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.

Implications of Bias in Artificial Intelligence: Considerations for Cardiovascular Imaging.

PURPOSE OF REVIEW: Bias in artificial intelligence (AI) models can result in unintended consequences. In cardiovascular imaging, biased AI models used in clinical practice can negatively affect patient outcomes. Biased AI models result from decisions made when training and evaluating a model. This paper is a comprehensive guide for AI development teams to understand assumptions in datasets and chosen metrics for outcome/ground truth, and how this translates to real-world performance for cardiovascular disease (CVD). RECENT FINDINGS: CVDs are the number one cause of mortality worldwide; however, the prevalence, burden, and outcomes of CVD vary across gender and race. Several biomarkers are also shown to vary among different populations and ethnic/racial groups. Inequalities in clinical trial inclusion, clinical presentation, diagnosis, and treatment are preserved in health data that is ultimately used to train AI algorithms, leading to potential biases in model performance. Despite the notion that AI models themselves are biased, AI can also help to mitigate bias (e.g., bias auditing tools). In this review paper, we describe in detail implicit and explicit biases in the care of cardiovascular disease that may be present in existing datasets but are not obvious to model developers. We review disparities in CVD outcomes across different genders and race groups, differences in treatment of historically marginalized groups, and disparities in clinical trials for various cardiovascular diseases and outcomes. Thereafter, we summarize some CVD AI literature that shows bias in CVD AI as well as approaches that AI is being used to mitigate CVD bias.

Identifying and improving the "ground truth" of race in disparities research through improved EMR data reporting. A systematic review.

BACKGROUND: Studies about racial disparities in healthcare are increasing in quantity; however, they are subject to vast differences in definition, classification, and utilization of race/ethnicity data. Improved standardization of this information can strengthen conclusions drawn from studies using such data. The objective of this study is to examine how data related to race/ethnicity are recorded in research through examining articles on race/ethnicity health disparities and examine problems and solutions in data reporting that may impact overall data quality. METHODS: In this systematic review, Business Source Complete, Embase.com, IEEE Xplore, PubMed, Scopus and Web of Science Core Collection were searched for relevant articles published from 2000 to 2020. Search terms related to the concepts of electronic medical records, race/ethnicity, and data entry related to race/ethnicity were used. Exclusion criteria included articles not in the English language and those describing pediatric populations. Data were extracted from published articles. This review was organized and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement for systematic reviews. FINDINGS: In this systematic review, 109 full text articles were reviewed. Weaknesses and possible solutions have been discussed in current literature, with the predominant problem and solution as follows: the electronic medical record (EMR) is vulnerable to inaccuracies and incompleteness in the methods that research staff collect this data; however, improved standardization of the collection and use of race data in patient care may help alleviate these inaccuracies. INTERPRETATION: Conclusions drawn from large datasets concerning peoples of certain race/ethnic groups should be made cautiously, and a careful review of the methodology of each publication should be considered prior to implementation in patient care.

Percutaneous Robotics in Interventional Radiology.

The accuracy of the robotic device not only relies on a reproducible needle advancement, but also on the possibility to correct target movement at chosen checkpoints and to deviate from a linear to a nonlinear trajectory. We report our experience in using the robotic device for the insertion of trocar needles in CT guided procedures. The majority of procedures were targeted organ biopsies in the chest abdomen or pelvis. The accuracy of needle placement after target adjustments did not significantly differ from those patients where a linear trajectory could be used. The steering capabilities of the robot allow correction of target movement of the fly.

Uterine artery embolization in Tanzania: a procedure with major public health implications.

