Dx Ear: An automated tool for diagnosis of otitis media

Dx Ear：诊断中耳炎的自动化工具

• 批准号: 7908336
• 负责人: JELENA KOVACEVIC
• 金额: $ 17.28万
• 依托单位: BLUE BELT TECHNOLOGIES, INC.
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2011-10-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7908336
• 关键词: Acute; Age; Algorithms; Antibiotics; Area; Arts; Categories; Child; Child Care; Classification; Clinical; Clinical Research; Clinical Trials; Computer software; Consensus; Data Set; Diagnosis; Diagnostic; Direct Costs; Ear; Facilities and Administrative Costs; Goals; Image; Image Analysis; Infection; Inflammation; Liquid substance; Machine Learning; Methods; Otitis Media; Otitis Media with Effusion; Otoscopes; Phase; Physicians; Predictive Value; Process; Schools; Signs and Symptoms; Site; Software Tools; Solutions; Sterility; System; Techniques; Time; Training; Tympanic membrane; Work; antimicrobial; antimicrobial drug; bacterial resistance; clinical care; diagnostic accuracy; digital; digital imaging; ear infection; effusion; experience; image processing; improved; middle ear; multidisciplinary; point of care; programs; public health relevance; skills; social; superinfection; tool; user-friendly

DESCRIPTION (provided by applicant): Otitis media is a general term for middle-ear inflammation that is classified clinically as either acute otitis media (AOM) or otitis media with effusion (OME). AOM represents a bacterial super infection of the middle ear fluid and OME a sterile effusion that tends to subside spontaneously. Antibiotics are generally beneficial only for AOM. Accurate diagnosis of AOM, as well as distinction from both OME and no effusion (NOE) requires considerable training. AOM is the most common infection for which antimicrobial agents are prescribed for children in the US. By age seven, 93 percent of children will have experienced one or more episodes of otitis media.1 AOM results in significant social burden and indirect costs due to time lost from school and work. Estimated direct costs of AOM in 1995 were $1.96 billion and indirect costs were estimated to be $1.02 billion, with a total of 20 million prescriptions for antimicrobials related to otitis media.2 Given these considerations, our goal is to: Develop a software tool to classify images into one of three stringent clinical diagnostic categories (AOM/OME/NOE), and validate the algorithm on tympanic membrane (TM) images. We have assembled a strong multidisciplinary team that can successfully develop an automated diagnostic algorithm in this Phase-I program. We have (1) gathered a team of nationally-recognized otoscopists with substantial clinical and research experience in the context of AOM clinical trials; (2) studied the predictive value of diagnostic findings in discriminating AOM from OME from NOE; (3) acquired a large number of TM images from children; and (4) involved an internationally recognized expert in developing algorithms in all areas of image analysis and processing. In the planned Phase-II, we will use the algorithm developed in the Phase-I program and incorporate it into a user-friendly and marketable digital otoscope-software platform that can be used at the point-of-care by clinicians to improve the care of children with this frequently occurring condition. This will be followed by a clinical trial evaluating its immediate impact on clinical care, and, in particular, utilization of antimicrobials. Our main goal will be to develop an accurate automated algorithm for classifying the three diagnostic categories (AOM/OME/NOE). We aim to achieve an overall accuracy of 95 percent by applying a newly developed classification algorithm. This will include applying state-of-the-art classification methods as well as segmentation algorithms, for automated, robust diagnosis and classification of the three diagnostic categories (AOM/OME/NOE). We propose to achieve this through the following two specific aims: Specific Aim 1: Develop a robust and accurate diagnostic algorithm that can discriminate TM digital images into 1of 3 stringent diagnostic categories (AOM/OME/NOE). Specific Aim 2: Validate the algorithm on a dataset that includes over 2000 TM images collected in a recently completed NIAID-sponsored clinical trial. PUBLIC HEALTH RELEVANCE: AOM is the most common infection for which antimicrobial agents are prescribed in children in the US. By age seven, 93 percent of children will have experienced one or more episodes of otitis media. AOM results in significant social burden and indirect costs due to time lost from school and work. Estimated direct costs of AOM in 1995 were $1.96 billion and indirect costs were estimated to be $1.02 billion, with a total of 20 million prescriptions for antimicrobials related to otitis media. Developing an automated and accurate software tool to help classify otitis media images into one of three stringent clinical categories would have a great impact on both clinical care as well as reducing the unnecessary prescriptions of antibiotics in the US.

