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，占听力障碍的50%以上，但不像 感觉神经丧失，治疗的可能性很高。传导损耗源于多种可能的 病理学，如听骨固定、听骨离断或上耳道裂开，其中每一种都 需要不同的待遇。此外，这些不同的病理可能是由类似的身体创伤引起的， 表现出类似的症状，这意味着在大多数情况下，基于X射线的成像和探索性手术， 用于确认可疑的病理。由于费用高、对患者有风险、主观性强等原因， 诊断选择，一个便宜的，非侵入性的措施将是有价值的评估中耳（ME） 状态，以减少术前诊断的不确定性，并监测术后结果。 宽带鼓室导抗法（WBT），使用耳道探针快速测量频率变化的 在负静态压力和正静态压力范围内的ME的导纳/阻抗，可能成为成本- 非侵入性诊断ME病理的有效工具。然而，挖掘复杂的WBT数据集的任务 ME病理学的可靠指标已被证明具有挑战性。机器学习（ML），以其强大的模式- 识别和分类能力，可以提供一个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，主要是由于缺乏 足够大的WBT数据集，这些数据集通常是训练ML算法所需的。我们 建议训练一个推理神经网络（NN）来执行快速准确的WBT客观解释 数据为了解决缺乏足够的病理学识别训练数据的问题，我们建议使用合成WBT 来自人耳的解剖学上真实的有限元（FE）模型的响应， 行为在正常范围内随机改变模型的材料特性和几何参数， 超出正常范围将模拟正常和病理条件同时考虑受试者间的变异性， ME结构的年龄相关变化和测量噪声。推理NN将在此基础上进行训练 模型参数和响应的总体，以生成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 通过确定ME的生理特性，预测的参数值可以指示响应是否表现出 正常或病理特征。接下来，通过应用迁移学习来扩展NN知识 由于确诊病理病例的临床WBT数据有限，沿着额外的非侵入性 临床数据，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NN用于非侵入性客观评估给定耳朵具有以下一种（或多种）的可能性 各种传导性疾病它的使用可以减少或避免不必要的探查手术， 提高术前准备的特异性，并提供一种低成本的术后监测手段。

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.