BACKGROUND: The burden of uterine fibroids is substantial in sub-Saharan Africa (SSA), with up to 80% of black women harboring them in their lifetime. While uterine artery embolization (UAE) has emerged as an effective alternative to surgery to manage this condition, the procedure is not available to the vast majority of women living in SSA due to limited access to interventional radiology (IR) in the region. One of the few countries in SSA now offering UAE in a public hospital setting is Tanzania. This study aims to assess the safety and effectiveness of UAE in this new environment. METHODS: From June 2019 to July 2022, a single-center, retrospective cohort study was conducted at Tanzania's first IR service on all patients who underwent UAE for the management of symptomatic fibroids or adenomyosis. Patients were selected for the procedure based on symptom severity, imaging findings, and medical management failure. Procedural technical success and adverse events were recorded for all UAEs. Self-reported symptom severity and volumetric response on imaging were compared between baseline and six-months post-procedure using paired sample t-tests. RESULTS: During the study period, 92.1% (n = 35/38) of patients underwent UAE for the management of symptomatic fibroids and 7.9% (n = 3/38) for adenomyosis. All (n = 38/38) were considered technically successful and one minor adverse event occurred (2.7%). Self-reported symptom-severity scores at six-months post-procedure decreased in all categories: abnormal uterine bleeding from 8.8 to 3.1 (-5.7), pain from 6.7 to 3.2 (-3.5), and bulk symptoms from 2.8 to 1 (-1.8) (p < 0.01). 100% of patients reported satisfaction with outcomes. Among the nine patients with follow-up imaging, there was a mean volumetric decrease of 35.5% (p = 0.109). CONCLUSIONS: UAE for fibroids and adenomyosis can be performed with high technical success and low complication rates in a low-resource setting like Tanzania, resulting in significant symptom relief for patients. Building capacity for UAE has major public health implications not only for fibroids and adenomyosis, but can help address the region's leading cause of maternal mortality, postpartum hemorrhage.

Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients.

Purpose: Our study investigates whether graph-based fusion of imaging data with non-imaging electronic health records (EHR) data can improve the prediction of the disease trajectories for patients with coronavirus disease 2019 (COVID-19) beyond the prediction performance of only imaging or non-imaging EHR data. Approach: We present a fusion framework for fine-grained clinical outcome prediction [discharge, intensive care unit (ICU) admission, or death] that fuses imaging and non-imaging information using a similarity-based graph structure. Node features are represented by image embedding, and edges are encoded with clinical or demographic similarity. Results: Experiments on data collected from the Emory Healthcare Network indicate that our fusion modeling scheme performs consistently better than predictive models developed using only imaging or non-imaging features, with area under the receiver operating characteristics curve of 0.76, 0.90, and 0.75 for discharge from hospital, mortality, and ICU admission, respectively. External validation was performed on data collected from the Mayo Clinic. Our scheme highlights known biases in the model prediction, such as bias against patients with alcohol abuse history and bias based on insurance status. Conclusions: Our study signifies the importance of the fusion of multiple data modalities for the accurate prediction of clinical trajectories. The proposed graph structure can model relationships between patients based on non-imaging EHR data, and graph convolutional networks can fuse this relationship information with imaging data to effectively predict future disease trajectory more effectively than models employing only imaging or non-imaging data. Our graph-based fusion modeling frameworks can be easily extended to other prediction tasks to efficiently combine imaging data with non-imaging clinical data.

Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports.

PURPOSE: To evaluate the complexity of diagnostic radiology reports across major imaging modalities and the ability of ChatGPT (Early March 2023 Version, OpenAI, California, USA) to simplify these reports to the 8th grade reading level of the average U.S. adult. METHODS: We randomly sampled 100 radiographs (XR), 100 ultrasound (US), 100 CT, and 100 MRI radiology reports from our institution's database dated between 2022 and 2023 (N = 400). These were processed by ChatGPT using the prompt "Explain this radiology report to a patient in layman's terms in second person: ". Mean report length, Flesch reading ease score (FRES), and Flesch-Kincaid reading level (FKRL) were calculated for each report and ChatGPT output. T-tests were used to determine significance. RESULTS: Mean report length was 164 ± 117 words, FRES was 38.0 ± 11.8, and FKRL was 10.4 ± 1.9. FKRL was significantly higher for CT and MRI than for US and XR. Only 60/400 (15%) had a FKRL <8.5. The mean simplified ChatGPT output length was 103 ± 36 words, FRES was 83.5 ± 5.6, and FKRL was 5.8 ± 1.1. This reflects a mean decrease of 61 words (p < 0.01), increase in FRES of 45.5 (p < 0.01), and decrease in FKRL of 4.6 (p < 0.01). All simplified outputs had FKRL <8.5. DISCUSSION: Our study demonstrates the effective use of ChatGPT when tasked with simplifying radiology reports to below the 8th grade reading level. We report significant improvements in FRES, FKRL, and word count, the last of which requires modality-specific context.

Contemporary Management of Blunt Liver Trauma: An Analysis of the Trauma Quality Improvement Program Registry (2007-2019).

