The impact of gamification and potential of kaizen in radiology education

Gamification is an emerging tool in medical education that has been increasingly adopted in the field of radiology. The purpose of this non-systematic review is to explore the use of gamification in medical education with a particular focus on new generations of learners and radiology education. This manuscript begins by examining the effectiveness of gamification in improving learning outcomes in medicine and radiology. Future research recommendations and the potential impact of gamification on new learners are discussed. Finally, this review provides insight into a gaming platform, Kaizen, as a promising approach to enhance education by improving motivation and increasing interest in radiology knowledge.

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

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.

Gamification: Basic concepts and applications in radiology

Gamification is the use of elements from games in non-game environments, such as education. It is an alternative educational focus that promotes students' motivation and participation in the learning process. Gamification had proven effective in training health professionals and can play an important role in diagnostic radiology training, both at the undergraduate and postgraduate levels. Gamification activities can be carried out in real environments, such as classrooms or session rooms, but there are also interesting online modalities that favor remote access and user management. The possibilities of gamification in virtual worlds to teach undergraduate students radiology are very promising and deserve to be explored in training residents. This article aims to review general concepts in gamification and to present the main types of gamification used in medical training, pointing out its applications and advantages and disadvantages and emphasizing experiences in radiology education.

Radiography education in 2022 and beyond - Writing the history of the present: A narrative review

OBJECTIVES

The COVID-19 pandemic had a major effect on teaching and learning. This study aimed to describe a range of teaching, learning, and assessment strategies related to radiography education which have become more common due to the pandemic through a narrative literature review.

KEY FINDINGS

Educational change in radiography was accelerated by the disruption caused by the pandemic. Changes included the site and mode of teaching and conducting of assessment. While some of the digital transformation trends were introduced before the pandemic, others were further amplified during this period of time. Alternative solutions such as virtual reality technology, gamification, and technology-enhanced learning were especially salient and have the potential to mitigate challenges brought about by the pandemic. The use of technology in the clinical setting, in assessment, and to facilitate feedback, are important tools for improving learners' clinical skills performance. Collectively, these digital technologies can maximise learning and support mastery of knowledge, skills and attitudes.

CONCLUSION

The pandemic has cast a new light on existing methodologies and pedagogies in education. This review suggests that digital technology is shaping teaching and learning within radiography education and also that educators cannot ignore this digital shift. With the digital trajectory, it would be highly useful to transform approaches to education within radiography to support learning as radiography education moves towards the new normal era.

IMPLICATIONS FOR PRACTICE

Digital technology in education can help improve the learning experience for learners but educators need to be equipped with the technological skills and be adaptable to these changes. Continual sharing of experiences and knowledge among radiography educators is essential. Safety nets need to be in place to ensure digital inclusiveness and that no learner gets left behind due to the digital divide in education.

RADHunters: gamification in radiology perceptual education

Purpose

Gamification is used in several fields as an adjunct to standard educational methods but has found limited application in radiology to date. Gamification may be useful for teaching radiology skills typically acquired through experience, such as perceptual skills. The goal of our study is to use a gamified radiology workstation to teach skills related to identification of pulmonary nodules and evaluate for changes in trainee performance.

Approach

We constructed a game called RADHunters to teach perceptual skills related to identification of pulmonary nodules on chest radiographs. Control and experimental groups were tasked with identifying nodules on chest radiographs on two sets of cases. The experimental group received gamified training for nodule identification using RADHunters between case sets, while the control group did not. Performance at nodule identification, localization, and confidence were compared. A poststudy survey was administered to assess for participants’ thoughts about the gamified nodule detection training.

Results

Survey responses were very positive with p-values for all survey responses <0.001, indicating subjects felt this training was beneficial. Experimental and control groups had a statistically significant improvement in their ability to identify and localize nodules with p-values <0.05. There was no significant difference between control and experimental groups. Neither group showed a statistically significant increase in their confidence in nodule localization.

Conclusions

Perceptual training using gamification may be a useful adjunct to conventional methods of radiology education.

