Objective and noninvasive diagnosis of middle-ear and conductive pathologies using simulation-based inference and transfer learning applied to clinical data

传导性听力损失影响所有年龄段，占听力损伤的50%以上，但不同于 感觉神经缺失，治疗的可能性很大。传导损耗源于多种可能的 病理，如听骨固定、听骨分离或上耳道裂开，每一种 需要不同的治疗方法。此外，这些不同的病理可能是由类似的身体创伤和 表现出类似的症状，这意味着在大多数情况下，基于x射线的成像和探查手术 用来确认一种可疑的病理。由于存在高成本、对患者的风险和主观性 诊断选项，一种廉价的非侵入性测量将是有价值的评估中耳(ME)。 以减少手术前诊断的不确定性，并监测手术后的结果。 宽带鼓室测定法(WBT)，使用耳道探头快速测量频率变化 ME在负静压和正静压范围内的导纳/阻抗可能成为成本- 无创性诊断ME病理的有效工具。然而，挖掘复杂的WBT数据集的任务 对于ME病理的可靠指标已被证明是具有挑战性的。机器学习(ML)，以其强大的模式- 识别和分类能力，可以提供一种可靠的方法来做到这一点。然而，只有非常 到目前为止，将ML纳入ME评估的尝试有限，主要是因为缺乏 足够大的已确认病理的WBT数据集，通常需要训练ML算法。我们 建议训练一个推理神经网络(NN)，以便快速准确地对WBT进行客观解释 数据。为了解决缺乏足够的病理识别的训练数据，我们建议使用合成WBT 解剖逼真的人耳有限元模型的响应 行为。随机改变模型的材料属性和几何参数 超出正常范围将模拟正常和病理条件，同时考虑受试者之间的可变性， ME结构的年龄相关变化，以及测量噪声。推理神经网络将在这方面进行训练 模型参数和响应的总体，以产生每个参数值的概率分布 每当它被呈现新的WBT响应时。由于每个模型参数都映射到特定的 ME的生理特性，预测的参数值可以指示是否表现出响应 正常的或病理的特征。接下来，通过应用迁移学习来扩展神经网络的知识 确认的病理病例的有限的可用临床WBT数据，以及其他非侵入性的 临床资料，如听力图和气骨间隙测量。该项目的成果将是一个训练有素的 推理神经网络用于非侵入性客观评估给定耳朵具有一个(或多个) 各种传导性病理。它的使用可以减少或避免不必要的探查手术， 提高了术前准备的特异性，提供了一种低成本的术后监护手段。

Conductive hearing loss affects all ages and represents over 50% of hearing impairments, but unlike sensorineural loss, the potential for treatment is high. Conductive loss stems from a diverse set of possible pathologies, such as ossicular fixation, ossicular disarticulation, or superior-canal dehiscence, each of which requires a different treatment. Moreover, these distinct pathologies can result from similar physical traumas and exhibit similar symptoms, which means that in most cases x-ray-based imaging and exploratory surgeries are used to confirm a suspected pathology. Because of the high cost, risk to the patient, and subjectivity of existing diagnostic options, an inexpensive, noninvasive measure would be valuable to assess the middle-ear (ME) status, to reduce uncertainties about the diagnosis prior to surgery, and to monitor outcomes postoperatively. Wideband tympanometry (WBT), which uses an ear-canal probe to quickly measure the frequency-varying admittance/impedance of the ME across a range of negative and positive static pressures, could become a cost- effective tool for noninvasively diagnosing ME pathologies. However, the task of mining complex WBT datasets for reliable indicators of ME pathologies has proven challenging. Machine learning (ML), with its powerful pattern- recognition and classification capabilities, may provide a reliable methodology for doing this. However, only very limited attempts have been made thus far to incorporate ML into ME assessments, mainly due to the lack of large-enough WBT datasets of confirmed pathologies that are usually required to train ML algorithms. We propose to train an inference neural network (NN) to perform fast and accurate objective interpretations of WBT data. To account for the lack of sufficient pathology-identified training data, we propose using synthetic WBT responses from anatomically realistic finite-element (FE) models of the human ear with verified mechanistic behavior. Randomly varying the material properties and geometric parameters of the models within normal and beyond-normal ranges will mimic normal and pathological conditions while accounting for inter-subject variability, age-related changes to the ME structures, and measurement noise. The inference NN will be trained on this population of model parameters and responses to produce a probability distribution for each parameter value whenever it is presented with a new WBT response. Since each model parameter maps to a specific physiological characteristic of the ME, the predicted parameter values can indicate whether a response exhibits normal or pathological characteristics. Next, the NN knowledge will be expanded by applying transfer learning to the limited available clinical WBT data of confirmed pathological cases, along with additional noninvasive clinical data such as audiograms and air–bone gap measurements. The outcome of the project will be a trained inference NN for noninvasive objective assessments of the likelihood that a given ear has one (or more) of various conductive pathologies. Its use could reduce the need for or avoid unnecessary exploratory surgery, improve the specificity of preoperative preparations, and provide a low-cost means of postoperative monitoring.