PURPOSE: To evaluate the effectiveness of management strategies for blunt liver injuries in adult patients. MATERIALS AND METHODS: Patients aged ≥18 years with blunt liver injuries registered via the Trauma Quality Improvement Program (2007-2019) were identified. Management strategies initiated within 24 hours of hospital presentation were classified as nonoperative management (NOM), embolization, surgery, or combination therapy. Patients were stratified by injury grade. Linear models estimated each strategy's effect on hospital length of stay (LOS), intensive care unit (ICU) LOS, ventilator dependence, and mortality. RESULTS: Of 78,127 included patients, 88.7%, 8.7%, 1.8%, and 0.8% underwent NOM, surgery, embolization, and combination therapy, respectively. Among patients with low-grade (n = 62,237) and high-grade (n = 15,890) injuries and compared with all other management strategies, NOM was associated with the shortest hospital LOS and ICU LOS. Among patients with low-grade injuries and compared with surgery, embolization was associated with a shorter hospital LOS (9.7 days; P < .001; Cohen d = 0.32) and ICU LOS (5.3 days; P < .001; Cohen d = 0.36). Among patients with high-grade injuries and compared with surgery, embolization was associated with a shorter ICU LOS (6.0 days; P < .01; Cohen d = 0.24). Among patients with low- and high-grade injuries and compared with embolization, surgery was associated with higher odds of mortality (P < .001). CONCLUSIONS: Among patients presenting with blunt liver injuries and compared with surgery, embolization was associated with a shorter ICU LOS and lower risk of mortality.

Fostering an inclusive workplace for LGBTQIA+ people in radiology and radiation oncology.

The inclusion and celebration of LGBTQIA+ staff in radiology and radiation oncology departments is crucial in developing a diverse and thriving workplace. Despite the substantial social change in Australia, LGBTQIA+ people still experience harassment and exclusion, negatively impacting their well-being and workplace productivity. We need to be proactive in creating policies that are properly implemented and translate to a safe and inclusive space for marginalised groups. In this work, we outline the role we all can play in creating inclusive environments, for both individuals and leaders working in radiology and radiation oncology. We can learn how to avoid normative assumptions about gender and sexuality, respect people's identities and speak out against witnessed discrimination or slights. Robust policies are needed to protect LGBTQIA+ members from discrimination and provide equal access across other pertinent parts of work life such as leave entitlements, representation in data collection and safe bathroom access. We all deserve to feel safe and respected at work and further effort is needed to ensure this extends to LGBTQIA+ staff in the radiology and radiation oncology workforces.

Patient Radiation Doses in IR Procedures: The American College of Radiology Dose Index Registry-Fluoroscopy Pilot.

PURPOSE: To update normative data on fluoroscopy dose indices in the United States for the first time since the Radiation Doses in Interventional Radiology study in the late 1990s. MATERIALS AND METHODS: The Dose Index Registry-Fluoroscopy pilot study collected data from March 2018 through December 2019, with 50 fluoroscopes from 10 sites submitting data. Primary radiation dose indices including fluoroscopy time (FT), cumulative air kerma (Ka,r), and kerma area product (PKA) were collected for interventional radiology fluoroscopically guided interventional (FGI) procedures. Clinical facility procedure names were mapped to the American College of Radiology (ACR) common procedure lexicon. Distribution parameters including the 10th, 25th, 50th, 75th, 95th, and 99th percentiles were computed. RESULTS: Dose indices were collected for 70,377 FGI procedures, with 50,501 ultimately eligible for analysis. Distribution parameters are reported for 100 ACR Common IDs. FT in minutes, Ka,r in mGy, and PKA in Gy-cm2 are reported in this study as (n; median) for select ACR Common IDs: inferior vena cava filter insertion (1,726; FT: 2.9; Ka,r: 55.8; PKA: 14.19); inferior vena cava filter removal (464; FT: 5.7; Ka,r: 178.6; PKA: 34.73); nephrostomy placement (2,037; FT: 4.1; Ka,r: 39.2; PKA: 6.61); percutaneous biliary drainage (952; FT: 12.4; Ka,r: 160.5; PKA: 21.32); gastrostomy placement (1,643; FT: 3.2; Ka,r: 29.1; PKA: 7.29); and transjugular intrahepatic portosystemic shunt placement (327; FT: 34.8; Ka,r: 813.0; PKA: 181.47). CONCLUSIONS: The ACR DIR-Fluoro pilot has provided state-of-the-practice statistics for radiation dose indices from IR FGI procedures. These data can be used to prioritize procedures for radiation optimization, as demonstrated in this work.

Patient Radiation Doses in Interventional Radiology Procedures: Comparison of Fluoroscopy Dose Indices between the American College of Radiology Dose Index Registry-Fluoroscopy Pilot and the Radiation Doses in Interventional Radiology Study.

