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.

项目总结