Development and validation of an educational software based in artificial neural networks for training in radiology (JORCAD) through an interactive learning activity

Jorge Hernández-Rodríguez; María-José Rodríguez-Conde; José-Ángel Santos-Sánchez; Francisco-Javier Cabrero-Fraile

doi:10.1016/j.heliyon.2023.e14780

Development and validation of an educational software based in artificial neural networks for training in radiology (JORCAD) through an interactive learning activity

Heliyon. 2023 Mar 22;9(4):e14780. doi: 10.1016/j.heliyon.2023.e14780. eCollection 2023 Apr.

Authors

Jorge Hernández-Rodríguez^{1

2}, María-José Rodríguez-Conde³, José-Ángel Santos-Sánchez^{1

4}, Francisco-Javier Cabrero-Fraile¹

Affiliations

¹ Department of Biomedical and Diagnostic Sciences, Faculty of Medicine, University of Salamanca, C/Alfonso X El Sabio S/n (37007), Salamanca, Spain.
² Department of Medical Physics and Radiation Protection. Salamanca University Hospital. Paseo de San Vicente 58-182 (37007), Salamanca, Spain.
³ University Institute of Educational Sciences (IUCE). Grupo de Investigación en InterAcción y ELearning (GRIAL). University of Salamanca, Paseo de Canalejas 169 (37008), Salamanca, Spain.
⁴ Department of Radiology. Salamanca University Hospital. Paseo de San Vicente 58-182 (37007), Salamanca, Spain.

Abstract

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.

Keywords: Computer aided detection; Convolutional neural network; Educational software; Expert validation; Interactive learning activity; Training in radiology.