Auto-Scope Software-Automated Otoscopy to Diagnose Ear Pathology

Auto-Scope 软件 - 用于诊断耳部病理的自动耳镜检查

• 批准号: 9790958
• 负责人: Metin Nafi Gurcan
• 金额: $ 19.91万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-21 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9790958
• 关键词: Academy; Acute; Address; Adverse event; Affect; Algorithms; American; Antibiotics; Appearance; Awareness; Bacterial Antibiotic Resistance; Child; Childhood; Cholesteatoma; Clinic; Clinical; Clip; Computer Vision Systems; Computer software; Computer-Assisted Image Analysis; Computers; Cyst; Databases; Devices; Diagnosis; Diagnostic; Disease; Ear; Ear Diseases; Financial Hardship; Goals; Guidelines; Hair; Hand; Health; Health Care Costs; Human; Image; Image Analysis; Image Enhancement; Interobserver Variability; Label; Language Delays; Language Development; Lighting; Liquid substance; Machine Learning; Methods; Mission; National Institute on Deafness and Other Communication Disorders; Nose; Nurse Practitioners; Operative Surgical Procedures; Oral; Otitis Media; Otitis Media with Effusion; Otolaryngologist; Otoscopes; Otoscopy; Pathology; Patients; Pediatrics; Perforation; Performance; Pharmaceutical Preparations; Pharyngeal structure; Physician Assistants; Physicians; Primary Care Physician; Primary Health Care; Public Health; Radiology Specialty; Reporting; Research; Resolution; Retrieval; Side; Skin; Societies; Surgical Pathology; System; Testing; Training; Tube; Tympanic membrane; United States National Institutes of Health; Waxes; Work; accurate diagnosis; acute infection; base; central database; clinical decision support; cognitive development; computerized; diagnostic accuracy; digital imaging; digital video recording; effusion; experience; hearing impairment; improved; middle ear; novel; novel strategies; overtreatment; personalized therapeutic; primary care setting; prototype; software development

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.

摘要 急性中耳感染(急性中耳炎-AOM)是儿童最常用的治疗方法 疾病。对并发症和对儿童认知和语言的影响的担忧推动了治疗 发展。非物质文化遗产造成的财政负担估计每年超过50亿美元。因为AOM是如此的 常见的一个主要社会问题是对这种疾病的过度诊断和过度治疗，其结果是两种 因素：首先，准确诊断AOM很困难，即使是有经验的初级保健或耳朵、鼻子和喉咙也是如此 (Et)医生。其次，随着美国初级保健医生的日益短缺，更多的护士 从业者和医生助理在初级保健环境中担任一线临床医生，但缺乏广泛的 接受耳镜检查培训(即鼓膜临床检查)。因此，实践者经常会犯错。 诊断出AOM并开出口服抗生素。超过800万种不必要的抗生素 每年一次的处方，导致了抗药性细菌的增加，并创造了最多的 儿科用药相关不良事件。许多诊断不准确的AOM儿童被问及 这些病例中，有高达70%的病例没有指征。 AOM的诊断仍然依赖于临床医生的主观性，基于对鼓膜的短暂一瞥。这 诊断的主观性给社会降低医疗费用的目标造成了严重的障碍 减少AOM的过度诊断和过度治疗。根据美国儿科学会于年 2013年，需要设备来帮助更准确、一致和客观地诊断AOM。一个简单的 分析患者耳朵图像以诊断或排除AOM的客观方法将极大地 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。 通过分析使用以下方法收集的鼓膜图像向临床医生提供客观信息的软件 当前可用的硬件。基于前人在应用类似方法提高临床医生水平方面的工作 在放射学和外科病理学方面的表现，我们的首要假设是增量 实施增强图像、自动识别异常情况和检索类似病例 将提高临床医生诊断的准确性。 在我们的前期工作中，我们开发了一款名为Auto-Scope的软件，它将耳膜标记为“正常”与 “不正常。”在本研究中，我们提出了提高诊断性能的两个具体目标： 具体目标1：创建一个增强的鼓膜合成图像。 具体目标2：使用机器学习方法为临床决策提供支持。