healthcare costs in developed countries, with dental care focused on repairing rather than preventing tooth decay. This project will develop an add-on solution for software already in use by 200,000 dentists nationally. The technology resulting from this project will allow non-expert assistants to automate the triaging, screening, and tracking of patients, increasing access to oral care for underserved communities nationally and throughout the world.This Small Business Technology Transfer (STTR) Phase I project will demonstrate the feasibility of two key innovations: (1) a novel software framework using an innovative neural network algorithm for the detection of cavities in X-rays, and (2) the world’s largest database of dental radiographs annotated by specialists in oral radiology. The goals of R&D are to achieve high sensitivity and specificity in cavity detection and to ensure consistent high-quality annotations. Outcomes include: (1) achieving state-of-the-art performance in cavity detection, (2) outperforming domain experts in detecting all stages of cavities, and (3) enabling professionals and non-experts alike to interpret pathologies using a visual heatmap of prediction confidence. The proposed technology features an innovative neural network structure for learning visual representations of dental radiographs that jointly characterize the data while highlighting their most salient attributes. Using a new and original training procedure, the technology will maximize the benefit of existing unlabeled data. Technical challenges include scaling performance while maintaining a minimal false-negative rate, establishing interoperability under various calibration settings, and achieving the desired level of results on the types of machines used by customers with reasonable resource costs.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.

这个小企业技术转让（STTR）第一阶段项目的更广泛/商业影响将是开发一种人工智能软件解决方案，可以在数字X射线中自动检测蛀牙。蛀牙（蛀牙）的常规误诊是一个全球性挑战;在发达国家，仅蛀牙就占医疗保健费用的5%以上，牙科护理的重点是修复而不是预防蛀牙。该项目将为全国200，000名牙医已使用的软件开发附加解决方案。该项目所产生的技术将使非专家助理能够自动化对患者进行分诊、筛查和跟踪，从而增加全国和全世界服务不足社区获得口腔护理的机会。该小企业技术转让（STTR）第一阶段项目将展示两项关键创新的可行性：（1）一种新的软件框架，使用创新的神经网络算法检测X射线中的空洞，以及（2）世界上最大的口腔放射学专家注释的牙科X射线照片数据库。研发的目标是在腔检测中实现高灵敏度和特异性，并确保一致的高质量注释。成果包括：（1）在空洞检测中实现最先进的性能，（2）在检测所有阶段的空洞方面优于领域专家，以及（3）使专业人员和非专家都能够使用预测置信度的视觉热图来解释病理。所提出的技术具有创新的神经网络结构，用于学习牙科X射线照片的视觉表示，这些图像共同表征数据，同时突出显示其最显著的属性。使用新的原始训练程序，该技术将最大限度地发挥现有未标记数据的优势。技术挑战包括在保持最低假阴性率的同时扩展性能，在各种校准设置下建立互操作性，以及在客户使用的机器类型上以合理的资源成本实现所需的结果水平。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。