A Gamified Social Media-Based Head and Neck Radiology Education Initiative of the American Society of Head and Neck Radiology: Viewership and Engagement Trends at 3 Years

BACKGROUND AND PURPOSE

Social media has made inroads in medical education. We report the creation and 3-year (2018-2021) longitudinal assessment of the American Society of Head and Neck Radiology Case of the Week (#ASHNRCOTW), assessing viewership, engagement, and impact of the coronavirus disease 2019 (COVID-19) pandemic on this Twitter-based education initiative.

MATERIALS AND METHODS

Unknown cases were tweeted from the American Society of Head and Neck Radiology account weekly. Tweet impressions (number of times seen), engagements (number of interactions), and new followers were tabulated. A social media marketing platform identified worldwide distribution of Twitter followers. Summary and t test statistics were performed.

RESULTS

#ASHNRCOTW was highly visible with 2,082,280 impressions and 203,137 engagements. There were significantly greater mean case impressions (9917 versus 6346), mean case engagements (1305 versus 474), case engagement rates (13.06% versus 7.76%), mean answer impressions (8760 versus 5556), mean answer engagements (908 versus 436), answer engagement rates (10.38% versus 7.87%), mean total (case + answer) impressions (18,677 versus 11,912), mean total engagements (2214 versus 910), and total engagement rates (11.79% versus 7.69%) for cases published after the pandemic started (all P values < .001). There was a significant increase in monthly new followers after starting #ASHNRCOTW (mean, 134 versus 6; P < .001) and significantly increased monthly new followers after the pandemic started compared with prepandemic (mean, 178 versus 101; P = .003). The American Society of Head and Neck Radiology has 7564 Twitter followers throughout 130 countries (66% outside the United States).

CONCLUSIONS

Social media affords substantial visibility, engagement, and global outreach for radiology education. #ASHNRCOTW viewership and engagement increased significantly during the COVID-19 pandemic.

Gamification in Radiology Training Module Developed During the Society for Imaging Informatics in Medicine Annual Meeting Hackathon

The purpose of this manuscript is to report our experience in the 2021 SIIM Virtual Hackathon, where we developed a proof-of-concept of a radiology training module with elements of gamification. In the 50 h allotted in the hackathon, we proposed an idea, connected with colleagues from five different countries, and completed an operational proof-of-concept, which was demonstrated live at the hackathon showcase, competing with eight other teams. Our prototype involved participants annotating publicly available chest radiographs of patients with tuberculosis. We showed how we could give experience points to trainees based on annotation precision compared to ground truth radiologists' annotation, ranked in a live leaderboard. We believe that gamification elements could provide an engaging solution for radiology education. Our project was awarded first place out of eight participating hackathon teams.

Improving Radiology Trainees' Perception Using Where's Waldo?

RATIONALE AND OBJECTIVES

Perception is an essential skill leading to expertise in diagnostic radiology. We determined if practicing "Where's Waldo?" images improves accuracy and speed with which first and second year radiology residents detect abnormalities on chest radiographs (CXRs).

MATERIALS AND METHODS

Residents at three institutions were pretested using 50 CXRs, identifying locations of potential abnormalities. They were then split into trained (examining 7 "Where's Waldo?" images over three weeks) and control groups (no "Where's Waldo?"). They were then re-tested on the 50 CXRs. At one site, visual search parameters were acquired. Data were analyzed with repeated measures ANOVAs.

RESULTS

There was no significant difference in performance for trained vs control (F = 0.622, p = 0.436), with both improving significantly on post-test (F = 4.72, p = 0.037). Session time decreased significantly for both groups from pre to post-test (F = 81.47, p < 0.0001) and the decrease was significantly more (F = 31.59, p < 0.0001) for the trained group than the control group as well as for PGY with PGY3 having a larger average decrease in session time than PGY2. Eye-tracking data also showed significant increases in per image search efficiency with training.

CONCLUSION

Practicing "Where's Waldo?" or similar nonradiology search tasks may facilitate the acquisition of radiology image search but not detection skills, impacting reading efficiency more than detection accuracy.

