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

使用基于模拟的推理和应用于临床数据的迁移学习来客观、无创地诊断中耳和传导性病变

• 批准号: 10438246
• 负责人: Hamid Motallebzadeh
• 金额: $ 10.91万
• 依托单位: MASSACHUSETTS EYE AND EAR INFIRMARY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10438246
• 关键词: Accounting; Affect; Age; Air; Anatomy; Audiology; Behavior; Characteristics; Classification; Clinical; Clinical Data; Clinical Research; Complex; Computer Models; Conductive hearing loss; Data; Data Set; Diagnosis; Diagnostic; Differential Diagnosis; Dimensions; Ear; Elements; Exhibits; External auditory canal; Eye; Feasibility Studies; Frequencies; Geometry; Goals; Hearing Tests; Human; Image; Individual; Knowledge; Label; Ligaments; Literature; Machine Learning; Manuals; Maps; Measurement; Measures; Methodology; Methods; Mining; Modeling; Monitor; Network-based; Noise; Normal Range; Operative Surgical Procedures; Otosclerosis; Outcome; Outcome Study; Output; Pathologic; Pathology; Patients; Pattern Recognition; Performance; Physicians; Physiological; Population; Postoperative Period; Preparation; Probability; Process; Property; Randomized; Reporting; Risk; Roentgen Rays; Sampling; Series; Specificity; Specimen; Stapes; Statistical Data Interpretation; Structure; Surgical Disarticulation; Symptoms; Techniques; Testing; Training; Trauma; Tympanometry; Uncertainty; Variant; X-Ray Computed Tomography; age related; base; bone; computerized data processing; cost; cost effective; diagnostic criteria; electric impedance; experience; feature extraction; feature selection; feeding; hands-on learning; hearing impairment; image registration; improved; insight; large datasets; machine learning algorithm; microCT; middle ear; neural network; neural network architecture; noninvasive diagnosis; pressure; response; sample fixation; simulation; stem; tool; transfer learning

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.

传导性听力损失影响所有年龄段，占听力障碍的 50% 以上，但与 感觉神经性损失，治疗的潜力很高，源于多种可能的情况。 病理学，例如听骨固定、听骨离断或上半规管裂开，其中每一种 此外，这些不同的病症可能是由类似的身体创伤和症状引起的。 表现出相似的症状，这意味着在大多数情况下基于 X 射线的成像和探查手术 用于确认可疑的病理，因为成本高，对患者有风险，并且现有的主观性。 诊断选择，一种廉价、非侵入性的措施对于评估中耳 (ME) 很有价值 状态，以减少术前诊断的不确定性，并监测术后结果。 宽带鼓室导抗测试 (WBT)，使用耳道探头快速测量频率变化的声音 ME 在一系列负静压和正静压下的导纳/阻抗可能会成为成本 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。 机器学习 (ML) 具有强大的模式，因此寻找可靠的 ME 病理指标已被证明具有挑战性。 识别和分类能力，可以为此提供可靠的方法，但是，只是非常可靠。 迄今为止，将机器学习纳入 ME 评估的尝试有限，主要是由于缺乏 训练机器学习算法通常需要足够大的已确认病理的 WBT 数据集。 建议训练推理神经网络 (NN) 以快速、准确地客观解释 WBT 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。 来自人耳解剖学上真实的有限元 (FE) 模型的响应，并经过验证的机械性能 在正常和正常范围内随机改变模型的材料属性和几何参数。 超出正常范围将模仿正常和病理条件，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将对此进行训练。 模型参数和响应的总体，以生成每个参数值的概率分布 每当出现新的 WBT 响应时，因为每个模型参数都映射到特定的。 ME的生理特征，预测的参数值可以指示是否表现出响应 接下来，将通过应用迁移学习来扩展神经网络知识。 已确诊病理病例的可用临床 WBT 数据有限，以及额外的无创性 该项目的结果将是经过培训的临床数据，例如听力图和气骨间隙测量。 推理神经网络，用于对给定耳朵有一个（或多个）以下情况的可能性进行非侵入性客观评估 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，并提供低成本的术后监测手段。