John Mongan

Background: Accurate organ segmentation in pediatric focused assessment with sonography in trauma (FAST) is an important prerequisite for automated image analysis and image quality assessment, but manual segmentation is time-intensive, subject to inter- and intra-observer variability, and limits the scalability of deep learning dataset development. Although deep learning has shown promise for ultrasound organ segmentation, its application to pediatric FAST has not been evaluated. Objective: To describe the development and evaluation of deep learning models to automatically localize and segment six abdominal organs in pediatric FAST. Methods: We conducted a cross-sectional study using a convenience sample of archived FAST examinations from 200 injured children evaluated at a Level I pediatric trauma center. Emergency ultrasound fellowship-trained physicians manually segmented six key abdominal organs. Annotated image-mask pairs were split into training and test sets in an 80:20 ratio. We developed or fine-tuned U-Net, EVIT U-Net, UltraSAM, and nnU-Net models to automatically segment the bladder, kidney, liver, spleen, spine, and diaphragm. Segmentation accuracy was assessed using the Dice-Sørensen Coefficient (DSC) compared with manual annotations. Pediatric emergency ultrasound experts reviewed previously unlabeled FAST clips and rated the acceptability of model-generated organ masks. Results: The dataset included 1,807 manually annotated image-mask pairs from 200 pediatric FAST clips. Using an 80:20 split with no overlap between video clips, 1,446 image-mask pairs were used for training and 361 for testing. Across all models and organs, mean DSC was 0.79 (95% CI: 0.75-0.84). Mean DSC was highest for the kidney and bladder, each 0.91 (95% CI: 0.87-0.95), and lowest for the diaphragm at 0.63 (95% CI: 0.60-0.67). EVIT U-Net achieved the highest DSC for kidney, liver, and spleen, performed comparably to the highest-performing models for bladder, and had a mean inference time of 54 milliseconds per image. In previously unlabeled pediatric FAST clips, experts rated model-generated organ masks as acceptable for localization in 73%-95% of clips across organs. Conclusion: We describe the automatic segmentation of key abdominal organs in pediatric FAST with limited annotated data. This represents an important advancement in our image processing pipeline and can accelerate the development of large, robust datasets with segmentation for anatomy and pathology. Experts found that model-generated masks provided acceptable localization in most previously unlabeled pediatric clips, supporting the potential use of automated segmentation in pediatric FAST image analysis.

The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) provides a structured framework for transparent and reproducible reporting of AI studies in medical imaging. Since its introduction in 2020, CLAIM has been widely adopted by researchers, reviewers, and journal editors, but variability in its interpretation has limited consistent application. In 2024, the CLAIM Steering Committee published an updated checklist developed through a structured Delphi consensus process involving 72 experts across imaging-related medical specialties, AI science, journal editing, and biostatistics. This article provides a detailed explanation and elaboration of each of the 44 items in the CLAIM 2024 Update, clarifying the intent, common misinterpretations, and appropriate implementation of each item. Illustrative examples from the published literature demonstrating adherence to each item are provided in an accompanying Supplement and through a user-friendly online tool at https://rsna.github.io/claim/ . The scope of this work spans the full AI study lifecycle covered by CLAIM, from study design and data sourcing to model development, evaluation, and reporting of results. This resource is intended to support authors, reviewers, and editors in the accurate and consistent application of CLAIM, thereby improving the quality, transparency, and reproducibility of AI research in medical imaging. © RSNA, 2026

As artificial intelligence (AI) tools are increasingly integrated into imaging workflows, understanding how AI results are generated and presented to the end user can equip radiologists to optimize interactions with AI results in practice. Although AI solutions are marketed as high-performing options that promise efficiency and diagnostic gains, issues arising at the radiologist-AI interface can lead to diminished returns due to unintentional cognitive burdens or misalignments with clinical workflows. A foundational understanding of how images are processed by AI solutions, presented in imaging workflows, and documented can allow radiologists to troubleshoot shortcomings in practice after clinical deployment. This article is the second in a primer series providing a foundation in AI literacy for radiologists. Building on the first article in this primer series, this article addresses questions raised by end users while using AI in practice. It covers how to consider the intended use of AI solutions, the process through which AI results are generated, the reasons why results may not be available at the time of study interpretation, and how to align the presentation of AI results with end user workflows. The discussion also explores emerging topics and challenges, including considerations for storing AI results and the related medicolegal considerations.

The RSNA Lumbar Degenerative Imaging Spine Classification dataset is the largest publicly available adult MRI lumbar spine dataset for degenerative disease. The dataset includes multisequence, multiplanar MRI scans from 2697 patients through contributions from eight institutions across six countries and five continents.

Objectives To develop and do multicenter validation on an algorithm that screens for osteoporosis from abdominal CTs. Methods This is a diagnostic accuracy study with retrospective data from January 2022 to July 2022 consisting of two steps: a segmentation step of the lumbar vertebral bodies, involving outpatient non-contrast abdominal CTs from Diagnósticos da América S/A (Dasa), and a multicenter validation step incorporating data from four additional institutions. The segmentation employed a 2D UNet with a ResNet34 backbone. We determined the Pearson correlation coefficient (r) between the mean of the slices’ mean attenuations (MSMA) on CT scans against the bone mineral density (BMD) on recent DEXA scans, calculated AUCs and performance metrics for osteoporosis prediction, including 95% confidence intervals, and evaluated calibration plots. Results The multicenter validation included 504 participants (median age, 66 years, interquartile range 56–72; 388 women). A linear regression analysis showed an r of 0.62 (95% CI, 0.56–0.67) between MSMA (HU) and BMD (g/cm²). The AUCs (95% CI) for distinguishing between normal and osteoporosis were 0.96 (0.89, 1.0) for the internal dataset and 0.82 (0.74, 0.89) for the external dataset, and the performance metrics (95% CI), for a globally optimized threshold of 202.6 HU, were 100% sensitivity (44, 100) and 91% specificity (84, 95) for internal data and 79% sensitivity (61, 90) and 81% specificity (76, 84) for external sites. MSMA was independently associated with osteoporosis on a mixed-effects logistic regression analysis. The model showed good calibration, with Brier score of 0.054. Conclusion We developed and performed a multicenter validation of a DL model for osteoporosis prediction on CT. Graphical abstract

A foundational understanding of core principles for artificial intelligence (AI) development enables radiologists to function as effective users of AI in their practice.

Algorithms submitted to the RSNA Screening Mammography Breast Cancer Detection AI Challenge detected different cancers; ensemble models had increased sensitivity while maintaining low recall rates; and sensitivity was higher for invasive versus noninvasive cancers.

As the application of artificial intelligence (AI) in radiology transitions to foundational visual models, dependence on expert annotations to prepare “AI-ready” datasets continues to evolve with increasing use of AI tools to automate label creation.