PURPOSE: To compare radiation dose index distributions for fluoroscopically guided interventions in interventional radiology from the American College of Radiology (ACR) Fluoroscopy Dose Index Registry (DIR-Fluoro) pilot to those from the Radiation Doses in Interventional Radiology (RAD-IR) study. MATERIALS AND METHODS: Individual and grouped ACR Common identification numbers (procedure types) from the DIR-Fluoro pilot were matched to procedure types in the RAD-IR study. Fifteen comparisons were made. Distribution parameters, including the 10th, 25th, 50th, 75th, and 95th percentiles, were compared for fluoroscopy time (FT), cumulative air kerma (Ka,r), and kerma area product (PKA). Two derived indices were computed using median dose indices. The procedure-averaged reference air kerma rate (Ka,r¯) was computed as Ka,r / FT. The procedure-averaged x-ray field size at the reference point (Ar) was computed as PKA / (Ka,r × 1,000). RESULTS: The median FT was equally likely to be higher or lower in the DIR-Fluoro pilot as it was in the RAD-IR study, whereas the maximum FT was almost twice as likely to be higher in the DIR-Fluoro pilot than it was in the RAD-IR study. The median Ka,r was lower in the DIR-Fluoro pilot for all procedures, as was median PKA. The maximum Ka,r and PKA were more often higher in the DIR-Fluoro pilot than in the RAD-IR study. Ka,r¯ followed the same pattern as Ka,r, whereas Ar was often greater in DIR-Fluoro. CONCLUSIONS: The median dose indices have decreased since the RAD-IR study. The typical Ka,r rates are lower, a result of the use of lower default dose rates. However, opportunities for quality improvement exist, including renewed focus on tight collimation of the imaging field of view.

Graph-based Fusion Modeling and Explanation for Disease Trajectory Prediction.

We propose a relational graph to incorporate clinical similarity between patients while building personalized clinical event predictors with a focus on hospitalized COVID-19 patients. Our graph formation process fuses heterogeneous data, i.e., chest X-rays as node features and non-imaging EHR for edge formation. While node represents a snap-shot in time for a single patient, weighted edge structure encodes complex clinical patterns among patients. While age and gender have been used in the past for patient graph formation, our method incorporates complex clinical history while avoiding manual feature selection. The model learns from the patient's own data as well as patterns among clinically-similar patients. Our visualization study investigates the effects of 'neighborhood' of a node on its predictiveness and showcases the model's tendency to focus on edge-connected patients with highly suggestive clinical features common with the node. The proposed model generalizes well by allowing edge formation process to adapt to an external cohort.

Prophylactic IVC filter placement in patients with severe intracranial, spinal cord, and orthopedic injuries at high thromboembolic event risk: A utilization and outcomes analysis of the National Trauma Data Bank.

PURPOSE: To determine relationships between prophylactic inferior vena cava filter (IVCF) insertion and pulmonary embolism (PE), deep venous thrombosis (DVT), and in-hospital mortality outcomes in patients with severe traumatic pelvic/lower extremity, intracranial, and spinal cord injuries. METHODS: Adult patients with severe traumatic pelvic/lower extremity, intracranial, and spinal cord injuries admitted to level I-IV trauma centers were selected from the National Trauma Data Bank (NTDB). IVCFs that were inserted both ≤48 h after admission and before a lower extremity venous ultrasound were defined as prophylactic. Associations between prophylactic IVCF insertion and PE, DVT, and overall mortality outcomes during admission were estimated using logistic regression models after propensity score matching. Additionally, factors predictive of prophylactic IVCF insertion were estimated using multivariate logistic regression. RESULTS: Of 462,838 patients, 11,938 (2.6%) underwent prophylactic IVCF insertion. Prophylactic IVCF utilization decreased over time (6.3% in 2008 to 1.8% in 2015). Factors associated with prophylactic IVCF placement were injury pattern, trauma center level/region, Injury Severity Score, and race. Prophylactic IVCF placement was positively associated with PE (Odds Ratio (OR): 5.25, p < 0.01) and DVT (OR: 5.55, p < 0.01), but negatively associated with in-hospital mortality compared to the propensity score-matched control group (OR: 0.46, p < 0.01). CONCLUSION: Prophylactic IVCF insertion in adult patients with severe pelvic/lower extremity fractures, intracranial injuries, and spinal cord injuries was negatively associated with in-hospital mortality, but positively associated with VTE. Further research evaluating the use of prophylactic IVCF placement in trauma patients with these specific severe injury types may be warranted.

