The Application of Deep Learning Methods for Proximal Humerus Fracture Feature Identification

摘要 肱骨近端骨折(PHF)是老年人中第三常见的骨折，估计有20万 在美国每年都会发生。PHF会导致疼痛，肩部功能不佳，以及短小和长短- 患者的长期残疾。对老年患者的初步治疗仍存在很大争议 受伤。PHF既可以保守治疗，也可以手术治疗，对于哪些患者存在很大争议 应该进行手术治疗。PHF的一个独特挑战是它们具有不同的呈现形式和范围 复杂性。与其他主要关节骨折的治疗不同，PHF的初始治疗选择是高度 这取决于裂缝的特征。需要疗效证据来指导临床护理 个别PHF患者。Neer分类最初开发于1970年，是使用最广泛的框架 对PHF进行描述和分类。尽管Neer分类是实践中使用最广泛的，但它已经过时， 不完整，经常不正确地应用，并且观察员之间的可靠性很差。没有一个普遍存在的 公认的、标准化的骨折分类系统是治疗发展的关键障碍 PHF的有效性证据。深度学习(DL)计算模型的应用可以自动化和 规范裂缝分类流程，识别所有相关裂缝特征。DL图像分析 已经证明，模型在识别诊断图像上感兴趣的特征方面非常准确。一个 自动化、标准化的PHF分类系统将增强我们普遍标准化骨折的能力 对所有骨科临床护理环境进行分类，提高骨折护理的精确度和效率 并产生治疗效果证据以指导临床实践。此应用程序的总体目标是， 是开发和验证一种能够使用X射线图像识别骨折特征的DL计算模型。 我们的中心假设是，我们可以开发出一个与专家肩部专家一样准确的DL模型 在识别X射线图像上的重要骨折特征方面。在目标1中，我们将修改NEER分类 裂缝特征识别框架。目标2将是为Depth开发一个黄金标准数据集 学习DL断裂特征识别，最终目标3将训练和测试一个DL模型，以 在X光片上识别骨折特征。

SUMMARY Proximal humerus fractures (PHFs) are the third most common fracture in the elderly, with an estimated 200,000 occurring each year in the United States. PHFs can lead to pain, poor shoulder function, plus short and long- term disability for patients. Substantial controversy persists regarding initial treatment for elderly adults with this injury. PHFs can be managed conservatively or surgically and great controversy exists over which patients should be treated surgically. A unique challenge with PHFs is that they have variable presentation and range in complexity. Unlike the management of other major joint fractures, the initial treatment choice for PHF is highly dependent on the fracture characteristics. Treatment effectiveness evidence is needed to guide clinical care for individual patients with PHF. The Neer Classification, first developed in 1970, is the most widely used framework to describe and classify PHFs. Although the Neer Classification is the most widely used in practice, it is outdated, incomplete, often incorrectly applied, and suffers from poor interobserver reliability. The absence of a universally accepted, standardized fracture classification system is a critical barrier in the development of treatment effectiveness evidence for PHF. The application of deep learning (DL) computational models can automate and standardize the fracture classification process and identify all relevant fracture characteristics. DL image analysis models have been shown to be highly accurate at identifying features of interest on diagnostic images. An automated, standardized PHF classification system will enhance our ability to universally standardize fracture classification across all orthopaedic clinical care settings, improve the precision and efficiency in fracture care and generate treatment effectiveness evidence to guide clinical practice. The overall objective for this application, is to develop and validate a DL computational model capable of identifying fracture features using X-ray images. Our central hypothesis is that we can develop a DL model that will be as accurate as expert shoulder specialists in identifying important fracture features on X-ray images. In Aim 1 we will modify the Neer Classification framework for fracture feature identification. Aim 2 will be the development of a gold standard dataset for deep learning DL fracture feature identification, and finally Aim 3 will be the training and testing of a DL model to identify fracture features on X-rays.