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Towards case-based medical learning in radiological decision making using content-based image retrieval

机译：基于内容的图像检索在放射学决策中基于案例的医学学习

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Background Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education. Methods We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment. Results We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system. Conclusions The IBCR-RE paradigm incorporates a novel combination of essential aspects of diagnostic learning in radiology: (i) Provision of work-relevant experiences in a training environment integrated into the radiologist's working context; (ii) Up-to-date training cases that do not require cumbersome preparation because they are provided by routinely generated electronic medical records; (iii) Support of the way adults learn while remaining suitable for the patient- and problem-oriented nature of medicine. Future work will address unanswered questions to complete the implementation of the IRMAdiag trainer.
机译：背景技术放射科医生的培训基于强化实践，可以通过使用诊断培训系统加以改进。但是，现有系统通常需要费力地准备培训案例，并且缺乏通过适当的学习方案整合到临床环境中的能力。因此，在放射教育中不能很好地建立提高决策能力的诊断培训系统。方法我们研究了适合放射学领域的教学概念和评估方法，如下：（i）成人学习理论强调在解决问题的团队中获得与工作有关的实践的重要性; （ii）基于案例的推理（CBR）与人类解决问题的过程并行; （iii）基于内容的图像检索（CBIR）可用于计算机辅助诊断（CAD）。为了克服现有学习系统的已知缺陷，我们开发了基于图像的放射教育案例检索（IBCR-RE）的概念。 IBCR-RE诊断培训被嵌入到基于“七级跳跃”方法的教学框架中，该框架在基于问题的学习（PBL）中已得到很好的确立。为了提供与放射实践尽可能相似的学习环境，我们分析了放射工作流程和环境。结果我们将IBCR-RE诊断培训方法映射到医疗应用程序图像检索（IRMA）框架中，从而提出了IRMAdiag培训应用程序的建议概念。 IRMAdiag利用IRMA的模块化结构，并且包括（i）IRMA核心，即IRMA CBIR引擎; （ii）IRMAcon查看器。我们建议使用标准协议医学数字成像和通信（DICOM）和七级卫生（HL7）将IRMAdiag嵌入医院信息技术（IT）基础结构中。此外，我们提出了一个案例描述和一个计划评估计划，以对系统进行全面评估。结论IBCR-RE范例结合了放射学诊断学习的基本方面的新颖组合：（i）在整合到放射科医生工作环境的培训环境中提供与工作相关的经验; ii不需要繁琐准备的最新培训案例，因为它们是由例行生成的电子病历提供的; （iii）支持成年人学习的方式，同时保持适合患者和问题导向的医学性质。未来的工作将解决未解决的问题，以完成IRMAdiag培训师的实施。

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