Auto-Scope Software-Automated Otoscopy to Diagnose Ear Pathology

摘要 急性中耳炎（急性中耳炎- AOM）是儿童时期最常治疗的疾病， 疾病治疗是由关注并发症和对儿童的认知和语言的影响推动的 发展AOM的财政负担估计每年超过50亿美元。因为AOM如此 一个常见的主要社会问题是对这种疾病的过度诊断和过度治疗，这是两个原因造成的。 因素：首先，准确诊断AOM是困难的，即使是有经验的初级保健或耳，鼻，喉 (ENT)医生其次，随着美国初级保健医生的日益短缺， 执业医师和医师助理在初级保健环境中担任一线临床医生，但缺乏广泛的 耳镜检查培训（即鼓膜临床检查）。因此，从业者往往在一边犯错 诊断AOM并开口服抗生素的方法超过800万种不必要的抗生素 每年开处方，导致抗药性细菌的增加，并创造了最大数量的 儿科用药相关不良事件。许多AOM诊断不准确的儿童被称为 耳鼻喉科用于耳管手术放置，其中高达70%的病例不适用。 诊断AOM仍然依赖于临床医生的主观性，基于对鼓膜的短暂一瞥。这 诊断的主观性对社会降低医疗费用的目标的进步造成了严重的障碍 减少AOM的过度诊断和过度治疗。根据美国儿科学会在 2013年，需要设备来帮助更准确，一致和客观地诊断AOM。一个简单 分析患者耳朵图像以诊断或排除AOM的客观方法将大大提高 减少过度治疗。该项目将通过开发计算机辅助图像分析（CAIA）来填补这一空白 一种通过分析鼓膜图像向临床医生提供客观信息的软件，所述鼓膜图像是使用 现有硬件。基于以前的工作，在应用类似的方法，以提高临床医生 在放射学和外科病理学方面，我们的总体假设是， 实现增强图像、自动识别异常和检索类似病例 将导致临床医生诊断准确性的提高。 在我们的初步工作中，我们开发了一种名为Auto-Scope的软件，它将鼓膜标记为“正常”， “不正常”在这项研究中，我们提出了两个具体目标，以提高诊断性能： 具体目标#1：创建鼓膜的增强合成图像。 具体目标#2：使用机器学习方法进行临床决策支持。

ABSTRACT Acute infections of the middle ear (acute otitis media - AOM), are the most commonly treated childhood disease. Treatment is fueled by concern for complications and effects on children's cognitive and language development. The financial burden of AOM is estimated at more than $5 billion per year. Because AOM is so common, a major societal problem is the over-diagnosis and over-treatment of this disease, as a result of two factors: First, accurately diagnosing AOM is difficult, even for experienced primary care or ear, nose, and throat (ENT) physicians. Second, with a growing shortage of primary care physicians in the US, more Nurse Practitioners and Physician Assistants serve as first-line clinicians in primary care settings, but lack extensive training in otoscopy (i.e. clinical examination of the eardrum). Consequently, practitioners often err on the side of making a diagnosis of AOM 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. Many children with inaccurate diagnoses of AOM are referred to ENTs for surgical placement of ear tubes, and up to 70% of these cases are not indicated. Diagnosing AOM still depends on clinician subjectivity, based on a brief glimpse of the eardrum. This diagnostic subjectivity creates a critical barrier to progress in society's goal of decreasing healthcare costs and reducing over-diagnosis and over-treatment of AOM. According to the American Academy of Pediatrics in 2013, devices are needed to assist in more accurate, consistent, and objective diagnosis of AOM. A simple and objective method of analyzing an image of a patient's ear to diagnose or rule out AOM would drastically reduce over-treatment. This project will fill that gap, by developing computer-assisted image analysis (CAIA) software that provides objective information to a clinician by analyzing eardrum images collected using currently available hardware. Based on previous work in applying similar methods to improve clinician performance in radiology and surgical pathology, our overarching hypothesis is that the incremental implementation of enhanced images, automated identification of abnormalities, and retrieval of similar cases will result in improved clinician diagnostic accuracy. In our preliminary work, we developed software, called Auto-Scope, which labels eardrums as “normal” versus “abnormal.” In this study, we propose two Specific Aims to improve diagnostic performance: Specific Aim #1: Create an enhanced composite image of the eardrum. Specific Aim #2: Use machine learning approaches for clinical decision support.