A narrative review of e-learning in professional education of healthcare professionals in medical imaging and radiation therapy

OBJECTIVES

This literature review attempts to explore the characteristics of e-learning tools used to develop the qualifications and skills of healthcare professionals in medical imaging and radiation therapy, and to promote the effectiveness and acceptance of e-learning through highlighting the outcomes of its implementation where applicable.

KEY FINDINGS

From the literature search in the PubMed and ResearchGate databases we concluded to 21 articles, which were included in the qualitative synthesis. Acceptance of e-learning tools was confirmed. Also, e-learning can be part of healthcare professionals' blended learning. The acquisition of new or improvement of existing knowledge, the improvement of clinical skills and the increase of the self-confidence of healthcare professionals in their daily practice were recorded, as outcomes of the e-learning implementation. The importance of human-computer interaction for the comprehension of theoretical concepts and practical aspects using multimedia was also captured. No significant findings emerged among the 21 articles against the adoption of the e-learning for the training of healthcare professionals. The Internet is the channel used for synchronous and asynchronous interaction of trainees with instructors.

CONCLUSIONS

We concluded that e-learning is an attractive training method, equally or occasionally more effective than the traditional educational methods for the lifelong training of healthcare professionals in the field of medical imaging and radiation therapy. Also, many collaborative web-based applications provide the necessary means to build an e-learning program, according to the training needs of each professional team.

IMPLICATIONS FOR PRACTICE

This new knowledge corroborates the perspective of e-learning beneficial contribution to remote interaction and collaboration of healthcare professionals in medical imaging and radiation therapy. Collaborative web-based tools are already available to decision makers and stakeholders, who want to develop an e-learning program.

Using Online Survey Software to Enhance Radiology Learning

RATIONALE AND OBJECTIVES

Online educational modules can augment radiology learning by creating opportunities to interact with images in more dynamic ways than with static presentation of images in lectures or journal articles. Building these modules on an online survey platform allows for quantitative assessment and learner feedback, without requiring programming knowledge or need for new website creation.

MATERIALS AND METHODS

Interactive online tutorials were built on a web-based survey platform (Qualtrics, Provo, Utah) accessible by computer or mobile device to teach radiology imaging findings of selected high-morbidity diagnoses. Topics included congenital-type internal hernias (module 1), acute appendicitis in the pregnant patient (module 2), and unintentionally retained surgical instruments (RSI; module 3). Modules consisted of pretest, educational module, and post-test components. For modules 1 and 2, graphics interchange formats were utilized to show CT and MRI image stacks for the diagnosis of congenital-type internal hernias and acute appendicitis in pregnant patients, respectively. For module 3, the "Heat Map" format was chosen to showcase intraoperative radiograph cases, which allowed participants to click on the potential RSI in the image. Pre- and post-test scores were evaluated. To determine statistical significance, an alpha level of 0.05 was utilized.

RESULTS

Module 1 (Internal Hernia): Twenty-one radiology trainees completed the module. The mean pretest score was 3.66 (±1.13) points out of a total 6 possible points (61%), compared to 4.52 (±1.03) points on the post-test (75%). This was a statistically significant increase on the post-test of 0.87 points (95% CI [confidence interval] 0.36, 1.38), t(20) = 3.53, p= 0.002. Module 2 (MR Appendicitis): Seventeen radiology trainees completed the module. The mean pretest score was 3.18 (±1.42) points out of a total 6 possible points (53%), compared to 5.12 (±0.86) points on the post-test (85%). This was a statistically significant increase on the post-test of 1.94 points (95% CI 1.12, 2.76), t(16) = 5.00, p< 0.001. Module 3 (RSI): One hundred seven participants completed the module. The mean pretest score was 3.60 (±1.53) points out of a total 6 possible points (60%), compared to 4.54 (±1.36) points on the post-test (76%). This was a statistically significant increase on the post-test of 0.94 points (95% CI 0.67, 1.21), t(106) = 6.84, p< 0.001.

