目的:在正常耳镜检查的初步诊断评估中,如果有机械性病变,可能很难确定具体的病理。听力图可以告知传导性听力损失,但不能告知根本原因。例如,内耳状况上耳道裂开(SCD)和中耳病变骨固定术(SF)之间的听力图相似,尽管病理和病变部位存在差异。为了获得机械信息,宽带鼓室测量(WBT)可以很容易地进行无创。吸光度,最常见的WBT指标,与吸收的声能有关,可以提供有关特定机械病理学的信息。然而,吸光度测量是具有挑战性的分析和解释。本研究开发了一种原型分类方法来自动化诊断估计。考虑了三种预测模型:一种用于识别SCD与SF的耳朵,另一个用于识别SCD与正常,最后,一种区分SCD的三向分类模型,SF,正常的耳朵
方法:在鼓室峰值压力(TPP)和0daPa下,在具有SCD和SF的耳朵以及正常耳朵中测量吸光度。通过两种方法估算特性阻抗:常规方法(基于恒定的耳道面积)和浪涌方法,用声学方法估计耳道面积。使用多变量逻辑回归的分类模型预测每个条件的概率。要量化预期性能,选择概率最高的病症作为可能的诊断.模型的特点包括:仅吸收,仅空气-骨间隙(ABG),和吸光度+ABG。将吸光度转化为吸光度的主要成分,以降低数据的维数并避免共线性。为了最小化过拟合,正则化,由参数lambda控制,被引入回归中。跨多个频率的平均ABG是单个特征。通过调整主成分的数量来优化模型性能,λ的大小,以及ABG平均值中包含的频率。最后,使用TPP吸光度与0daPa的模型性能,并使用浪涌法与恒定耳道面积进行了比较。要在模型未知的种群上估计模型性能,对70%的数据重复训练回归模型,并对其余30%的数据进行验证.使用随机训练/验证拆分的交叉验证重复1000次。
结果:基于仅吸光度特征区分SCD和SF的模型对SCD的敏感性为77%,对SF的敏感性为82%。结合吸光度+ABG将灵敏度提高到96%和97%。仅使用吸光度区分SCD和正常情况提供40%的SCD灵敏度,通过吸光度+ABG提高到89%。仅使用吸光度的三向模型正确分类了31%的SCD,SF的20%和正常耳的81%。吸光度+ABG对SCD的敏感性提高到82%,SF为97%,正常为98%。总的来说,在TPP下使用吸光度的分类性能优于在0daPa下的分类性能。
结论:作为多变量逻辑回归模型的特征,宽带吸收和ABG的组合可以在初始评估时为机械性耳部病变提供良好的诊断估计。这种诊断自动化可以实现更快的后处理并提高资源效率。
OBJECTIVE: 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.
METHODS: 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.
CONCLUSIONS: 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.