Professional identity formation (PIF) occurs both individually (the psychological development of the person), and collectively (the social interaction of the individual with their training environment). The purpose of this study is to assess aspects of medical trainees' personal identity, particularly those that are underrepresented in medicine, that have the most profound impact on PIF to identify opportunities to improve the educational environment through informed mentorship throughout medical training. Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Sompayrac Anne, Lopez-rippe Julian, Reid Janet

Keywords: Professional Identity Formation, medical trainee, individual identity

The purpose of this educational exhibit is to present a systematic 4-sequence hanging protocol and search pattern as an effective method to initially review a pediatric MR enterography (MRE) study prior to thorough interpretation. By analyzing frequently referenced sequences in MRE reports with positive inflammatory bowel disease findings, we identified novel 4-sequence hanging protocol: coronal T2-weighted HASTE (for anatomic orientation), axial fat-saturated fluid-sensitive sequence (for bowel wall thickening/edema), axial diffusion-weighted high b-value sequences (for bowel inflammation and complications), and coronal gradient-recall echo T1-weighted post-contrast (for enhancement). This structured 4-sequence hanging protocol highlights the most pertinent findings and allows for rapid first-pass evaluation to orient novice readers. The exhibit provides an efficient framework for radiology trainees to streamline review of pediatric MREs before proceeding to full detailed interpretation. Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Chaker Salama, Reid Janet, Lopez-rippe Julian

Keywords: inflammatory bowel disease, magnetic resonance enterography, hanging protocol

Many academic radiology leaders now equally value contributions to education and research in their departments. Whereby research generates publications and grants, the accolades from education may be less completely captured in a curriculum vitae (CV). We present a system that collects, organizes, and showcases a real-time record of the radiologist’s education portfolio to guide faculty development and academic promotion. Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Turner Steven, Lopez-rippe Julian, Reid Janet

Keywords: Radiology Education Portfolio, Faculty Development, Academic Promotion

Our primary goal is to study individual narratives of career pathways to provide insight into the crucial turning points in one’s journey from becoming to being a physician. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Lopez Rippe Julian, Velez Florez Maria, Sompayrac Anne, Reid Janet

Keywords: professional development, curriculum

Despite the global surge in adoption of Positron Emission Tomography Magnetic Resonance Imaging (PET/MRI) in healthcare, a formal educational framework for pediatric PET/MRI is lacking. The study aimed to conduct and report a needs assessment of pediatric radiology fellows, informing the development and implementation of a peer-reviewed pediatric PET/MRI curriculum and library. Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Lopez-rippe Julian, Alkhulaifat Dana, Sompayrac Anne, Amiruddin Raisa, Hamel Eva, States Lisa, Reid Janet

Keywords: Radiology education, Radiology curriculum, Positron Emission Tomography Magnetic Resonance Imaging

Artificial intelligence (AI) applications for radiology have undergone exponential growth in recent years, owing to the development of large datasets for use in machine learning algorithms and technological advancements in the field of imaging informatics. However, the advancement of AI algorithms in pediatric radiology has lagged behind adult applications. Currently, only seven commercially-available AI algorithms have received FDA approval for use in the pediatric population [1]. One of the major factors limiting the use of AI in pediatric radiology is the lack of the requisite large pediatric imaging datasets. In AI research and implementation, pediatric radiologists serve as stewards of imaging data. As such, pediatric radiologists should be trained in AI data management, including best practices for the selection, curation, de-identification, and storage of radiology data. Since a necessary first step in the development of AI algorithms requires the curation of large datasets, pediatric radiologists should have a basic understanding of how to archive imaging data for AI research and validation. However, few resources are currently available to provide targeted education for pediatric radiologists with respect to AI data curation.. The aim of this educational exhibit is to provide an educational resource specifically for pediatric radiologists which teaches best practices for data management in AI research, including the selection of patient cohorts, data anonymization techniques, image annotation and segmentation methods, and data storage tools. This exhibit integrates our professional experience, with a thorough literature review of prior AI research, into an educational resource to teach data science methodologies for the management of AI research and clinical implementation to the pediatric radiology community. References 1. AI Central. https://aicentral.acrdsi.org/. Accessed 18 Oct 2022 Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Alkhulaifat Dana, Rafful Patricia, Lopez Rippe Julian, Khalkhali Vahid, Welsh Michael, Wieczkowski Sydney, Reid Janet, Sotardi Susan

