Otitis Media Diagnosis and Treatment

项目摘要/摘要 中耳炎(OM)是美国因病就诊的儿童患者中最常见的诊断(1，2)， 90%的儿童会受到影响(3，4)，这是最常见的儿童抗菌治疗和手术适应症(5) 孩子们。尽管许多人试图改进诊断、治疗和预防，但OM仍在继续其高度的 对儿童的普遍影响和持续的大量发病率(1，3，6-30)。OM仍然是最常见的原因 儿童听力损失(HL)，并导致语言、教育和其他发育迟缓(31-37)。奥姆 会导致危及生命的并发症(22，27)，而且代价高昂，每年给美国的健康造成超过50亿美元的损失 护理支出(3，38)。尽管OM的流行和困难，诊断的准确性允许适当的 缺乏治疗，导致治疗OM的资源错位。这项建议建立在我们的中央 假设增强的诊断工具，特别是光学相干断层扫描(OCT)将产生 改善诊断并减少治疗急性OM所需的抗生素，减少手术 对慢性中耳炎的干预，总体上减少了与这种疾病相关的并发症和成本。 在这项提案中，我们将探讨三个具体目标。第一个目标，A部分，我们将进行一个比较 充气耳镜和光学相干断层扫描对中耳病理的评价 (OCT)在初级保健诊所就诊时主诉耳痛(耳痛)或OM的儿童患者， 假设OCT添加到标准PO中将提高诊断准确性并减少总体抗生素 处方。在这一目标的B部分，使用PO和PSA对ME病理进行比较评估 将对提交给儿科患者的儿童患者进行听力学/鼓室导纳(TY)和OCT 转诊为慢性渗出性中耳炎(OME)的耳鼻咽喉科诊所，假设OCT 添加到标准PO和TY将提高诊断准确性，并减少患者的总体手术需求 和OME一起。在第二个目标中，我们将使用在前一个目标中捕获的OCT图像来开发图像 OCT图像渗液和生物膜自动识别的处理和机器学习算法 为医疗决策增加OM诊断的数据。最后，使用之前捕获的OCT图像， 与我们的机器学习算法一起，我们将建立基于OCT B模式和M模式图像的特征 预测随着时间推移中耳积液的消退或持续时间。总体而言，这个项目将 展示诊断工具和算法方面的这些进步将如何改进诊断并提供 OM管理中的临床决策信息。

PROJECT SUMMARY/ABSTRACT Otitis media (OM) is the most common diagnosis in pediatric patients seen for illness in the United States (1,2), affects 90% of all children (3,4) and is the most common indication for antimicrobial therapy and surgery (5) in young children. Despite many attempts to improve diagnosis, treatment, and prevention, OM continues its highly prevalent impact on children and substantial ongoing morbidity (1,3,6-30). OM continues as the most common cause of hearing loss (HL) in children and leads to speech, educational and other developmental delays (31-37). OM causes life-threatening complications (22,27) and is expensive, resulting in over $5 billion annually in U.S. health care expenditures (3,38). Despite the prevalence and difficulties with OM, diagnostic accuracy to allow appropriate treatment is lacking, leading to misplaced resources in treating OM. This proposal builds on our central hypothesis that enhanced diagnostic tools, specifically, optical coherence tomography (OCT), will yield improved diagnosis and lead to reduced need for antibiotics to treat acute OM, reduced surgical interventions for chronic otitis media, and overall fewer complications and cost associated with this disease. In this proposal we will explore three specific aims. The first aim, part A, we will perform a comparative assessment of middle ear (ME) pathology using pneumatic otoscopy (PO) and optical coherence tomography (OCT) in pediatric patients that present to a primary care clinic with complaints of otalgia (ear pain) or OM, with the hypothesis that OCT added to standard PO will improve diagnostic accuracy and reduce overall antibiotic prescriptions. In part B of this aim, a comparative assessment of ME pathology using PO along with audiology/tympanometry (TY) and OCT will be performed in pediatric patients that present to the pediatric otolaryngology clinic with a referral for chronic otitis media with effusion (OME), with the hypothesis that OCT added to standard PO and TY will improve diagnostic accuracy and reduce overall need for surgery in patients with OME. In the second aim, using the OCT images captured in the previous aim, we will develop image processing and machine learning algorithms for automated identification of effusions and biofilms in OCT image data to augment OM diagnosis for medical decision making. Finally, using the OCT images captured previously, along with our machine learning algorithms, we will establish OCT B-mode and M-mode image-based features that predict the resolution or persistence of middle ear effusions over time. Collectively, this project will demonstrate how these advances in diagnostic tools and algorithms will improve diagnosis and provide added information for clinical decision making in the management of OM.