Open source software e-learning system to improve medical image interpretation in young thoracic surgeons

Abstract

Background We evaluated the efficacy of the e-learning system for improving young thoracic surgeons’ interpretations of chest computed tomography (CT) images.

Methods We established an e-learning system for medical image interpretation using open source software that can be accessed from multiple sites. We used virtual network computing (VNC), a graphical desktop sharing protocol, to observe the remote server desktop screen. OsiriX, an image-processing package, was installed on the server to share medical images. Five young thoracic surgeons (3-8 years of experience) were instructed to interpret a preoperative chest CT from a remote partner hospital. Chest CTs were extracted from remote hospitals, anonymized, and uploaded to the secure server. The participants accessed the server via high-speed Internet secured with a virtual private network. The young surgeons then created a 3-dimensional configuration of the pulmonary vessels before procedure using the 2-dimensional scans. Next, 5 expert thoracic surgeons ranked the renderings on a 10-point scale. The edited surgical video with the actual pulmonary configuration was uploaded to the web server, and the young thoracic surgeons watched the video as a self-education module. They also commented on their peers’ preoperative images of the pulmonary vessels.

Results The scores of the 5 interpretations and drawings of pulmonary vessel branching patterns of patients with lung cancer undergoing lobectomy increased with years of experience.

Conclusions The young thoracic surgeons virtually learned lobectomy using an e-learning system. We believe this virtual resource will help young thoracic surgeons accumulate greater experience.