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

摘要 耳部疾病，特别是急性中耳炎(AOM)和中耳积液，是最常见的 治疗儿童时期的病症。耳疾造成的经济负担估计超过32亿美元。 年。由于耳朵疾病很常见，一个重要的问题是过度诊断和过度治疗，这是由于两种 各种因素。首先，诊断耳病的主观性质--基于对耳膜的短暂一瞥 耳镜-使准确的诊断变得困难，即使是有经验的初级保健、急救或 耳鼻喉(耳鼻喉科)医生。其次，随着美国初级保健医生的日益短缺， 更多的高级实践提供者(执业护士和医生助理)在 初级保健和急救环境，但缺乏广泛的耳镜检查培训(即，临床检查 耳膜)。因此，临床医生经常错误地诊断耳朵感染并开出处方。 口服抗生素。每年超过800万种不必要的抗生素被开出，这导致了 抗生素耐药细菌和造成最多的儿科用药相关不良事件。 耳朵诊断不准确的儿童经常被转到耳鼻喉科进行手术放置耳管，以治疗 反复感染，其中高达70%的病例没有指征。诊断耳部疾病仍取决于 关于临床医生的主观性，基于对鼓膜的简要一瞥。这种诊断的主观性创造了一个关键的 这是降低医疗费用、减少过度诊断和过度治疗耳部疾病的障碍。设备 以协助更准确、一致和客观的耳部病理诊断。我们之前的工作 为开发机器学习方法，为耳朵诊断提供客观方法奠定了基础 采用数字耳镜计算机辅助图像分析。这个项目将极大地扩展我们以前的 与开发新的机器学习应用程序以分析耳膜视频的总体目标一起工作 用数字耳镜收集，结合鼓室导纳、人口学和临床数据，以 实现诊断的客观性。长期目标是提高临床医生对耳部疾病的诊断准确率， 使用新的计算机辅助方法。为了实现这些目标，我们提出了三个具体目标： 特殊目标1将完善客观的计算机辅助图像分析(CAIA)软件，以区分 多发性鼓膜异常。特指2将开发耳镜临床决策支持系统 系统通过将CAIA与其他数据源相结合，包括鼓室导纳、人口统计和 临床信息。具体目标3将决定耳镜临床决策支持系统如何 提高临床医生的诊断能力。

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.