Background: This study aimed to evaluate the impact of an AI-assisted fracture detection program on radiology residents’ performance in pediatric and adult trauma patients and assess its implications for residency training. Methods: This study, conducted retrospectively, included 200 radiographs from participants aged 1 to 95 years (mean age: 40.7 ± 24.5 years), encompassing various body regions. Among these, 50% (100/200) displayed at least one fracture, totaling one hundred thirty-five fractures, assessed by four radiology residents with different experience levels. A machine learning algorithm was employed for fracture detection, and the ground truth was established by consensus among two experienced senior radiologists. Fracture detection accuracy, reporting time, and confidence were evaluated with and without AI support. Results: Radiology residents’ sensitivity for fracture detection improved significantly with AI support (58% without AI vs. 77% with AI, p < 0.001), while specificity showed minor improvements (77% without AI vs. 79% with AI, p = 0.0653). AI stand-alone performance achieved a sensitivity of 93% with a specificity of 77%. AI support for fracture detection significantly reduced interpretation time for radiology residents by an average of approximately 2.6 s (p = 0.0156) and increased resident confidence in the findings (p = 0.0013). Conclusion: AI support significantly enhanced fracture detection sensitivity among radiology residents, particularly benefiting less experienced radiologists. It does not compromise specificity and reduces interpretation time, contributing to improved efficiency. This study underscores AI’s potential in radiology, emphasizing its role in training and interpretation improvement.

Objectives: Manual field-of-view (FoV) prescription in whole-body magnetic resonance imaging (WB-MRI) is vital for ensuring comprehensive anatomic coverage and minimising artifacts, thereby enhancing image quality. However, this procedure is time-consuming, subject to operator variability, and adversely impacts both patient comfort and workflow efficiency. To overcome these limitations, an automated system was developed and evaluated that prescribes multiple consecutive FoV stations for WB-MRI using deep-learning (DL)-based three-dimensional anatomic segmentations. Materials and Methods: A total of 374 patients (mean age: 50.5 ± 18.2 y; 52% females) who underwent WB-MRI, including T2-weighted Half-Fourier acquisition single-shot turbo spin-echo (T2-HASTE) and fast whole-body localizer (FWBL) sequences acquired during continuous table movement on a 3T MRI system, were retrospectively collected between March 2012 and January 2025. An external cohort of 10 patients, acquired on two 1.5T scanners, was utilized for generalizability testing. Complementary nnUNet-v2 models were fine-tuned to segment tissue compartments, organs, and a whole-body (WB) outline on FWBL images. From these predicted segmentations, 5 consecutive FoVs (head/neck, thorax, liver, pelvis, and spine) were generated. Segmentation accuracy was quantified by Sørensen–Dice coefficients (DSC), Precision (P), Recall (R), and Specificity (S). Clinical utility was assessed on 30 test cases by 4 blinded experts using Likert scores and a 4-way ranking against 3 radiographer prescriptions. Interrater reliability and statistical comparisons were employed using the intraclass correlation coefficient (ICC), Kendall W, Friedman, and Wilcoxon signed-rank tests. Results: Mean DSCs were 0.98 for torso (P = 0.98, R = 0.98, S = 1.00), 0.96 for head/neck (P = 0.95, R = 0.96, S = 1.00), 0.94 for abdominal cavity (P = 0.95, R = 0.94, S = 1.00), 0.90 for thoracic cavity (P = 0.90, R = 0.91, S = 1.00), 0.86 for liver (P = 0.85, R = 0.87, S = 1.00), and 0.63 for spinal cord (P = 0.64, R = 0.63, S = 1.00). The clinical utility was evidenced by assessments from 2 expert radiologists and 2 radiographers, with 98.3% and 87.5% of cases rated as clinically acceptable in the internal test data set and the external test data set. Predicted FoVs received the highest ranking in 60% of cases. They placed within the top 2 in 85.8% of cases, outperforming radiographers with 9 and 13 years of experience (P < 0.001) and matching the performance of a radiographer with 20 years of experience. Conclusions: DL-based three-dimensional anatomic segmentations enable accurate and reliable multistation FoV prescription for WB-MRI, achieving expert-level performance while significantly reducing manual workload. Automated FoV planning has the potential to standardize WB-MRI acquisition, reduce interoperator variability, and enhance workflow efficiency, thereby facilitating broader clinical adoption.

Background: Cystic fibrosis bone disease (CFBD) is a common comorbidity in adult people with cystic fibrosis (pwCF), resulting in an increased risk of bone fractures. This study evaluated the capacity of artificial intelligence (AI)-assisted low-dose chest CT (LDCT) opportunistic screening for detecting low bone mineral density (BMD) in adult pwCF. Methods: In this retrospective single-center study, 65 adult pwCF (mean age 30.1 ± 7.5 years) underwent dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae L1 to L4 to determine BMD and corresponding z-scores and completed LDCTs of the chest within three months as part of routine clinical care. A fully automated CT-based AI algorithm measured the attenuation values (Hounsfield units [HU]) of the thoracic vertebrae Th9–Th12 and first lumbar vertebra L1. The ability of the algorithm to diagnose CFBD was assessed using receiver operating characteristic (ROC) curves. Results: HU values of Th9 to L1 and DXA-derived BMD and the corresponding z-scores of L1 to L4 showed a strong correlation (all p < 0.05). The area under the curve (AUC) for diagnosing low BMD was highest for L1 (0.796; p = 0.001) and Th11 (0.835; p < 0.001), resulting in a specificity of 84.9% at a sensitivity level of 75%. The HU threshold values for distinguishing normal from low BMD were <197 (L1) and <212 (Th11), respectively. Conclusions: Routine LDCT of the chest with the fully automated AI-guided determination of thoracic and lumbar vertebral attenuation values is a valuable tool for predicting low BMD in adult pwCF, with the best results for Th11 and L1. However, further studies are required to define clear threshold values.

