Evolving radiology continuing medical education: Tapping into the power of online learning.

INTRODUCTION: Global education, particularly Continuing Medical Education (CME) for healthcare professionals, is quickly shifting online. This study assesses the opportunities and challenges of adopting online learning in radiology CME. It explores how radiologists and radiographers have adapted to this digital shift and the changing landscape of radiology education. The study also seeks to envision an innovative future for radiology education. METHODS: A descriptive cross-sectional survey was conducted among radiologists and radiographers working in radiology departments in the United Arab Emirates (UAE). The survey collected data on participant demographics, experiences with CME, sources of CME, and perceptions of online learning. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) software. RESULTS: The survey involved 65 radiologists and 215 radiographers. Findings indicated a significant shift from face-to-face to online CME activities, with 76.9% of radiologists and 70.7% of radiographers utilizing online resources for CME. Concerns about time management, technical issues, and expenses have emerged as challenges for online CME. Participants also highlighted the importance of free-of-charge CME and the value of active participation and anonymity in online discussions. CONCLUSION: Radiology professionals have rapidly adapted to the changing landscape of CME by embracing online learning. While this shift offers greater flexibility and accessibility, technology-related challenges and concerns over time management persist. The study suggests that the future of radiology CME may involve personalized, adaptive, and interactive learning experiences, emphasizing mental well-being and resilience. IMPLICATIONS FOR PRACTICE: Radiology professionals must embrace online CME for continuous skill enhancement, addressing technical challenges, fostering interactive learning environments, and ensuring accessibility to maintain high standards in patient care and medical advancements.

Creating a Sustainable Foundation for IR Services and Training in Sub-Saharan Africa: 5-Year Update on the Road2IR Initiative.

PURPOSE: To evaluate the growth and quality of an interventional radiology (IR) training model designed for resource-constrained settings and implemented in Tanzania as well as its overall potential to increase access to minimally invasive procedures across the region. MATERIALS AND METHODS: IR training in Tanzania began in October 2018 through monthly deployment of visiting teaching teams for hands-on training combined with in-person and remote lectures. A competency-based 2-year Master of Science in IR curriculum was inaugurated at the nation's main teaching hospital in October 2019, with the first 2 classes graduating in 2021 and 2022. Procedural data, demographics, and clinical outcomes were collected and analyzed throughout the duration of this program. RESULTS: From October 2018 to July 2022, 1,595 procedures were performed in Tanzania: 1,236 nonvascular and 359 vascular, all with local fellows as primary interventional radiologists. Of these, 97.2% were technically successful, 95.2% were without adverse events, and 28.9% were performed independently by Tanzanian fellows and faculty with no difference in adverse event and technical success rates (P = .63 and P = .90, respectively), irrespective of procedural class. Ten IR physicians graduated from this program during the study period, followed by another 3 per year going forward. Partner training programs in Uganda and Rwanda mirroring this model commenced in 2023 and 2024, respectively. CONCLUSIONS: The reported training model offers a practical and effective solution to meet many of the challenges associated with the lack of access to IR in sub-Saharan Africa.

Maximizing Mentorship Throughout Your Breast Imaging Career.

Unlike many other subspecialties in radiology, breast radiologists practice in a patient-facing and interdisciplinary environment where team building, communication, and leadership skills are critical. Although breast radiologists can improve these skills over time, strong mentorship can accelerate this process, leading to a more successful and satisfying career. In addition to providing advice, insight, feedback, and encouragement to mentees, mentors help advance the field of breast radiology by contributing to the development of the next generation of leaders. During the mentorship process, mentors continue to hone their listening, problem-solving, and networking skills, which in turn creates a more supportive and nurturing work environment for the entire breast care team. This article reviews important mentorship skills that are essential for all breast radiologists. Although some of the principles apply to all mentoring relationships, ensuring that every breast radiologist has the skills to be both an effective mentor and mentee is key to the future of the profession.

Novel training approach to improve a cohort of radiographers' image interpretation skills of trauma chest radiographs.