Comparative effectiveness of pelvic arterial embolization versus laparotomy in adults with pelvic injuries: A National Trauma Data Bank analysis.

PURPOSE: To compare the clinical outcomes and trends of arterial embolization (AE) versus laparotomy which are used in the management of pelvic trauma. MATERIALS AND METHODS: Adult patients with pelvic injuries were identified using the National Trauma Data Bank (NTDB) from 2007 to 2015. Patients with non-pelvic life-threatening injuries were excluded. Patients were grouped in operatively managed pelvic ring injuries, laparotomy ± fixation, AE ± fixation, and laparotomy and AE ± fixation. Using a linear mixed regression and logistic regression models, hospital length of stay (LOS), ICU days, ventilator days, and mortality for different therapies were compared. A propensity score weighting method was used to further eliminate treatment selection bias in the study sample and compare the outcomes between AE and laparotomy. RESULTS: Of 7473 pelvic trauma patients, 1226 (16.4%) patients were only operatively managed. 3730 patients (49.9%) underwent laparotomy, 2136 underwent AE (28.6%), and 381 (5.1%) patients underwent both laparotomy and AE. The year of injury, patient age, gender, race, severity of injury and presence of shock were found to be predictors of receipt of different therapies (P < 0.001 for all). When correcting for these confounding factors, the mortality rate was lower in the AE group compared to the laparotomy group 6.6% vs. 20.6% (P < 0.001). Additionally, LOS and ICU days were shorter for the AE group than the laparotomy group (P < 0.001). CONCLUSION: AE in patients with pelvic injuries is associated with lower mortality, as well as shorter LOS and ICU stays compared to laparotomy.

Balancing the Scales: An Analysis of Social Determinants of Health, Radiology Report Acuity, and Radiology Staffing Models in an Academic Health System.

PURPOSE: Social determinants of health, including race and insurance status, contribute to patient outcomes. In academic health systems, care is provided by a mix of trainees and faculty members. The optimal staffing ratio of trainees to faculty members (T/F) in radiology is unknown but may be related to the complexity of patients requiring care. Hospital characteristics, patient demographics, and radiology report findings may serve as markers of risk for poor outcomes because of patient complexity. METHODS: Descriptive characteristics of each hospital in an urban five-hospital academic health system, including payer distribution and race, were collected. Radiology department T/F ratios were calculated. A natural language processing model was used to classify multimodal report findings into nonacute, acute, and critical, with report acuity calculated as the fraction of acute and critical findings. Patient race, payer type, T/F ratio, and report acuity score for hospital 1, a safety net hospital, were compared with these factors for hospitals 2 to 5. RESULTS: The fraction of patients at hospital 1 who are Black (79%) and have Medicaid insurance (28%) is significantly higher than at hospitals 2 to 5 (P < .0001), with the exception of hospital 3 (80.1% black). The T/F ratio of 1.37 at hospital 1 as well as its report acuity (28.9%) were significantly higher (P < .0001 for both). CONCLUSIONS: T/F ratio and report acuity are highest at hospital 1, which serves the most at-risk patient population. This suggests a potential overreliance on trainees at a site whose patients may require the greatest expertise to optimize care.

Graph-based Fusion Modeling and Explanation forDisease Trajectory Prediction.

We propose a relational graph to incorporate clinical similarity between patients while building personalized clinical event predictors with a focus on hospitalized COVID-19 patients. Our graph formation process fuses heterogeneous data, i.e., chest X-rays as node features and non-imaging EHR for edge formation. While node represents a snap-shot in time for a single patient, weighted edge structure encodes complex clinical patterns among patients. While age and gender have been used in the past for patient graph formation, our method incorporates complex clinical history while avoiding manual feature selection. The model learns from the patient's own data as well as patterns among clinically-similar patients. Our visualization study investigates the effects of 'neighborhood' of a node on its predictiveness and showcases the model's tendency to focus on edge-connected patients with highly suggestive clinical features common with the node. The proposed model generalizes well by allowing edge formation process to adapt to an external cohort.

Academic Radiology in the United States: Defining Gender Disparities in Faculty Leadership and Academic Rank.