描述（由申请人提供）： 中耳炎是中耳炎症的总称，临床上分为急性中耳炎（AOM）或渗出性中耳炎（OME）。 AOM 代表中耳液的细菌重复感染，OME 代表无菌积液，往往会自行消退。抗生素通常仅对 AOM 有益。准确诊断 AOM 以及区分 OME 和无积液 (NOE) 需要大量培训。 在美国，AOM 是最常见的感染，需要为儿童使用抗菌药物。到 7 岁时，93% 的儿童都会经历一次或多次中耳炎。1 AOM 会因耽误上学和工作时间而造成严重的社会负担和间接成本。 1995 年 AOM 的直接成本估计为 19.6 亿美元，间接成本估计为 10.2 亿美元，总共有 2000 万张与中耳炎相关的抗菌药物处方。2 鉴于这些考虑，我们的目标是： 开发一个软件工具来分类将图像纳入三个严格的临床诊断类别 (AOM/OME/NOE) 之一，并在鼓膜 (TM) 图像上验证算法。 我们组建了一支强大的多学科团队，可以在此一期项目中成功开发自动诊断算法。我们 (1) 聚集了一支国家认可的耳镜医师团队，在 AOM 临床试验方面拥有丰富的临床和研究经验； (2)研究了诊断结果在区分AOM、OME和NOE方面的预测价值； （3）获取大量儿童的TM图像； (4) 聘请了国际公认的图像分析和处理所有领域的算法开发专家。 在计划的第二阶段中，我们将使用第一阶段项目中开发的算法，并将其纳入用户友好且可销售的数字耳镜软件平台中，临床医生可以在现场使用该平台来改善照顾患有这种常见病症的儿童。随后将进行一项临床试验，评估其对临床护理的直接影响，特别是抗菌药物的使用。 我们的主要目标是开发一种准确的自动化算法来对三个诊断类别（AOM/OME/NOE）进行分类。我们的目标是通过应用新开发的分类算法实现 95% 的总体准确率。这将包括应用最先进的分类方法和分割算法，以实现自动化、稳健的诊断和三个诊断类别（AOM/OME/NOE）的分类。我们建议通过以下两个具体目标来实现这一目标： 具体目标 1：开发一种稳健且准确的诊断算法，可以将 TM 数字图像区分为 3 个严格诊断类别中的一个（AOM/OME/NOE）。 具体目标 2：在数据集上验证算法，该数据集包含最近完成的 NIAID 赞助的临床试验中收集的 2000 多张 TM 图像。 公共卫生相关性： AOM 是美国儿童最常见的感染，需使用抗菌药物治疗。到七岁时，93% 的儿童都会经历过一次或多次中耳炎发作。由于在学校和工作中损失时间，AOM 会造成巨大的社会负担和间接成本。 1995年AOM的直接费用估计为19.6亿美元，间接费用估计为10.2亿美元，总共开出2000万张与中耳炎相关的抗菌药物处方。开发一种自动化且准确的软件工具来帮助将中耳炎图像分类为三个严格的临床类别之一，将对美国的临床护理以及减少不必要的抗生素处方产生巨大影响。

项目成果

OTITIS MEDIA VOCABULARY AND GRAMMAR.

OTITIS 媒体词汇和语法。

• 发表时间: 2012
• 作者: Kuruvilla, Anupama;Li, Jian;Yeomans, Pablo Hennings;Quelhas, Pedro;Shaikh, Nader;Hoberman, Alejandro;Kovačević, Jelena
• 通讯作者: Kovačević, Jelena