INTRODUCTION: Zambia is experiencing a critical shortage of radiologists responsible for interpreting X-ray images. Nine radiologists serve the entire population of over 18 million people. Consequently, referring physicians can receive reports late and often receive X-ray images without radiological reports attached, which may lead to delayed diagnoses and treatment of critically injured patients. This challenge could be alleviated if radiographers could assist with interpreting X-ray images. This study was undertaken to subject a cohort of Zambian radiographers to a training intervention, however, the COVID-19 pandemic necessitated using a novel approach to the intervention by delivering the training mainly through social media but also through face-to-face lectures. METHODS: A cohort of 27 radiographers employed at eight public hospitals in the Copperbelt Province of Zambia undertook a training intervention using face-to-face training and image discussions on the social media WhatsApp® platform. The participants underwent a pre-and post-test in which they were asked to interpret 20 adult trauma CXR images. For the training intervention, the radiographers attended a face-to-face image interpretation lecture, after which they received training images with a radiologist report weekly for eight weeks via the WhatsApp® platform. Participants were encouraged to discuss and pose questions via the platform. RESULTS: The cohort of radiographers (n = 27) showed an improvement in their interpretation skills for trauma CXR images. The interpretation median scores ranged from approximately 82% to 93% in the pre-test and 85% to 97% in the post-test. The Wilcoxon signed-rank tests revealed significant differences in the interpretation ability skills for 12 of the 20 CXR images after the 8-week training, demonstrating the successful implementation of the program. When comparing three categories of radiographers' years of experience (1-5; >5-10; and >10 years), the Kruskal Wallis test could not identify significant differences in the CXR image interpretation skills among the different categories of experience (P = 0.1616). When comparing the interpretation skills of radiographers working at the three different hospital levels (Level 3 with a full-time radiologist and more than ten radiographers; Level 1 and 2 without a full-time radiologist; Level 2 with six to ten radiographers; and Level 1 with five or less radiographers), the Kruskal Wallis test revealed that the level of the hospital where the radiographers were employed significantly influenced their skills to interpret the CXR images (P = 0.0323). CONCLUSION: This type of novel training intervention is urgently required in the Copperbelt Province of Zambia. The results show that the training process was implemented successfully to improve radiographers' image interpretation skills of adult trauma CXR images. IMPLICATIONS FOR PRACTICE: Promoting radiographers' involvement in image interpretation will likely improve imaging services in Zambia, considering the critical shortage of radiologists.

Training the New Radiologists: Approaches for Education.

The field of Radiology is continually changing, requiring corresponding evolution in both medical student and resident training to adequately prepare the next generation of radiologists. With advancements in adult education theory and a deeper understanding of perception in imaging interpretation, expert educators are reshaping the training landscape by introducing innovative teaching methods to align with increased workload demands and emerging technologies. These include the use of peer and interdisciplinary teaching, gamification, case repositories, flipped-classroom models, social media, and drawing and comics. This publication aims to investigate these novel approaches and offer persuasive evidence supporting their incorporation into the updated Radiology curriculum.

Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists.

Segmentation of glioma is crucial for quantitative brain tumor assessment, to guide therapeutic research and clinical management, but very time-consuming. Fully automated tools for the segmentation of multi-sequence MRI are needed. We developed and pretrained a deep learning (DL) model using publicly available datasets A (n = 210) and B (n = 369) containing FLAIR, T2WI, and contrast-enhanced (CE)-T1WI. This was then fine-tuned with our institutional dataset (n = 197) containing ADC, T2WI, and CE-T1WI, manually annotated by radiologists, and split into training (n = 100) and testing (n = 97) sets. The Dice similarity coefficient (DSC) was used to compare model outputs and manual labels. A third independent radiologist assessed segmentation quality on a semi-quantitative 5-scale score. Differences in DSC between new and recurrent gliomas, and between uni or multifocal gliomas were analyzed using the Mann-Whitney test. Semi-quantitative analyses were compared using the chi-square test. We found that there was good agreement between segmentations from the fine-tuned DL model and ground truth manual segmentations (median DSC: 0.729, std-dev: 0.134). DSC was higher for newly diagnosed (0.807) than recurrent (0.698) (p < 0.001), and higher for unifocal (0.747) than multi-focal (0.613) cases (p = 0.001). Semi-quantitative scores of DL and manual segmentation were not significantly different (mean: 3.567 vs. 3.639; 93.8% vs. 97.9% scoring ≥ 3, p = 0.107). In conclusion, the proposed transfer learning DL performed similarly to human radiologists in glioma segmentation on both structural and ADC sequences. Further improvement in segmenting challenging postoperative and multifocal glioma cases is needed.

Systematic training of LI-RADS CT v2018 improves interobserver agreements and performances in LR categorization for focal liver lesions.

AIM: To retrospectively explored whether systematic training in the use of Liver Imaging Reporting and Data System (LI-RADS) v2018 on computed tomography (CT) can improve the interobserver agreements and performances in LR categorization for focal liver lesions (FLLs) among different radiologists. MATERIALS AND METHODS: A total of 18 visiting radiologists and the liver multiphase CT images of 70 hepatic observations in 63 patients at high risk of HCC were included in this study. The LI-RADS v2018 training procedure included three thematic lectures, with an interval of 1 month. After each seminar, the radiologists had 1 month to adopt the algorithm into their daily work. The interobserver agreements and performances in LR categorization for FLLs among the radiologists before and after training were compared. RESULTS: After training, the interobserver agreements in classifying the LR categories for all radiologists were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.053). After systematic training, the areas under the curve (AUCs) for LR categorization performance for all participants were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.062). CONCLUSION: Systematic training in the use of the LI-RADS can improve the interobserver agreements and performances in LR categorization for FLLs among radiologists with different levels of experience.

