Remmie.ai: a deep learning diagnostic assistance engine for ear-nose-throat diseases

Remmie.ai：耳鼻喉疾病深度学习诊断辅助引擎

• 批准号: 10602813
• 负责人: Jane Zhang
• 金额: $ 34.99万
• 依托单位: REMMIE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10602813
• 关键词: Academy; Acute; Address; Adult; Age; Age Years; Algorithms; American; Antibiotic Therapy; Antibiotics; Architecture; Artificial Intelligence platform; Blinded; Caregivers; Caring; Certification; Child; Child Support; Childhood; Classification; Clinic; Clinic Visits; Clinical; Collaborations; Computer software; Country; Coupled; Custom; Data; Data Set; Databases; Development; Devices; Diagnosis; Diagnostic; Disease; Drainage procedure; Ear; Earache; Early Diagnosis; Economic Burden; Ensure; Family; Feedback; Fever; Future; Health Care Costs; Health Insurance Portability and Accountability Act; Healthcare; Healthcare Systems; Home; Image; Income; Infection; Institutional Review Boards; Label; Libraries; Liquid substance; Machine Learning; Malleus; Medicine; Modeling; Monitor; Nose; Otitis Media; Otolaryngologist; Otolaryngology; Otoscopes; Outcome; Patient imaging; Patient observation; Patients; Pediatric Hospitals; Pediatrics; Persons; Pharmaceutical Preparations; Pharyngeal structure; Phase; Physicians; Positioning Attribute; Process; Protocols documentation; Provider; Recommendation; Recurrence; Resource-limited setting; Resources; Secure; Specialist; Structure; Supervision; Surveys; Symptoms; System; Technology; Telemedicine; Testing; Text; Time; Tissues; Training; Tube; Tympanic membrane; Tympanostomy Tube Insertions; Validation; Visit; Visual; Work; accurate diagnosis; age group; artificial intelligence algorithm; burden of illness; care burden; care providers; clinical diagnosis; clinical diagnostics; clinical efficacy; convolutional neural network; cost; deep learning; disability; ear infection; efficacy study; experience; experimental study; hearing impairment; improved; infection management; machine learning algorithm; middle ear; mobile application; neural network algorithm; novel; pediatrician; portability; prevent; recurrent infection; research and development; response; success; symptom management; technological innovation; telehealth; tool; transfer learning; usability; user-friendly; ventilation; virtual visit

PROJECT SUMMARY Otitis media (OM) is experienced by five out of six children before their third birthday, and 30-40% suffer recurring infections, leading to 16 million annual episodes in the US. Ear infections are the primary reason for antibiotic prescription for children under 6 years, are the second most common cause of hearing loss, and can lead to lifelong sequelae. Diagnosis depends upon in-person clinic visits and visual examination by care providers, at great inconvenience to patients and caregivers and at significant cost to the healthcare system, estimated at $4 billion per year. Although the majority of OM cases resolve within a week and symptoms may be managed by over-the-counter medications,10-20% do not, requiring additional antibiotic treatment or, in extreme cases, tympanostomy tube insertion to provide ventilation to the middle ear and aid in fluid drainage. Another compounding factor is limited access to otolaryngologists for accurate diagnosis and infection management. The expansion of telehealth has the potential to address this need with rapid, convenient, and affordable, but to date, there are no platforms to support and facilitate effective virtual visits for OM diagnosis. The first Specific Aim of this Phase I proposal involves building a comprehensive database of several thousand images of eardrums from patients with or without acute OM, with associated clinical diagnostic labels to, in Specific Aim 2, train a novel custom machine learning algorithm, Remmie.ai. A convolutional neural network will be developed to classify images of eardrums paired with text description of symptoms. Image classification will be improved through data augmentation, and the custom Remmie.ai architecture built through transfer learning of a publicly available training model. Unblinded labels will be compared to the algorithm readout as blinded testing data are loaded into Remmie.ai to ensure convergence of accuracy and validation for classification of acute OM versus normal eardrums. In Specific Aim 3, the Remmie.at platform, coupled with a handheld “portable otoscope” for imaging patients’ eardrums and a user-friendly mobile device application, will be tested by end-user physicians to derive feedback on the usability of the device and software. The outcome will be a novel tool for both patients and caregivers to monitor otolaryngic diseases, specifically acute OM, based on patient-provided images and symptoms, and diagnosis, aided by the proprietary Remmie.ai algorithm.

项目摘要 中耳炎（OM）在三分之三生日之前就经历了六个孩子中的五分之一，而30-40％的孩子受苦 经常性感染，在美国导致每年1600万次发作。耳朵感染是产生的主要原因 6岁以下儿童的抗生素处方是第二大最常见的听力损失原因，并且可以 导致终身后遗症。诊断取决于面对面的诊所就诊和视觉检查 提供者给患者和看护人带来极大的不便，并为医疗体系带来了巨大的代价， 估计每年40亿美元。尽管大多数OM病例都在一周内解决，并且症状可能 通过非处方药管理，10-20％不需要额外的抗生素治疗或 极端情况下，鼓膜造口管插入以提供通风的中耳通风并有助于流体排水。 另一个复合因素是有限的访问Otrolyngologists以进行准确的诊断和感染 管理。远程医疗的扩展有可能通过快速，方便，以及 负担得起，但迄今为止，没有平台来支持和促进OM诊断的有效虚拟访问。 该阶段I建议的第一个具体目的是建立数千个的综合数据库 来自或不具有急性OM的患者的耳朵图像，并带有相关的临床诊断标签 特定目标2，训练一种新颖的定制机器学习算法，remmie.ai。卷积神经网络 将开发以对耳朵的图像进行分类，并与符号的文本描述配对。图像分类 将通过扩展和通过转移构建的自定义Remmie.AI架构来改进 学习公开培训模型。不盲目的标签将与算法读数进行比较 盲测数据被加载到remmie.ai中，以确保准确性和验证的收敛性 急性OM与正常耳朵的分类。在特定的AIM 3中，Remmie.AT平台，加上 用于成像患者耳环和用户友好的移动设备应用程序的手持式“便携式耳镜”将 由最终用户医生测试，以获取有关设备和软件的可用性的反馈。结果 对于患者和护理人员，将是监测耳鼻喉疾病，特别是急性OM的新工具 根据专有的Remmie.ai的帮助，基于患者提供的图像和症状以及诊断 算法。