Purpose: In emergency diagnostics, head CT and CT angiography (CTA) of craniocervical vasculature are indispensable for children, despite their increased radiation sensitivity. This study assesses the radiation dose metrics of head CT and CTA in pediatric patients managed in the trauma resuscitation unit (TRU). Methods: All patients aged 0–<15 years who underwent head CT and CTA in the TRU between April 2020 and August 2023 were included. CT dose index volume (CTDIvol) and dose-length product (DLP) were extracted from the Radimetrics Enterprise Platform, which also provided organ doses estimated via Monte Carlo simulations and effective doses (ED) derived from these estimates. Dose metrics were compared with national diagnostic reference levels (DRLs), defined for three pediatric age groups: I (0–<5 years), II (5–<10 years), and III (10–<15 years). Results: Of 212 pediatric TRU patients, 62.7% (133/212) underwent CT and 72.2% (96/133) received combined CT and CTA. Median CTDIvol and DLP increased with age, whereas ED decreased. For head CT, CTDIvol ranged from 18.9 mGy to 29.4 mGy, DLP from 282 to 460 mGycm, and ED from 1.6 to 1.3 mSv. For CTA, CTDIvol ranged from 1.4 to 2.2 mGy, DLP from 40 to 83 mGycm, and ED from 1.0 to 0.8 mSv. All doses remained below national DRLs. Conclusion: Head CT and CTA in pediatric trauma can be performed with radiation doses well below national DRLs. Careful dose management is important to reduce potential long-term cancer risks and deterministic effects such as lens cataract formation.

Background: This study aimed to develop an automated algorithm to noninvasively distinguish gliomas from other intracranial pathologies, preventing misdiagnosis and ensuring accurate analysis before further glioma assessment. Methods: A cohort of 1280 patients with a variety of intracranial pathologies was included. It comprised 218 gliomas (mean age 54.76 ± 13.74 years; 136 males, 82 females), 514 patients with brain metastases (mean age 59.28 ± 12.36 years; 228 males, 286 females), 366 patients with inflammatory lesions (mean age 41.94 ± 14.57 years; 142 males, 224 females), 99 intracerebral hemorrhages (mean age 62.68 ± 16.64 years; 56 males, 43 females), and 83 meningiomas (mean age 63.99 ± 13.31 years; 25 males, 58 females). Radiomic features were extracted from fluid-attenuated inversion recovery (FLAIR), contrast-enhanced, and noncontrast T1-weighted MR sequences. Subcohorts, with 80% for training and 20% for testing, were established for model validation. Machine learning models, primarily XGBoost, were trained to distinguish gliomas from other pathologies. Results: The study demonstrated promising results in distinguishing gliomas from various intracranial pathologies. The best-performing model consistently achieved high area-under-the-curve (AUC) values, indicating strong discriminatory power across multiple distinctions, including gliomas versus metastases (AUC = 0.96), gliomas versus inflammatory lesions (AUC = 1.0), gliomas versus intracerebral hemorrhages (AUC = 0.99), gliomas versus meningiomas (AUC = 0.98). Additionally, across all these entities, gliomas had an AUC of 0.94. Conclusions: The study presents an automated approach that effectively distinguishes gliomas from common intracranial pathologies. This can serve as a quality control upstream to further artificial-intelligence-based genetic analysis of cerebral gliomas.

Objectives: This study aimed to evaluate the feasibility and accuracy of automated contrast-to-noise ratio (CNR) analysis in chest CT using the open-source body and organ analysis (BOA) framework and to validate segmentation modifications for reproducible image-quality assessment. Materials and methods: This retrospective study analyzed 100 contrast-enhanced chest CTs (mean age 60.2 ± 15 years; 40% female; 50 CTA, 50 CTPA) and validated the approach in an external cancer imaging archive (TCIA) cohort ( n = 100). Automated BOA segmentations of the aorta, pulmonary trunk, and paraspinal muscles were modified by fat subtraction and binary erosion and compared with manual measurements from three radiologists. Agreement was assessed using statistical testing, Bland–Altman analysis, and intraclass correlation coefficients (ICC). Results: Unmodified BOA segmentations yielded significantly lower CNRs than manual measurements (all p < 0.01, mean difference up to 6.3). Fat subtraction and binary erosion progressively reduced deviations, with the optimized variant (m_erode6 combined with a_erode6 or p_erode6) showing no significant differences from radiologists ( p ≥ 0.35). In the external TCIA validation cohort ( n = 100), agreement was excellent (ICC 0.89–0.93), and Bland–Altman analysis demonstrated minimal bias (Aorta: 0.16 [limits of agreement (LoA) –3.47 to 3.80]; PT: 0.42 [LoA –4.03 to 4.87]). Conclusions: A minimally modified open-source segmentation framework enables fully automated, reproducible CNR assessment in chest CT, achieving expert-level agreement, including robust performance in external validation. This scalable alternative to manual region-of-interest (ROI) measurement streamlines image-quality assessment, facilitates protocol optimization, and provides standardized metrics ready for integration into AI workflows. Critical relevance statement: This study provides a validated, fully automated method for quantitative CT image quality assessment, reducing observer dependence and enabling consistent evaluation across scanners, protocols, and institutions, thereby supporting reproducible image quality metrics in clinical routine. Key Points: Automated CNR assessment enables objective and reproducible evaluation of image quality in CTA and CTPA. Adjustments of the segmentation strategy can substantially improve the accuracy of automated measurements. The fully automated approach provides a foundation for standardized and scalable CT image quality analysis in research and clinical practice. Graphical