Computer-assisted diagnosis of ear pathologies by combining digital otoscopy with complementary data using machine learning

通过使用机器学习将数字耳镜与补充数据相结合来计算机辅助诊断耳部病变

• 批准号: 10564534
• 负责人: Metin Nafi Gurcan
• 金额: $ 65.88万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-05 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10564534
• 关键词: Academy; Acute; Adverse event; Algorithms; American; Antibiotics; Awareness; Bacterial Antibiotic Resistance; Child; Childhood; Clinical; Clinical Data; Clinical Decision Support Systems; Clip; Computer Assisted; Computer software; Computer-Assisted Diagnosis; Computer-Assisted Image Analysis; Data; Data Sources; Decision Support Systems; Devices; Diagnosis; Diagnostic; Ear; Ear Diseases; Emergency Medicine; Financial Hardship; Foundations; Goals; Guidelines; Health; Health Care Costs; Human; Image; Image Analysis; Individual; Insufflation; Interobserver Variability; Label; Language Development; Machine Learning; Medical; Methods; Mission; Movement; National Institute on Deafness and Other Communication Disorders; Nature; Nose; Nurse Practitioners; Operative Surgical Procedures; Oral; Otitis Media; Otitis Media with Effusion; Otoscopes; Otoscopy; Pathologist; Pathology; Pediatrics; Performance; Pharmaceutical Preparations; Pharyngeal structure; Physician Assistants; Physicians; Primary Care; Primary Care Physician; Provider; Public Health; Reader; Recommendation; Research; Side; Support System; System; Techniques; Technology; Testing; Training; Tube; Tympanic membrane; Tympanometry; United States National Institutes of Health; Visual; Work; accurate diagnosis; clinical examination; cognitive development; deep learning; diagnostic accuracy; digital; ear infection; effectiveness evaluation; effusion; emergency settings; experience; hearing impairment; improved; large datasets; machine learning algorithm; middle ear fluid; novel; novel strategies; overtreatment; personalized therapeutic; primary care clinician; prototype; radiologist; recurrent infection; software development; tool

ABSTRACT Diseases of the ear, particularly acute otitis media (AOM) and middle ear effusions, are the most commonly treated childhood pathologies. The financial burden of ear disease is estimated at more than $3.2 billion per year. Because ear diseases are common, a significant problem is over-diagnosis and over-treatment, due to two factors. First, the subjective nature of diagnosing ear disease - based on a brief glimpse of the eardrum with an otoscope - makes an accurate diagnosis difficult, even for experienced primary care, emergency medicine, or ear, nose, and throat (ENT) physicians. Second, with a growing shortage of primary care physicians in the US, more Advanced Practice Providers (Nurse Practitioners and Physician Assistants) serve as first-line clinicians in primary care and emergency settings but lack extensive training in otoscopy (i.e., clinical examination of the eardrum). Consequently, clinicians often err on the side of making a diagnosis of ear infection and prescribing oral antibiotics. Over 8 million unnecessary antibiotics are prescribed annually, contributing to the rise of antibiotic-resistant bacteria and creating the largest number of pediatric medication-related adverse events. Children with inaccurate ear diagnoses are often referred to ENTs for surgical placement of ear tubes for recurrent infections, and up to 70% of these cases are not indicated. Diagnosing ear pathologies still depends on clinician subjectivity, based on a brief glimpse of the eardrum. This diagnostic subjectivity creates a critical barrier to decreasing healthcare costs and reducing over-diagnosis and over-treatment of ear disease. Devices are needed to assist in a more accurate, consistent, and objective diagnosis of ear pathology. Our previous work laid the foundation to develop machine-learning approaches to provide an objective approach to ear diagnosis using digital otoscopy computer-assisted image analysis. This project will dramatically expand on our previous work with the overarching goal of developing new machine learning applications to analyze eardrum videos collected with a digital otoscope, which will be combined with tympanometry, demographic, and clinical data, to achieve diagnostic objectivity. The long-term goal is to improve clinicians’ diagnostic accuracy for ear diseases, using novel computer-assisted approaches. To accomplish these goals, we propose three Specific Aims: Specific Aim 1 will refine an objective computer-assisted image analysis (CAIA) software to differentiate multiple eardrum abnormalities. Specific Aim 2 will develop an otoscopy clinical decision support system by combining CAIA with additional data sources, including tympanometry, demographic, and clinical information. Specific Aim 3 will determine how the otoscopy clinical decision support system improves clinicians’ diagnostic performance.

抽象的 耳部疾病，特别是急性中耳炎 (AOM) 和中耳积液，是最常见的疾病 据估计，每年因耳部疾病治疗造成的经济负担超过 32 亿美元。 由于耳部疾病很常见，一个显着的问题是过度诊断和过度治疗，原因有二。 首先，诊断耳病的主观性质——基于对耳膜的简短观察。 耳镜 - 即使对于经验丰富的初级保健、急诊医学或 其次，随着美国初级保健医生的日益短缺， 更多 高级实践提供者（执业护士和医师助理）作为一线人群 初级保健和急诊机构，但缺乏耳镜检查培训（即对耳廓进行广泛的临床检查） 鼓膜）。 每年开出超过 800 万种不必要的抗生素，导致了这种情况的增加。 抗生素耐药细菌并造成最多数量的儿科药物相关不良事件。 耳部诊断不准确的儿童通常会被转介至耳鼻喉科进行耳管置入手术 复发性感染，其中高达 70% 的病例无法诊断耳部病变。 临床医生的主观性，基于对耳膜的短暂了解，这种诊断主观性创造了一个关键的问题。 降低医疗成本和减少耳病设备的过度诊断和过度治疗的障碍。 需要帮助我们对耳部病理学进行更准确、一致和客观的诊断。 为开发机器学习方法奠定了基础，为耳部诊断提供客观的方法 使用数字耳镜计算机辅助图像分析该项目将极大地扩展我们之前的项目。 致力于开发新的机器学习应用程序来分析耳膜视频的总体目标 通过数字耳镜收集，并将其与鼓室导抗测试、人口统计和临床数据相结合，以 实现诊断客观性，长期目标是提高上级对耳部疾病的诊断准确性， 为了实现这些目标，我们提出了三个具体目标： 具体目标 1 将完善客观的计算机辅助图像分析 (CAIA) 软件，以区分 具体目标 2 将开发耳镜检查临床决策支持。 系统将 CAIA 与其他数据源相结合，包括鼓室导抗测量、人口统计和 具体目标 3 将确定耳镜检查临床决策支持系统的方式。 提高灯具的诊断性能。