Hernández-Rodríguez Jorge, Rodríguez-Conde María-José, Santos-Sánchez José-Ángel, Cabrero-Fraile Francisco-Javier
Department of Biomedical and Diagnostic Sciences, Faculty of Medicine, University of Salamanca, C/Alfonso X El Sabio S/n (37007), Salamanca, Spain.
Department of Medical Physics and Radiation Protection. Salamanca University Hospital. Paseo de San Vicente 58-182 (37007), Salamanca, Spain.
Heliyon. 2023 Mar 22;9(4):e14780. doi: 10.1016/j.heliyon.2023.e14780. eCollection 2023 Apr.
The use of Computer Aided Detection (CAD) software has been previously documented as a valuable tool to improve specialist training in Radiology. This research assesses the utility of an educational software tool aimed to train residents in Radiology and other related medical specialties and students from Medicine degree. This in-house developed software, called JORCAD, integrates a CAD system based in Convolutional Neural Networks (CNNs) with annotated cases from radiological image databases. The methodology followed for software validation was expert judgement after completing an interactive learning activity. Participants received a theoretical session and a software usage tutorial and afterwards utilized the application in a dedicated workstation to analyze a series of proposed cases of thorax computed tomography (CT) and mammography. A total of 26 expert participants from the Radiology Department at Salamanca University Hospital (15 specialists and 11 residents) fulfilled the activity and evaluated different aspects through a series of surveys: software usability, case navigation tools, CAD module utility for learning and JORCAD educational capabilities. Participants also graded imaging cases to establish JORCAD usefulness for training radiology residents. According to the statistical analysis of survey results and expert cases scoring, along with their opinions, it can be concluded that JORCAD software is a useful tool for training future specialists. The combination of CAD with annotated cases from validated databases enhances learning, offering a second opinion and changing the usual training paradigm. Including software as JORCAD in residency training programs of Radiology and other medical specialties would have a positive effect on trainees' background knowledge.
计算机辅助检测（CAD）软件的使用先前已被记录为提高放射科专科培训的宝贵工具。本研究评估了一种教育软件工具的效用，该工具旨在培训放射科及其他相关医学专业的住院医师以及医学学位的学生。这种内部开发的软件称为JORCAD，它将基于卷积神经网络（CNN）的CAD系统与放射图像数据库中的带注释病例集成在一起。软件验证所遵循的方法是在完成交互式学习活动后进行专家判断。参与者接受了理论课程和软件使用教程，然后在专用工作站上使用该应用程序来分析一系列胸部计算机断层扫描（CT）和乳房X线摄影的病例。萨拉曼卡大学医院放射科的26名专家参与者（15名专家和11名住院医师）完成了该活动，并通过一系列调查评估了不同方面：软件可用性、病例导航工具、用于学习的CAD模块效用以及JORCAD的教育能力。参与者还对影像病例进行评分，以确定JORCAD对培训放射科住院医师的有用性。根据调查结果和专家病例评分的统计分析以及他们的意见，可以得出结论，JORCAD软件是培训未来专科医生的有用工具。CAD与经过验证的数据库中的带注释病例相结合可增强学习效果，提供第二种意见并改变通常的培训模式。将JORCAD软件纳入放射科和其他医学专业的住院医师培训计划将对学员的背景知识产生积极影响。