Keywords: Artificial Intelligence, Education, Data Management

In recent years, artificial intelligence (AI) applications for radiology have undergone exponential growth. AI has the potential to assist pediatric radiologists in several respects, including identification of various pathologies and prioritization of studies with urgent imaging findings. Although image analysis is an important application of AI, there are many additional use cases where AI can assist radiologists, including radiology operations and quality analysis. Therefore, it is critical that radiologists familiarize themselves with the wide range of AI methodologies and applications. Although efforts have been made to create formal education for radiologists, currently available resources typically rely upon didactic lectures, without real-life context for the technology. However, case-based education is a principal component of radiology education. Furthermore, prior research has demonstrated that trainees and radiologists are interested in case-based approaches for learning AI [1]. Thus, the purpose of this educational exhibit is to provide an informative resource specifically for pediatric radiologists which utilizes a case-based learning approach. This resource uses 5 cases intended to demonstrate the variety of uses of AI in pediatric radiology, while illustrating the underlying process of AI model selection and development. Specifically, these cases will be selected to encompass AI topics including: image classification, lesion segmentation, natural language processing for imaging reports, recommender systems to enhance radiologist practice and predictive modeling for radiology operations research. This approach allows us to explore decision-points in AI model building, while examining potential pitfalls that may arise. This educational exhibit was developed based on a thorough literature review of prior AI research combined with our professional experience, and aims to provide educational resource intended to teach AI model selection and development to pediatric radiologists. References 1. Velez-Florez MC, Ghosh A, Patton D, et al (2022) Artificial intelligence curriculum needs assessment for a pediatric radiology fellowship program: what, how, and why? Acad Radiol. https://doi.org/10.1016/j.acra.2022.04.026 Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Alkhulaifat Dana, Rafful Patricia, Lopez Rippe Julian, Khalkhali Vahid, Welsh Michael, Wieczkowski Sydney, Reid Janet, Sotardi Susan

Keywords: Artificial Intelligence, Case-Based Learning, Education

Teaching fluoroscopy skills remains an ongoing challenge in pediatric radiology education. Radiologists must be competent to perform a wide range of fluoroscopy procedures and are often required to teach these clinical skills to their peers, junior staff, and students. Teaching procedural skills through frameworks, observation, and feedback, with opportunities for repeated practice, assists in the learner’s acquisition and retention of skills. Fluoroscopy presents patient safety and ethical challenges as “practicing” this skill on patients requires ionizing radiation exposure and often invasive procedures. Through this educational exhibit, we describe a proposed program to improve skill performance, determine competency, and provide feedback. To improve voiding cystourethrogram (VCUG) training two patient models were created for bladder catheterization with different grades of vesicoureteral reflux using 3D printing and silicon rubber. Additional educational materials, including videos and graphical representations, were created to better help the learner understand the steps of the VCUG protocol. A three-component curriculum included 1) Knowledge (indications, contraindications, complications); 2) Communication (with patient and family, with fluoroscopy team); and 3) Performance of the skill (preparation before commencing, steps and dexterity, immediate aftercare of the patient). We modeled our evaluation of the learner from Peyton’s four-step approach to skills teaching (Demonstration, Deconstruction, Formulation, and Performance) to teach the physical performance of the fluoroscopy procedure. Feedback was provided to the learners using the Pendleton Feedback Model. Finally, competency was assessed using the Framework for clinical assessment developed by Miller. This educational exhibit aims to provide radiologists in training with an alternative learning curriculum to better understand and evaluate the steps of the VCUG and its performance on patients in a simulated setting before being performed on patients. Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Veselis Clinton, Venkatakrishna Shyam Sunder, Silvestro Elizabeth, Bennett Brittany, Srinivasan Abhay, Acord Michael, Sze Raymond, Reid Janet, Anupindi Sudha

Keywords: Voiding cystourethrogram, VCUG, Fluoroscopy

Artificial intelligence (AI) has the potential to improve many aspects of pediatric radiology. AI solutions have emerged to perform a wide range of tasks, including facilitating the workload of radiologists, improving image quality, performing motion correction, and reducing contrast dose. While a myriad of courses for learning AI are available, most resources lack the necessary radiology perspective required for clinical application. Few interactive resources are available to teach a systematic approach for pediatric radiologists when analyzing AI literature. Since AI methodologies in pediatric radiology research fundamentally differ from classical statistical analyses, an educational poster dedicated to interpreting pediatric radiology AI literature would assist in bridging this gap. The purpose of this education exhibit is to provide a resource for pediatric radiologists that teaches a systematic approach for the interpretation of AI research publications. Knowledge delivery will be made by eLearning slide-based presentation. The major topics regarding AI literature will focus on: problem identification; data curation; data annotation, artificial intelligence models; validation and performance. Next, we review prior literature on important criteria for AI publications, including Checklist for Artificial Intelligence in Medical Imaging (CLAIM), the AI version of the Standards for Reporting of Diagnostic Accuracy Studies (STARD-AI), Transparent Report of Multivariable Prediction Model of Individual Prognosis and Diagnosis for AI (TRIPOD-AI) and Prediction Model Risk of Bias Assessment Tool for AI (PROBAST-AI). Lastly, we present these criteria in the context of pediatric AI research applications. AI research methodology in pediatric radiology differs from classical research methodologies. Therefore, this education exhibit is intended to teach the systematic interpretation of the primary AI literature to pediatric radiologists in order to translate scientific knowledge into meaningful clinical information. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Rafful Patricia, Alkhulaifat Dana, Lopez Rippe Julian, Khalkhali Vahid, Welsh Michael, Venkatakrishna Shyam Sunder, Wieczkowski Sydney, Reid Janet, Sotardi Susan

Keywords: Artificial Intelligence, Radiology, Research