Dx Ear: An automated tool for diagnosis of otitis media

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代表中耳液的细菌超级感染，而OME是倾向于自发消退的无菌渗出液。抗生素通常仅对AOM有益。AOM的准确诊断以及与OME和无积液（NOE）的区分需要大量的培训。 AOM是美国儿童最常见的抗菌药物感染。到7岁时，93%的儿童将经历一次或多次中耳炎发作。1 AOM会造成重大的社会负担和间接成本，因为它会损失上学和工作的时间。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，与中耳炎有关的抗菌药物处方总数为2000万份。2鉴于这些考虑因素，我们的目标是：开发软件工具，将图像分类为三个严格的临床诊断类别之一（AOM/OME/NOE），并在鼓膜（TM）图像上验证算法。 我们已经组建了一个强大的多学科团队，可以在这个第一阶段计划中成功开发自动诊断算法。我们已经（1）聚集了一个国家认可的耳镜专家团队，在AOM临床试验的背景下具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中的预测价值;（3）获得了大量的儿童TM图像;以及（4）涉及国际公认的专家在图像分析和处理的所有领域开发算法。 在计划的第二阶段，我们将使用在第一阶段项目中开发的算法，并将其整合到用户友好且可销售的数字耳镜软件平台中，该平台可由临床医生在护理点使用，以改善对这种常见疾病儿童的护理。随后将进行临床试验，评估其对临床护理的直接影响，特别是抗菌药物的利用。 我们的主要目标将是开发一个准确的自动化算法，用于分类三个诊断类别（AOM/OME/NOE）。我们的目标是通过应用新开发的分类算法实现95%的整体准确率。这将包括应用最先进的分类方法以及分割算法，以实现三种诊断类别（AOM/OME/NOE）的自动化，稳健的诊断和分类。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：开发一个强大的和准确的诊断算法，可以区分TM数字图像到3个严格的诊断类别（AOM/OME/NOE）。 具体目标二：在一个数据集上验证该算法，该数据集包括在最近完成的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是美国儿童中最常见的感染，抗菌药物是处方药。到7岁时，93%的儿童将经历一次或多次中耳炎发作。AOM造成了巨大的社会负担和间接成本，因为学校和工作的时间损失。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，与中耳炎有关的抗菌剂处方总数为2000万份。开发一种自动化和准确的软件工具，以帮助将中耳炎图像分类为三个严格的临床类别之一，将对临床护理以及减少美国不必要的抗生素处方产生重大影响。

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.