RATIONALE AND OBJECTIVES: Female physicians in academic medicine have faced barriers that potentially affect representation in different fields and delay promotion. Little is known about gender representation differences in United States academic radiology departments, particularly within the most pursued subspecialties. PURPOSE: To determine whether gender differences exist in United States academic radiology departments across seven subspecialties with respect to academic ranks, departmental leadership positions, experience, and scholarly metrics. MATERIALS AND METHODS: In this cross-sectional study from November 2018 to June 2020, a database of United States academic radiologists at 129 academic departments in seven subspecialties was created. Each radiologist's academic rank, departmental leadership position (executive-level - Chair, Director, Chief, and Department or Division Head vs vice-level - vice, assistant, or associate positions of executive level), self-identified gender, years in practice, and measures of scholarly productivity (number of publications, citations, and h-index) were compiled from institutional websites, Doximity, LinkedIn, Scopus, and official NPI profiles. The primary outcome, gender composition differences in these cohorts, was analyzed using Chi2 while continuous data were analyzed using Kruskal-Wallis rank sum test. The adjusted gender difference for all factors was determined using a multivariate logistic regression model. RESULTS: Overall, 5086 academic radiologists (34.7% women) with a median 14 years of practice (YOP) were identified and indexed. There were 919 full professors (26.1% women, p < 0.01) and 1055 executive-level leadership faculty (30.6% women, p < 0.01). Within all subspecialties except breast imaging, women were in the minority (35.4% abdominal, 79.1% breast, 12.1% interventional, 27.5% musculoskeletal, 22.8% neuroradiology, 45.1% pediatric, and 19.5% nuclear; p < 0.01). Relative to subspecialty gender composition, women full professors were underrepresented in abdominal, pediatric, and nuclear radiology (p < 0.05) and women in any executive-level leadership were underrepresented in abdominal and nuclear radiology (p < 0.05). However, after adjusting for h-index and YOP, gender did not influence rates of professorship or executive leadership. The strongest single predictors for professorship or executive leadership were h-index and YOP. CONCLUSION: Women academic radiologists in the United States are underrepresented among senior faculty members despite having similar levels of experience as men. Gender disparities regarding the expected number of women senior faculty members relative to individual subspecialty gender composition were more pronounced in abdominal and nuclear radiology, and less pronounced in breast and neuroradiology. Overall, h-index and YOP were the strongest predictors for full-professorship and executive leadership among faculty. KEY RESULTS: ● Though women comprise 34.7% of all academic radiologists, women are underrepresented among senior faculty members (26.1% of full professors and 30.6% of executive leadership) ● Women in junior faculty positions had higher median years of practice than their male counterparts (10 vs 8 for assistant professors, 21 vs 13 for vice leadership) ● Years of practice and h-index were the strongest predictors for full professorship and executive leadership.

Utilization and Comparative Effectiveness of Uterine Artery Embolization versus Hysterectomy for Severe Postpartum Hemorrhage: A National Inpatient Sample Study.

PURPOSE: To identify differences in mortality or length of hospital stay for mothers treated with uterine artery embolization (UAE) or hysterectomy for severe postpartum hemorrhage (PPH), as well as to analyze whether geographic or clinical determinants affected the type of therapy received. MATERIALS AND METHODS: This National Inpatient Sample study from 2005 to 2017 included all patients with live-birth deliveries. Severe PPH was defined as PPH that required transfusion, hysterectomy, or UAE. Propensity score weighting-adjusted demographic, maternal, and delivery risk factors were used to assess mortality and prolonged hospital stay. RESULTS: Of 9.8 million identified live births, PPH occurred in 31.0 per 1,000 cases. The most common intervention for PPH was transfusion (116.4 per 1,000 cases of PPH). Hysterectomy was used more frequently than UAE (20.4 vs 12.9 per 1,000 cases). The following factors predicted that hysterectomy would be used more commonly than UAE: previous cesarean delivery, breech fetal position, placenta previa, transient hypertension during pregnancy without pre-eclampsia, pre-existing hypertension without pre-eclampsia, pre-existing hypertension with pre-eclampsia, unspecified maternal hypertension, and gestational diabetes (all P < .001). Delivery risk factors associated with greater utilization of hysterectomy over UAE included postterm pregnancy, premature rupture of membranes, cervical laceration, forceps vaginal delivery, and shock (all P < .001). There was no difference in mortality between hysterectomy and UAE. After balancing demographic, maternal, and delivery risk factors, the odds of prolonged hospital stay were 0.38 times lower with UAE than hysterectomy (P < .001). CONCLUSIONS: Despite similar mortality and shorter hospital stays, UAE is used far less than hysterectomy in the management of severe PPH.

Challenges of Implementing Artificial Intelligence in Interventional Radiology.

Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.