Automating quality assessment of B-mode ultrasound scans (AQABUS)

同一患者的超声（US）扫描的诊断质量可能会有很大差异，这取决于超声医师的技能和专业水平以及所使用的设备。这种有充分记录的操作者对质量的依赖可能导致误诊；不必要地转诊到其他成像方式；重新扫描;并导致临床工作流程效率低下，并增加相关的医疗保健成本。为了避免由此导致的延迟或不正确分层，鼓励超声医师遵循标准化采集协议指南。临床研究表明了这种做法的好处。然而，没有进行系统的图像审查。这种缺乏标准化和一致性监测的情况影响了卫生服务的诊断质量和临床决策，也是广泛采用US作为分层医学的一种具有成本效益的技术的关键障碍。在智能超声有限公司和牛津大学的合作中，AQABUS项目提议开发和评估一个技术平台，用于自动评估超声扫描的质量。该解决方案将作为质量保证服务（QAS）交付给成像部门，以评估扫描是否适合目的。创新包括为提供该等服务而建立资讯科技基础设施；QA报告的临床设计；用机器学习来自动化图像质量保证，这是商业主张的关键。

Diagnostic quality of ultrasound (US) scans of the same patient can vary significantly, depending on the level of skill andexpertise of the sonographer and the equipment used. This well documented operator dependence of quality can lead tomisdiagnosis; unnecessary referrals to other imaging modalities; rescans; and results in inefficiencies in clinical workflowwith associated added healthcare costs.In order to avoid the resultant delayed or incorrect stratification, sonographers are encouraged to follow standardisedacquisition protocol guidelines. Clinical studies show the benefit of this. However, no systematic image reviews areundertaken. This lack of standardization and monitoring of conformance has implications for the quality of diagnosis andclinical decisions made in a health service, and is also a key barrier to wider adoption of US as a cost-effective technologyfor stratified medicine.In a collaboration between Intelligent Ultrasound Ltd and Oxford University, the AQABUS project proposes to develop andevaluate a technology platform for automated quality assessment of US scans. The solution will be delivered as a QualityAssurance Service (QAS) to imaging departments, to assess whether scans are fit for purpose. Innovations includecreation of the IT infrastructure for delivering such a service; clinical design of QA reporting; and machine learning toautomate image QA which is key to the business proposition.