Competency based curriculum for cardiovascular magnetic resonance: A position statement of the Society for Cardiovascular Magnetic Resonance.

This position statement guides cardiovascular magnetic resonance (CMR) imaging program directors and learners on the key competencies required for Level II and III CMR practitioners, whether trainees come from a radiology or cardiology background. This document is built upon existing curricula and was created and vetted by an international panel of cardiologists and radiologists on behalf of the Society for Cardiovascular Magnetic Resonance (SCMR).

Preparing MSK radiologists of the future.

The future will bring increasing demands for specialty training of MSK radiologists. These will include requirements for further standardization of curriculum, educational methods, and documentation. This will place further burdens (both temporal and financial) on the fellowship programs and may lead to termination of some programs. There is a risk that increasingly bureaucratic training requirements may "crowd out" the basic conceptual education necessary for the continued growth and relevance of the subspecialty. Educators must find ways to incorporate these aspects into programs even when they are not mandated.

Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources

Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.

Radiologists' perceptions of knowledge and training required by radiographers in the interpretation of radiographic images: An explorative study in KwaZulu-Natal province, South Africa.

INTRODUCTION: The health sector of South Africa is burdened by the shortage of radiologists leading to the under-reporting of radiographic images and ultimately mismanagement of patients. Previous studies have recommended training of radiographers in radiographic image interpretation in order to improve the reporting. There is paucity of information regarding the knowledge and training required by radiographers to interpret radiographic images. The purpose of this study was therefore to explore the knowledge and training required by diagnostic radiographers, according to radiologists, for the interpretation of radiographs. METHOD: A qualitative descriptive study employing criterion sampling to select qualified radiologists practicing in the eThekwini district of the KwaZulu Natal province, was conducted. One-on-one and in-depth, semi-structured interviews were used to collect data from three participants. The interviews were not carried out face to face as a result of the Covid 19 pandemic and the regulation of social distancing. This did not permit engagement with research communities. The data from the interviews were analysed using Tesch's eight steps for analysing qualitative data. RESULTS: Findings revealed that radiologists supported the interpretation of radiographic images by radiographers in rural settings, and for the radiographer's scope of practice to be restructured to include the reporting of chest and the musculoskeletal system images. The themes that emerged from the analysis included knowledge, training, clinical competencies and medico-legal responsibilities required by radiographers in the interpretation of radiographic images. CONCLUSION: Although the radiologists support the training of radiographers in the interpretation of radiographic images, radiologists think that the scope of practice should be limited to the interpretation of the chest and musculoskeletal systems in rural areas only.

Insights from nursing for radiologists.

We have observed that former nurses often make very good radiology residents, which leads us to think that nursing offers important lessons to radiology. To be clear, we are not proposing that undergraduate or medical students pursue nursing training so they can enhance their performance in residency - in view of the long course of radiology training, such a suggestion would be highly impractical. But we do believe that aspects of nursing training and practice not typically emphasized in medical education can help radiologists perform better and ultimately promote better patient care.

Creation of a protective space between the rectum and prostate prior to prostate radiotherapy using a hydrogel spacer.

The placement of a polyethylene glycol (PEG) hydrogel spacer is a recently developed technique employed to reduce the radiation dose administered to the rectum during prostate radiotherapy. This procedure has been adopted by urologists and radiation oncologists involved in transperineal prostate biopsy and brachytherapy, and more recently by radiologists with experience in transperineal prostate procedures. Radiologists should be familiar with the product, which may be encountered on computed tomography (CT) or magnetic resonance imaging (MRI). Radiologists may wish to become involved in the delivery of this increasingly utilised procedure. This review familiarises radiologists with the technique and risks and benefits of the use of transperineal delivery of hydrogel spacers with imaging examples.

Interobserver variability in the evaluation of primary graft dysfunction after lung transplantation: impact of radiological training and analysis of discordant cases.