CONCLUSION

An online survey platform can be used to build interactive education modules. Post-test scores significantly improved from pretest scores with these educational modules.

Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

Patients with multiple sclerosis (MS) have heterogeneous clinical presentations, symptoms, and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using MRI. First, development of validated MS-specific image analysis methods can be boosted by verified reference, test, and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic, and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy, or functional network changes) to large multidomain datasets (imaging, cognition, clinical disability, genetics). After reviewing data sharing and artificial intelligence, we highlight 3 areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging, and the understanding of MS.

Do Video Games Predict an Early Advanced Capacity to Learn Interventional Radiology Skills?

PURPOSE

To elucidate the relationship between video game (VG) play and interventional radiology (IR) technical skills in medical students.

MATERIALS AND METHODS

Twenty medical students recruited at our institution's IR symposium completed a survey to ascertain demographics and prior VG experience, then participated in a 3-part trial of skills assessing IR and VG skill and visuospatial aptitude (VSA). IR skill was evaluated via an endovascular simulation task, VG skill by performance on three separate VGs, and VSA using the Cube Comparison test. Regression analysis was tested the strength of relationship between IR skill and VG experience, VG skill, and VSA, respectively, and participants were stratified by IR skill to top and bottom halves for survey-response comparison.

RESULTS

There was no correlation between either VG skill or visuospatial aptitude and IR skill (r = -0.22, p = 0.35; and r = 0.14, p = 0.57). Greater number of years playing VGs correlated with superior IR skill (Spearman's rho=-0.45, p<0.05). Students who selected IR as their specialty of interest had extensive VG experience, playing for > 15 years (n = 4, 80%), at least 10 hours per week at their peak (n = 3, 60%), and reported being either "skilled" or "highly skilled" at VGs (n = 3, 60%).

CONCLUSIONS

In our study, though limited by power, number of years playing VGs correlated positively with IR skills in medical students. Prior VG experience may predict an early advanced capacity to learn IR skills and an interest in the specialty.

Development of a volumetric pancreas segmentation CT dataset for AI applications through trained technologists: a study during the COVID 19 containment phase

PURPOSE

To evaluate the performance of trained technologists vis-à-vis radiologists for volumetric pancreas segmentation and to assess the impact of supplementary training on their performance.

METHODS

In this IRB-approved study, 22 technologists were trained in pancreas segmentation on portal venous phase CT through radiologist-led interactive videoconferencing sessions based on an image-rich curriculum. Technologists segmented pancreas in 188 CTs using freehand tools on custom image-viewing software. Subsequent supplementary training included multimedia videos focused on common errors, which were followed by second batch of 159 segmentations. Two radiologists reviewed all cases and corrected inaccurate segmentations. Technologists' segmentations were compared against radiologists' segmentations using Dice-Sorenson coefficient (DSC), Jaccard coefficient (JC), and Bland-Altman analysis.

RESULTS

Corrections were made in 71 (38%) cases from first batch [26 (37%) oversegmentations and 45 (63%) undersegmentations] and in 77 (48%) cases from second batch [12 (16%) oversegmentations and 65 (84%) undersegmentations]. DSC, JC, false positive (FP), and false negative (FN) [mean (SD)] in first versus second batches were 0.63 (0.15) versus 0.63 (0.16), 0.48 (0.15) versus 0.48 (0.15), 0.29 (0.21) versus 0.21 (0.10), and 0.36 (0.20) versus 0.43 (0.19), respectively. Differences were not significant (p > 0.05). However, range of mean pancreatic volume difference reduced in the second batch [- 2.74 cc (min - 92.96 cc, max 87.47 cc) versus - 23.57 cc (min - 77.32, max 30.19)].

CONCLUSION

Trained technologists could perform volumetric pancreas segmentation with reasonable accuracy despite its complexity. Supplementary training further reduced range of volume difference in segmentations. Investment into training technologists could augment and accelerate development of body imaging datasets for AI applications.