Rapid Detection of Common Failure Modes for Knee Prosthetics

摘要 在过去的十年里，膝关节置换手术的数量呈指数级增长，没有 有放缓的迹象。在维持高度成功的结果的同时，种植失败仍会发生， 膝关节翻修手术的年发病率稳定在初次手术的10%左右。种植体松动 已经被认为是导致长期失败的主要原因。然而，只有当骨骼松动时才能检测到松动 损失是如此之大，以至于在x光图像中可以看到种植体和骨骼之间的缝隙，到那时 松动可能会相当严重，并有立即导致周围剩余厚度的骨骼骨折的危险。 植入物。当病人在膝关节内或周围有疼痛时，外科医生通常不愿 在无法明确诊断疼痛原因的情况下进行翻修手术。一种高灵敏度的工具 而特异性和比目前诊断更好地检测松动的能力将为外科医生提供 在疼痛患者的诊断性评估中确认或排除植入物松动的能力。 Bruin Biometrics LLC计划开发一种非侵入性设备，可以检测到人工关节的松动 比目前诊断标准更准确的膝关节植入物。最终的产品被设想出来了 作为一种类似于膝盖支架的捆绑装置，它使用传感器来捕捉声发射和关节角度 当受试者进行一系列屈/伸动作时。多重被动声学 传感器与皮肤接触，从身体内部接收自然产生的声发射。 来自传感器的信号经过过滤、分析和分类，以确定是否有任何信号是由 松动的种植体和松动的程度。在相关的一期工程中，我们验证了一个松动的种植体 将产生与功能良好或磨损的植入物相关的独特的声学事件。最近， 我们建立了这些信号相对于自然产生的本机膝关节信号的额外唯一性 猪膝盖模型。显然，天然组织产生的声信号的特征，如 韧带、肌腱和肌肉，很容易通过频率与人工关节信号区分开来 域功能过滤。为第二阶段研究拟议的工作将建立在这一基础知识和 开发和优化能够捕获、定位和分类指示植入物的信号的诊断工具 松开了。这项工作将从进一步的膝盖和植入物的体外测试开始，并通过身体进行进展 测试，最后，结合对手术后活体人类患者的临床评估对该设备进行测试 都在经历痛苦。

Abstract The number of knee joint replacement procedures has increased exponentially over the last decade with no indications of slowing down. While sustaining highly successful outcomes, implant failures still occur and the annual incidence of knee revision surgeries remain steady around 10% of primary surgeries. Implant loosening has been cited as the dominant cause of long-term failure. Loosening, however, is only detectable when the bone loss is so great that gaps between the implant and the bone are visible in an x-ray image, by which time the loosening may be quite severe with an immediate risk of fracture of the remaining thickness of the bone around the implant. When a patient has pain in or around their knee implant, surgeons are generally reluctant to perform revision surgery in the absence of a clear diagnosis of the cause of the pain. A tool with high sensitivity and specificity and the ability to detect loosening better than current diagnostics would provide a surgeon with the ability to confirm, or rule out, loosening of the implant during the diagnostic assessment of a patient in pain. Bruin Biometrics LLC proposes to develop a non-invasive device that could detect loosening of an artificial knee implant with greater accuracy than the current diagnostics standard. The final product is envisioned as a strap-on device resembling a knee brace which uses sensors to capture acoustic emission and joint angle information while the test subject performs a series of flexion/extension movements. Multiple passive acoustic sensors are in contact with the skin to pick up naturally occurring acoustic emissions from within the body. Signals from the sensors are filtered, analyzed, and categorized to determine if any of the signals are caused by a loosened implant and the degree of loosening. During the related Phase-1 project, we verified a loose implant will produce distinctive acoustic events relative to well-functioning or otherwise worn implants. More recently, we established the additional uniqueness of these signals against naturally occurring native knee signals using a porcine knee model. Evidently, the characteristics of acoustic signals generated by native tissues, such as ligament, tendons, and muscles, are readily distinguishable from artificial joint signals through frequency domain feature filtering. The work proposed for the phase-2 research will build on this base knowledge and develop and optimize a diagnostic tool capable of capturing, locating, and classifying signals indicative of implant loosening. This work will start with further in vitro testing of knees and implant and progress through cadaveric tests and, finally, testing of the device in conjunction with clinical evaluation of live post-op human patients who are experiencing pain.