Innovative multi-channel AI method for the improved diagnosis of upper extremity bone fractures in the trauma setting.

创新的多通道人工智能方法，用于改进创伤环境中上肢骨折的诊断。

• 批准号: 44808
• 金额: $ 36.71万
• 依托单位: IBEX INNOVATIONS LTD
• 依托单位国家: 英国
• 项目类别: Study
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=44808
• 关键词: Innovative; multi; channel; AI; method

This project will develop the proof of concept of an innovative multi-channel AI method to improve the prevention and diagnosis of fragility fractures.Digital radiology (DR) remains the standard imaging protocol for fracture detection after trauma and whilst the majority of pathologies are correctly identified, a significant proportion are missed, leading to a delay in diagnosis, increased pain and suffering and additional costs from repeated diagnostic tests and treatment. Radiologists assess many features of a standard DR image to predict the presence of pathologies or the risk of future fractures that requires a more detailed inspection of bone structure in the affected region. However, trauma radiologists have limited time to manipulate image contrast and determine patient risk based on pre-determined factors, and certain pathologies will be missed if they are not immediately obvious in the radiograph.The project will build upon the unique IBEX Trueview(r) technology which has been developed with the support of previous InnovateUK funded projects. Using standard DR images, Trueview generates improved image quality and unique composition-based outputs to suppress soft tissue contrast and boost bone fine detail, thereby improving the visibility of subtle fractures and other pathologies. It is also able to provide an accurate measure of bone health based on bone density. Trueview includes a proprietary AI-based bone segmentation algorithm to accurately segment bone from soft tissue with limited training data.The project will have three primary aims: 1) To further enhance the proprietary "IBEX XNET" AI-based bone segmentation method to generate an additional quantitative bone morphology output; 2) To develop new composition-guided post processing methods to create independent bone-enhanced and tissue-enhanced diagnostic images; and 3) To automate the process of detecting or determining risk or future fragility fractures.

该项目将开发一种创新的多通道人工智能方法的概念验证，以改善脆性骨折的预防和诊断。数字放射学（DR）仍然是创伤后骨折检测的标准成像协议，虽然大多数病理被正确识别，但很大一部分被遗漏，导致诊断延迟，增加疼痛和痛苦以及重复诊断测试和治疗的额外费用。放射科医生评估标准DR图像的许多特征，以预测病变的存在或未来骨折的风险，这需要对受影响区域的骨结构进行更详细的检查。然而，创伤放射科医生只有有限的时间来操作图像对比度，并根据预先确定的因素确定患者的风险，如果某些病理在X光片中不立即明显，则会被遗漏。该项目将建立在独特的IBEX Trueview（r）技术基础上，该技术是在先前InnovateUK资助项目的支持下开发的。使用标准DR图像，Trueview生成改进的图像质量和独特的基于合成的输出，以抑制软组织对比度并增强骨骼精细细节，从而提高细微骨折和其他病变的可见性。它还能够根据骨密度提供骨骼健康的准确测量。Trueview包含专有的基于人工智能的骨骼分割算法，可以在有限的训练数据下准确地从软组织中分割骨骼。该项目将有三个主要目标：1）进一步增强专有的“IBEX XNET”基于人工智能的骨骼分割方法，以生成额外的定量骨骼形态输出; 2）开发新的成分引导后处理方法，以创建独立的骨增强和组织增强诊断图像;以及3）自动化检测或确定风险或未来脆性骨折的过程。