Toward Automating Diagnosis of Middle- and Inner-ear Mechanical Pathologies With a Wideband Absorbance Regression Model

Author:

Eberhard Kristine Elisabeth12,Merchant Gabrielle R.3,Nakajima Hideko Heidi1,Neely Stephen T.3

Affiliation:

1. Department of Otolaryngology—Head and Neck Surgery, Harvard Medical School, Massachusetts Eye and Ear, Boston, MA, USA

2. Copenhagen Hearing and Balance Centre, Department of Otolaryngology, Head and Neck Surgery & Audiology, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark

3. Boys Town National Research Hospital, Omaha, NE, USA.

Abstract

Objectives: During an initial diagnostic assessment of an ear with normal otoscopic exam, it can be difficult to determine the specific pathology if there is a mechanical lesion. The audiogram can inform of a conductive hearing loss but not the underlying cause. For example, audiograms can be similar between the inner-ear condition superior canal dehiscence (SCD) and the middle-ear lesion stapes fixation (SF), despite differences in pathologies and sites of lesion. To gain mechanical information, wideband tympanometry (WBT) can be easily performed noninvasively. Absorbance, the most common WBT metric, is related to the absorbed sound energy and can provide information about specific mechanical pathologies. However, absorbance measurements are challenging to analyze and interpret. This study develops a prototype classification method to automate diagnostic estimates. Three predictive models are considered: one to identify ears with SCD versus SF, another to identify SCD versus normal, and finally, a three-way classification model to differentiate among SCD, SF, and normal ears. Design: Absorbance was measured in ears with SCD and SF as well as normal ears at both tympanometric peak pressure (TPP) and 0 daPa. Characteristic impedance was estimated by two methods: the conventional method (based on a constant ear-canal area) and the surge method, which estimates ear-canal area acoustically. Classification models using multivariate logistic regression predicted the probability of each condition. To quantify expected performance, the condition with the highest probability was selected as the likely diagnosis. Model features included: absorbance-only, air-bone gap (ABG)-only, and absorbance+ABG. Absorbance was transformed into principal components of absorbance to reduce the dimensionality of the data and avoid collinearity. To minimize overfitting, regularization, controlled by a parameter lambda, was introduced into the regression. Average ABG across multiple frequencies was a single feature. Model performance was optimized by adjusting the number of principal components, the magnitude of lambda, and the frequencies included in the ABG average. Finally, model performances using absorbance at TPP versus 0 daPa, and using the surge method versus constant ear-canal area were compared. To estimate model performance on a population unknown by the model, the regression model was repeatedly trained on 70% of the data and validated on the remaining 30%. Cross-validation with randomized training/validation splits was repeated 1000 times. Results: The model differentiating between SCD and SF based on absorbance-only feature resulted in sensitivities of 77% for SCD and 82% for SF. Combining absorbance+ABG improved sensitivities to 96% and 97%. Differentiating between SCD and normal using absorbance-only provided SCD sensitivity of 40%, which improved to 89% by absorbance+ABG. A three-way model using absorbance-only correctly classified 31% of SCD, 20% of SF and 81% of normal ears. Absorbance+ABG improved sensitivities to 82% for SCD, 97% for SF and 98% for normal. In general, classification performance was better using absorbance at TPP than at 0 daPa. Conclusion: The combination of wideband absorbance and ABG as features for a multivariate logistic regression model can provide good diagnostic estimates for mechanical ear pathologies at initial assessment. Such diagnostic automation can enable faster workup and increase efficiency of resources.

Publisher

Ovid Technologies (Wolters Kluwer Health)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3