Automatic Rib Fracture Detection in Pediatric Radiography to Identify Non-Accidental Trauma

儿科放射线照相中的自动肋骨骨折检测以识别非意外创伤

• 批准号: 9976563
• 负责人: Adam M Alessio
• 金额: $ 17.81万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-12 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9976563
• 关键词: Assessment tool; Cause of Death; Cessation of life; Chest; Child; Child Abuse; Childhood; Clinical; Computer Assisted; Data; Detection; Diagnostic; Diagnostic radiologic examination; Ensure; Face; Fracture; Goals; Hospitals; Image; Image Analysis; Infant; Injury; Institutional Review Boards; Label; Lead; Lung; Machine Learning; Mediastinal; Medical Imaging; Methodology; Methods; Modeling; Non-accidental; Outcome; Patient imaging; Pediatric Hospitals; Pediatric Radiology; Performance; Population; Positioning Attribute; Respiratory Diaphragm; Rib Fractures; Role; Sentinel; Site; Structure; Supervision; Testing; Thoracic cavity structure; Time; Training; Translations; Trauma; United States; Vendor; Work; bone; child physical abuse; clinical practice; convolutional neural network; cost; deep learning; detector; experience; follow-up; hands-on learning; imaging platform; imaging study; improved; machine learning method; mortality; multimodality; novel; patient population; physical abuse; radiologist; rapid technique; rib bone structure; soft tissue; supervised learning; tool

ABSTRACT Automatic Rib Fracture Detection in Pediatric Radiography to Identify Non-Accidental Trauma PI: Adam M. Alessio Non-accidental trauma caused by physical abuse is a leading cause of death in children in the United States. Because rib fractures are highly predictive of child abuse and chest radiographs are commonly performed for multiple indications, pediatric chest radiographs can have a critical role in the identification of abuse. Detection of rib fractures on pediatric radiographs is challenging and a high percentage of fractures are missed, particularly in imaging centers with limited pediatric radiology experience. Currently, there are no viable computer assisted strategies for rib fracture detection on chest radiographs. The purpose of this proposal is to develop machine learning methodology to detect rib fractures on pediatric radiographs using images from a network of hospitals. These methods will rely on a two-stage approach including a thoracic cavity segmentation stage followed by a fracture detection stage. We will explore two fracture detection strategies using novel supervised learning approaches: a heterogeneous U-net and a multi-modal regional-convolutional neural network. These methods will be trained and tested with a large set of fracture-absent radiographs (N=1000) from Seattle Children's Hospital and a diverse set of labelled fracture-present radiographs (N=500) from collaborating sites. These methods will be developed with an intentionally diverse set of radiographs, representative of the variety of fracture presentations and image quality in clinical practice, in order to position this rib fracture detection method for rapid translation to clinical practice. The ultimate goal of this proposal is to provide a computer assisted rib fracture assessment tool that would be a rapid and widely-available add-on to all pediatric chest radiograph exams, improving detection of rib fractures and potentially leading to improved identification of child abuse.

抽象的 儿科放射线照相中的自动肋骨骨折检测以识别非意外创伤 PI：亚当·M·阿莱西奥 身体虐待造成的非意外创伤是美国儿童死亡的主要原因。 因为肋骨骨折高度预示着虐待儿童，而胸部 X 光检查通常用于以下情况： 在多种适应症下，儿科胸部X光片在识别虐待方面发挥着关键作用。检测 在儿童 X 光片上检查肋骨骨折具有挑战性，并且很大比例的骨折被遗漏， 特别是在儿科放射学经验有限的影像中心。目前还没有可行的 胸片上肋骨骨折检测的计算机辅助策略。该提案的目的是 开发机器学习方法，使用来自儿童 X 光片的图像来检测肋骨骨折 医院网络。这些方法将依赖于两阶段方法，包括胸腔分割 阶段之后是断裂检测阶段。我们将使用新颖的方法探索两种断裂检测策略 监督学习方法：异构 U 网和多模态区域卷积神经网络 网络。这些方法将使用大量无骨折的 X 光照片进行训练和测试 (N=1000) 来自西雅图儿童医院的照片以及来自西雅图儿童医院的一组不同的标记骨折存在的 X 光照片 (N=500) 合作网站。这些方法将通过一组有意多样化的射线照片来开发， 代表临床实践中的各种骨折表现和图像质量，以便定位 这种肋骨骨折检测方法可快速转化为临床实践。该提案的最终目标是 提供计算机辅助肋骨骨折评估工具，该工具将是一个快速且广泛可用的附加工具 适用于所有儿科胸部 X 光检查，改善肋骨骨折的检测，并可能导致改善 虐待儿童的识别。