PURPOSE: Radiologic criteria for the diagnosis of primary graft dysfunction (PGD) after lung transplantation are nonspecific and can lead to misinterpretation. The primary aim of our study was to assess the interobserver agreement in the evaluation of chest X-rays (CXRs) for PGD diagnosis and to establish whether a specific training could have an impact on concordance rates. Secondary aim was to analyze causes of interobserver discordances. MATERIAL AND METHODS: We retrospectively enrolled 164 patients who received bilateral lung transplantation at our institution, between February 2013 and December 2019. Three radiologists independently reviewed postoperative CXRs and classified them as suggestive or not for PGD. Two of the Raters performed a specific training before the beginning of the study. A senior thoracic radiologist subsequently analyzed all discordant cases among the Raters with the best agreement. Statistical analysis to calculate interobserver variability was percent agreement, Cohen's kappa and intraclass correlation coefficient. RESULTS: A total of 473 CXRs were evaluated. A very high concordance among the two trained Raters, 1 and 2, was found (K = 0.90, ICC = 0.90), while a poorer agreement was found in the other two pairings (Raters 1 and 3: K = 0.34, ICC = 0.40; Raters 2 and 3: K = 0.35, ICC = 0.40). The main cause of disagreement (52.4% of discordant cases) between Raters 1 and 2 was the overestimation of peribronchial thickening in the absence of unequivocal bilateral lung opacities or the incorrect assessment of unilateral alterations. CONCLUSION: To properly identify PGD, it is recommended for radiologists to receive an adequate specific training.

A machine and human reader study on AI diagnosis model safety under attacks of adversarial images.

While active efforts are advancing medical artificial intelligence (AI) model development and clinical translation, safety issues of the AI models emerge, but little research has been done. We perform a study to investigate the behaviors of an AI diagnosis model under adversarial images generated by Generative Adversarial Network (GAN) models and to evaluate the effects on human experts when visually identifying potential adversarial images. Our GAN model makes intentional modifications to the diagnosis-sensitive contents of mammogram images in deep learning-based computer-aided diagnosis (CAD) of breast cancer. In our experiments the adversarial samples fool the AI-CAD model to output a wrong diagnosis on 69.1% of the cases that are initially correctly classified by the AI-CAD model. Five breast imaging radiologists visually identify 29%-71% of the adversarial samples. Our study suggests an imperative need for continuing research on medical AI model's safety issues and for developing potential defensive solutions against adversarial attacks.

Holistic processing only? The role of the right fusiform face area in radiological expertise.

Radiologists can visually detect abnormalities on radiographs within 2s, a process that resembles holistic visual processing of faces. Interestingly, there is empirical evidence using functional magnetic resonance imaging (fMRI) for the involvement of the right fusiform face area (FFA) in visual-expertise tasks such as radiological image interpretation. The speed by which stimuli (e.g., faces, abnormalities) are recognized is an important characteristic of holistic processing. However, evidence for the involvement of the right FFA in holistic processing in radiology comes mostly from short or artificial tasks in which the quick, 'holistic' mode of diagnostic processing is not contrasted with the slower 'search-to-find' mode. In our fMRI study, we hypothesized that the right FFA responds selectively to the 'holistic' mode of diagnostic processing and less so to the 'search-to-find' mode. Eleven laypeople and 17 radiologists in training diagnosed 66 radiographs in 2s each (holistic mode) and subsequently checked their diagnosis in an extended (10-s) period (search-to-find mode). During data analysis, we first identified individual regions of interest (ROIs) for the right FFA using a localizer task. Then we employed ROI-based ANOVAs and obtained tentative support for the hypothesis that the right FFA shows more activation for radiologists in training versus laypeople, in particular in the holistic mode (i.e., during 2s trials), and less so in the search-to-find mode (i.e., during 10-s trials). No significant correlation was found between diagnostic performance (diagnostic accuracy) and brain-activation level within the right FFA for both, short-presentation and long-presentation diagnostic trials. Our results provide tentative evidence from a diagnostic-reasoning task that the FFA supports the holistic processing of visual stimuli in participants' expertise domain.

Medical app minefield: radiologists use of medical apps for education and reporting and do they require regulation?

AIM: To evaluate the use of apps in radiology and consider advised changes to practice. MATERIALS AND METHODS: A survey was conducted of all radiology consultants and specialty trainees within Devon and Cornwall. The responses were collated, including the list of all medical applications used. These were assessed using the Medicine & Healthcare Products Regulatory Agency (MHRA) "Medical device stand-alone software including apps" guidance. RESULTS: The response rate was 88/150 (59%) radiologists who responded with the majority 48/88 (54.4%) using apps. Forty-four of 66 (67%) states that they did not assess the reliability or accuracy of these devices prior to use with 71/81 (88%) indicating that they were unaware of any regulations. Thirty-three items were identified of which 27 functioning apps were identified and three of these were considered medical devices and did not have complete and recognisable CE marking as required by the MHRA. CONCLUSION: This study highlights that application use is widespread. The vast majority of these applications are not considered medical devices; however, there are some devices that, according to the MHRA flow chart, are used in a way that classifies them as medical devices and should therefore be CE marked. This highlights the need for guidance and regulation of the medical application market with recommendations provided.