Develop an Artificial Intelligence-powered Smartphone App AICaries for Caries Detection in Children

开发人工智能驱动的智能手机应用程序 AICaries，用于儿童龋齿检测

• 批准号: 10331877
• 负责人: Kevin Fiscella
• 金额: $ 23.1万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10331877
• 关键词: Address; Advisory Committees; Algorithms; American; Archives; Artificial Intelligence; Caregivers; Caries prevention; Cellular Phone; Child; Childhood; Chronic; Chronic Disease; Clinical; Clinical Trials; Coupled; Data; Data Analyses; Databases; Dental; Dental Care; Dental Clinics; Dental Hygiene; Dental caries; Dentists; Detection; Development; Diabetes Mellitus; Diagnosis; Diet; Disease; Early Diagnosis; Education; Family; Family Caregiver; Feedback; Future; Goals; Health; Healthcare; Healthcare Systems; Image; Individual; Knowledge; Life Style; Long-Term Effects; Low income; Methods; Minority; Modification; Monitor; Oral; Oral health; Parents; Patient Education; Patients; Performance; Population; Positioning Attribute; Preschool Child; Prevention; Prevention strategy; Preventive; Procedures; Process; Quality of life; Reporting; Research; Risk; Risk Assessment; Risk Factors; Sensitivity and Specificity; Severities; System; Technology; Testing; Tooth structure; Training; Underserved Population; Work; acceptability and feasibility; community based participatory research; community engagement; computer science; computerized; detection sensitivity; early childhood; education resources; empowered; field study; health literacy; improved; indexing; innovation; lower income families; mHealth; microbial; multidisciplinary; novel; pandemic disease; performance tests; population based; prototype; recruit; restorative treatment; satisfaction; skills; smartphone Application; success; tool; usability

Project Summary Early childhood caries (ECC) is the most common chronic childhood disease, with nearly 1.8 billion new cases per year globally. ECC afflicts approximately 55% of low-income and minority US preschool children, resulting in harmful short- and long-term effects on health and quality of life. The current biomedical approach to control the ECC pandemic has had limited success. It primarily focuses on restorative procedures rather than population-wide preventive strategies. Clinical evidence shows that caries is reversible if detected and addressed in its early stages. However, many low-income US children often have poor access to pediatric dental services. In this underserved group, dental caries is often diagnosed at a late stage when extensive restorative treatment is needed. We believe that with more than 85% of lower-income Americans owning a smartphone, mHealth tools hold great promise to achieve patient-driven early detection and risk control of ECC. Our long-term goal is to develop strategies that use mHealth tools to achieve early detection and prevention of ECC at a broad population base. Our previous innovative work has led to a novel prototype of an artificial intelligence (AI) -powered smartphone app, AICaries, to be used by children's parents/caregivers. This AICaries app prototype offers a) AI-powered caries detection using photos of children's teeth taken by the parents' smartphones, b) interactive caries risk assessment, and c) personalized education on reducing children's ECC risk. The preliminary AI- powered caries detection module demonstrated a satisfactory sensitivity and specificity for front teeth caries detection, using 6,895 annotated tooth images from 1,277 photos. We have recently built an archive of > 100,000 high-quality intra-oral photos that is ready to be used for finalizing the development of a reliable automatic detection algorithm. The immediate objectives of the study are - AIM 1: complete the development of AICaries smartphone app, maximize its caries detection performance, and achieve a caries detection sensitivity and specificity that are comparable to trained dental practitioners; AIM 2: employ a community-based participatory research strategy to conduct moderated testing and refinement of the app usability, and non-moderated field testing of the app feasibility/acceptability. Our multidisciplinary team is well-positioned for proposal success with needed expertise in computer science, AI imaging recognition, oral health care, mHealth, disparity research, patient education and community engagement. The AICaries app could facilitate early detection of ECC for many underserved US children, who often have poor access to pediatric dental services. Using AICaries, parents can use their regular smartphones to take photo of their children’s teeth and detect ECC aided by AICaries, so that they can actively seek treatment for their children at an early and reversible stage of ECC. Using AICaries, parents can also obtain essential knowledge on reducing their children's caries risk. Data from this R21 will support a R01 clinical trial that evaluates the real-world impact of using this innovative smartphone app on early detection and prevention of ECC among low-income children.

项目摘要 幼儿汽车（ECC）是最常见的慢性儿童疾病，近18亿例新病例 全球每年。 ECC遭受了约55％的低收入和少数族裔美国学龄前儿童 在有害的短期和长期对健康和生活质量的影响中。当前的生物医学方法 ECC大流行的成功有限。它主要关注修复程序，而不是 范围内的预防策略。临床证据表明，如果检测到并解决了携带 在早期阶段。但是，许多低收入的美国儿童通常无法获得儿科牙科服务。 在这一服务不足的组中，龋齿经常在晚期进行广泛的恢复治疗时被诊断 需要。我们认为，有超过85％的低收入美国人拥有智能手机，MHealth工具 保持巨大的希望，以实现患者驱动的早期检测和对ECC的风险控制。我们的长期目标是 制定使用MHealth工具在广泛人群中实现ECC的早期检测和预防的策略 根据。我们以前的创新作品导致了具有人工智能（AI）能力的新型原型 智能手机应用程序Aicaries将由孩子的父母/护理人员使用。这个AICARIES应用程序提供了a） 使用父母智能手机拍摄的儿童牙齿的照片，AI驱动的汽车检测，b）互动 Caries风险评估以及C）有关降低儿童ECC风险的个性化教育。初步的ai- 动力汽车检测模块表现出满意的工厂对前牙齿汽车的敏感性和特异性 检测，使用1,277张照片中的6,895张带注释的牙齿图像。我们最近建造了一个> 100,000的档案 高质量的口腔内照片，准备用于最终确定可靠自动的开发 检测算法。研究的直接目的是 - 目标1：完成审核的发展 智能手机应用程序，最大化其携带检测性能，并实现检测灵敏度和 与训练有素的牙科医生相当的特异性；目标2：员工以社区为基础的参与 研究策略，以进行适度的应用程序可用性和不调制字段的测试和完善 测试应用程序可行性/可接受性。我们的多学科团队井有供提议 在计算机科学，AI成像识别，口腔保健，MHealth，MHealth中所需的专业知识的成功， 差异研究，患者教育和社区参与。 AICARIES应用程序可以提早促进 为许多服务不足的美国儿童检测ECC​​，他们通常无法使用小儿牙齿 服务。使用AICARIES，父母可以使用常规智能手机拍照，并 检测ECC​​在AICARIS的帮助下，以便他们可以在早期且可逆 ECC的阶段。使用AICARIES，父母还可以获得有关减少孩子汽车的基本知识 风险。 R21的数据将支持一项R01临床试验，该试验评估使用这种创新的现实影响 低收入儿童中智能手机应用程序早期检测和预防ECC。

项目成果

Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test

人工智能驱动的智能手机应用程序 AICaries 改善了儿童家庭龋齿筛查：有节制和无节制的可用性测试

• DOI: 10.1371/journal.pdig.0000046
• 发表时间: 2022
• 期刊: PLOS Digital Health
• 作者: Al-Jallad, Nisreen;Ly-Mapes, Oriana;Hao, Peirong;Ruan, Jinlong;Ramesh, Ashwin;Luo, Jiebo;Wu, Tong Tong;Dye, Timothy;Rashwan, Noha;Ren, Johana
• 通讯作者: Ren, Johana