SCH: Smart Auscultation for Pulmonary Diagnostics and Imaging

SCH：用于肺部诊断和成像的智能听诊

• 批准号: 10590732
• 负责人: Mounya Elhilali
• 金额: $ 29.28万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590732
• 关键词: Accident and Emergency department; Acoustics; Address; Affect; Air; Air Movements; Alveolus; Ambulances; Anatomy; Architecture; Artificial Intelligence; Auscultation; Biological Markers; Blood Tests; Breathing; Case Management; Case/Control Studies; Categories; Chest; Childhood; Clinic; Clinical; Clinical Data; Communities; Complement; Complex; Consumption; Devices; Diagnosis; Diagnostic; Ear; Elements; Engineering; Environment; Goals; Hand; Head; Health; Health Personnel; Heart; Home; Hospitals; Image; Imaging Device; Inflammation; Interobserver Variability; Judgment; Location; Lower Respiratory Tract Infection; Lung; Lung infections; Machine Learning; Masks; Medical; Modality; Monitor; Mucous body substance; Noise; Nursing Staff; Obstruction; Outcome; Oxygen saturation measurement; Patients; Pattern; Physicians; Pneumonia; Positioning Attribute; Public Health; Radiation; Resources; Respiratory Diaphragm; Respiratory Sounds; Respiratory System; Respiratory Tract Infections; Roentgen Rays; Scheme; Signal Transduction; Skin; Source; Standardization; Stethoscopes; Techniques; Technology; Telemedicine; Testing; Thoracic Radiography; Time; Training; Travel; Triage; Visualization; accurate diagnosis; clinical biomarkers; clinical diagnosis; clinical examination; clinical practice; community clinic; cost; deep learning; design; diagnostic value; electric impedance; field study; frontier; imaging modality; improved; innovation; interest; invention; lung imaging; medical attention; new technology; novel; pediatric emergency; point of care; recurrent neural network; respiratory health; screening; sensor; sound; standard care; tool; transmission process; treatment strategy; ultrasound; usability; virtual; wearable device

The stethoscope is a ubiquitous technology used to listen to sounds from the chest in order to assess lung or heart conditions. Despite its universal use, it is considered an unreliable diagnosis tool due to a number of limitations: masking by noise, need for highly trained users and ear to interpret lung sounds and subjectivity in interpreting auscultation sounds. Still, one of the reasons auscultations are a staple of clinical screening is that sound is one the cheapest, fastest and most readily available biomarkers. The simple fact of breathing involves sound traveling through chest cavities that will be affected by presence of obstructions or abnormalities. While the signature of these air flow disruptions may be concealed, the right engineering innovation should not only identify their presence but can be extended as an imaging modality to identify their location, which would be a novel use of breath sounds to image lung cavities. The proposed smart auscultation technology is innovative in three ways: (i) it develops a machine learning architecture that imposes finite-element airway propagation constraints and stochastic variational inference using recurrent neural networks, (ii) a novel piezo-sensing material with tunable acoustic impedance that matches the skin hence eliminating air as transmission medium between the chest and device diaphragm which virtually eliminates pick up of any ambient noise, (iii) an array device that leverages the piezo-sensor to develop an imaging device using passive breathing sounds (instead of radiations or ultrasound probes). The proposed technology is extremely low-cost, deployable under adverse conditions, usable for immediate clinical examination as well as extendable for monitoring as a wearable device. The new technology will be field tested directly in case/control studies at the Johns Hopkins pediatric ER and pulmonary clinics to validate localization accuracy from the auscultation array using physicians’ judgments as gold standard. If successful, this technology will complement alternative, often costly and time-consuming diagnosis schemes (X-rays or ultrasounds which often cost $100-$1000’s) to offer a fast, cheap (few $) and accessible tool that can be widely disseminated from community clinics to hospitals and potentially home-based health monitoring. Given the dire public health need in addressing ALRI challenges, the proposed low-cost and efficient technology can be a game changer as a point-of-care aid to triage cases that require further medical attention.

听诊器是一种无处不在的技术，用于聆听胸部的声音以评估肺部或心脏状况。尽管有多种局限性，但它被认为是一种不可靠的诊断工具：通过噪声掩盖，需要训练有素的用户和耳朵来解释肺部声音和主观性在解释听觉声音时。尽管如此，听诊是临床筛查的主要原因之一，即声音是最便宜，最快，最容易获得的生物标志物之一。呼吸的简单事实涉及在胸腔中传播的声音，这些声音会受到物体或异常的影响。尽管可能会隐藏这些空气流中断的签名，但正确的工程创新不仅应标识它们的存在，而且可以作为成像方式扩展以识别其位置，这将是一种新颖的呼吸声音来形象肺腔。提出的智能神学培养技术在三种方面具有创新性：（i）不可能使用复发性神经网络的有限元元素呼吸道传播约束和随机变异推理，（ii）一种新颖的压力感应材料，可调节的挑战，使空气上的任何动力匹配中等程度上等的胸部，环境噪声（iii）一种阵列设备，该阵列设备利用压电传感器使用被动呼吸声（而不是辐射或超声问题）开发成像设备。提出的技术非常低成本，可在不利条件下部署，可用于立即进行临床检查，并且可作为可穿戴设备进行监视。这项新技术将直接在约翰·霍普金斯儿科和肺诊所的案件/对照研究中进行现场测试，以验证使用医师法官作为金标准的听诊阵列的定位准确性。如果成功的话，这项技术将补充替代方案，通常是昂贵且耗时的诊断方案（X射线或超声检查，通常花费$ 100- $ 1000），以提供一种快速，便宜（几美元），可访问的工具，可以将社区诊所广泛传播给医院和基于家庭的健康监测。考虑到解决ALRI挑战时的可怕公共卫生需求，拟议的低成本和高效技术可以成为改变游戏的人，作为对需要进一步医疗护理的分类案例的护理点辅助工具。