Gender disparity in academic advancement: exploring differences among adult and pediatric radiologists.

BACKGROUND: Gender imbalance in research output and academic rank in academic radiology is well-documented and long-standing. Less is known regarding this imbalance among pediatric radiologists. OBJECTIVE: To characterize gender differences for academic rank and scholarly productivity of pediatric radiologists relative to adult radiologists. MATERIALS AND METHODS: During summer 2021, faculty data for the top 10 U.S. News & World Report ranked adult radiology programs and the top 12 largest pediatric hospital radiology departments were collected. Information regarding self-reported gender, age, years of practice and academic rank was accessed from institutional websites and public provider databases. The h-index and the number of publications were acquired via Scopus. Group comparisons were performed using Mann-Whitney and chi-square tests. RESULTS: Three hundred and sixty-four (160 women) pediatric and 1,170 (468 women) adult radiologists were included. Compared to adult radiologists, there were significantly fewer pediatric radiologists in advanced ranks (associate or full professor) (P = 0.024), driven by differences between male (P = 0.033) but not female radiologists (P = 0.67). Among pediatric radiologists, there was no significant difference in years in practice (P = 0.29) between males and females. There also was no significant difference in academic rank by gender (P = 0.37), different from adult radiology where men outnumber women in advanced ranks (P < 0.001). Male pediatric radiologists displayed higher academic productivity (h-index: 9.0 vs. 7.0; P = 0.01 and number of publications: 31 vs. 18; P = 0.003) than their female colleagues. CONCLUSION: Academic pediatric radiology seems to have more equitable academic advancement than academic adult radiology. Despite similar time in the workforce, academic output among female pediatric radiologists lags that of their male colleagues.

Leveraging diversity, equity and inclusion for promoting wellness in the radiology workplace.

Over the last decade, health care professionals in the field of radiology have experienced increasing rates of burnout. A study in 2017 showed high prevalence of burnout in pediatric radiology, and other studies have identified several drivers for burnout. An important factor in promoting wellness and mitigating burnout is leveraging diversity, equity and inclusion in the workplace. This manuscript highlights the importance of diversity in high-functioning teams as well as the critical role of equity and inclusion in the workplace to help create an organization where people belong and can effectively succeed.

Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand Radiographs.

PURPOSE: To develop a deep learning approach to bone age assessment based on a training set of developmentally normal pediatric hand radiographs and to compare this approach with automated and manual bone age assessment methods based on Greulich and Pyle (GP). METHODS: In this retrospective study, a convolutional neural network (trauma hand radiograph-trained deep learning bone age assessment method [TDL-BAAM]) was trained on 15 129 frontal view pediatric trauma hand radiographs obtained between December 14, 2009, and May 31, 2017, from Children's Hospital of New York, to predict chronological age. A total of 214 trauma hand radiographs from Hasbro Children's Hospital were used as an independent test set. The test set was rated by the TDL-BAAM model as well as a GP-based deep learning model (GPDL-BAAM) and two pediatric radiologists (radiologists 1 and 2) using the GP method. All ratings were compared with chronological age using mean absolute error (MAE), and standard concordance analyses were performed. RESULTS: The MAE of the TDL-BAAM model was 11.1 months, compared with 12.9 months for GPDL-BAAM (P = .0005), 14.6 months for radiologist 1 (P < .0001), and 16.0 for radiologist 2 (P < .0001). For TDL-BAAM, 95.3% of predictions were within 24 months of chronological age compared with 91.6% for GPDL-BAAM (P = .096), 86.0% for radiologist 1 (P < .0001), and 84.6% for radiologist 2 (P < .0001). Concordance was high between all methods and chronological age (intraclass coefficient > 0.93). Deep learning models demonstrated a systematic bias with a tendency to overpredict age for younger children versus radiologists who showed a consistent mean bias. CONCLUSION: A deep learning model trained on pediatric trauma hand radiographs is on par with automated and manual GP-based methods for bone age assessment and provides a foundation for developing population-specific deep learning algorithms for bone age assessment in modern pediatric populations.Supplemental material is available for this article.© RSNA, 2020See also the commentary by Halabi in this issue.

Information availability on emergency radiology fellowship websites: current state and paths to improvement.

PURPOSE: The purpose of this study is to detail the current state of the websites of each of the currently established emergency radiology fellowship programs within the USA, in terms of publicly available information. The goal of the project is to present data for emergency radiology fellowship programs to tailor that information to attract those most interested in the pursuit of an emergency radiology fellowship position. METHODS: Emergency radiology fellowship programs were identified using the American Society of Emergency Radiology website and recent published literature. The website for each program was evaluated for the presence or absence of 23 discrete areas of information. Additionally, information from a prior study evaluating radiology resident's opinions on desired information for interventional radiology fellowship websites was utilized and compared to the information currently available on emergency radiology fellowship websites. RESULTS: Eighteen emergency radiology fellowship programs were initially identified. One program website was inaccessible at the time of data collection. Of the remaining 17 program websites, contact information, application requirements, program description, stand-alone website, length of fellowship, research opportunities, salary, and other benefits were available for greater than 75% of programs, while listing of current fellows, moonlighting opportunities, social information, and alumni information were available at fewer than 25% of program websites. CONCLUSION: There is broad variability in the type and amount of data available to potential emergency radiology fellows across the surveyed program websites. Several key areas-specifically, listings of current fellows, rotation schedules, facility descriptions, and didactic information-present high-yield opportunities for improvement of desired accessible data.

Evaluating Factors and Resources Affecting Ranking of Diagnostic Radiology Residency Programs by Medical Students in 2016-2017.

RATIONALE AND OBJECTIVES: Recent changes in radiology curriculum and access to residency program information, including the introduction of various online resources and the Interventional Radiology integrated pathway, may influence the rank list order of medical student applicants. The purpose of this study is to assess factors that affect the rank lists of medical students applying to our radiology residency program in the 2016-2017 academic year. MATERIALS AND METHODS: After IRB approval, an anonymous online 19 question survey was emailed to 622 applicants to our diagnostic radiology and/or interventional radiology integrated pathway. Applicants ranked 35 unique factors that may influence their residency rank list order from 1 (not important at all) to 5 (very important), listed their top five 'very important' factors, and ranked various sources of information used to learn about residency programs. General applicant demographic questions were also included. RESULTS: Response rate was 18.8% (117/622). The 5 most important factors affecting applicant ranking of programs are perceived happiness of the residents and faculty (4.69), fellowship and job placement of recent graduates (4.34), interactions with programs' current residents (4.33), stability of the department and program (4.29), and geographic location of the program (4.27). The top 5 resources for learning about residency programs were interactions with current residents at the program (4.47), program director (3.87), and interviewing faculty (3.87). Individual program websites were ranked more highly than internet message boards and forums as an information source. CONCLUSION: Medical students consider a large number of factors and resources in determining their rank lists, with factors encountered during the interview day playing a significant role in shaping the applicants' view of a residency program.