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ACR Approves New Framework for Assessing Imaging AI in Practice
The American College of Radiology Council has approved a new practice parameter to guide the assessment and deployment of AI in imaging.
MRI and Deep Learning Uncover Muscle Fat's Link to Heart Risks
MRI and deep learning can identify hidden muscle fat linked to heart and metabolic risks, offering a new imaging-based biomarker for preventive care.
AI Analyzes 66,000 MRI Scans to Map Body Composition Risks
Researchers used AI to analyze over 66,000 whole-body MRI scans, creating a detailed body composition reference map linked to health risks.
From the Research Hub
What Substitution and Prediction Strategies Address the Challenge of an Unmeasurable C2-7 Cobb Angle?
The C2-7 Cobb angle is an important parameter in evaluating cervical sagittal alignment, which is widely used for preoperative planning, identifying surgical indications, and postoperative assessment. However, this angle becomes unmeasurable in 28% to 49% of clinical radiographs because of poor visualization of the C7 inferior endplate, limiting treatment planning and radiographic follow-up in cervical alignment assessment. The C2-6 Cobb angle has been proposed as a substitute in previous research, but these studies were limited by small symptomatic cohorts from a single center and lacked both subgroup-specific and external validation. Furthermore, there is currently a lack of reference standards for the clinical use of the C2-6 Cobb angle, and no established machine-learning models are available to accurately predict the C2-7 Cobb angle. (1) Can the C2-6 Cobb angle serve as a reliable substitute for the C2-7 angle? (2) Can machine-learning models accurately predict the C2-7 Cobb angle? We conducted a retrospective, multicountry imaging study from January 2020 to January 2025, utilizing standing lateral cervical spine radiographs from a large hospital data set in China and public data sets from Vietnam and India. In China, 11,800 radiographs were initially screened. The inclusion criterion was cervical radiographs of sufficient clarity. The exclusion criterion was cervical radiographs with incomplete visualization of anatomic structures. Following these exclusions, 10,571 radiographs from China were included, comprising 10,000 standard standing lateral radiographs plus 284 implant and 287 flexion-extension radiographs. From the public data sets, 470 radiographs from Vietnam and 62 from India were reviewed, with no radiographs excluded. A total of 11,103 radiographs were available for final analysis. Key variables included demographics (age, sex), symptomatic status, implant status, and radiographic sagittal parameters derived from standing lateral views. Four orthopaedic specialists labeled keypoints on the original radiographs, including the corner points of C2 to C7 and the centroid of C2. An algorithm was employed for precise measurement of the C2-6 and C2-7 Cobb angles. The Pearson correlation coefficient was calculated to assess the strength of the correlation between the C2-6 and C2-7 Cobb angles, and a linear regression analysis was applied to derive a predictive equation for the C2-7 Cobb angle based on the C2-6 Cobb angle. Subsequently, the 10,000 standard Chinese standing lateral radiographs were randomly assigned to the training set (80%) and the testing set (20%). An independent validation set (n = 1103) was established to assess robustness, comprising 284 implant radiographs and 287 flexion-extension radiographs from China, together with 470 from Vietnam and 62 from India. Correlation analysis demonstrated a strong positive correlation between the C2-6 and C2-7 Cobb angles in the overall population (r = 0.92; p < 0.001). Machine-learning models incorporating the C2-6 Cobb angle and other sagittal parameters achieved high predictive accuracy for estimating the C2-7 Cobb angle, with Lasso regression performing best (R 2 = 0.93, mean absolute error [MAE] = 2.57). Additionally, strong performance was observed in the validation set (R 2 = 0.95, MAE = 3.21). In the subgroup analysis for the extension in males group, the linear model achieved the best validation results, with R 2 = 0.94 and MAE = 2.52. A strong correlation and high interpretable linear regression results between the C2-6 and C2-7 Cobb angles were observed across different countries, body positions, and implants, suggesting that the C2-6 Cobb angle can serve as a reliable substitute for the C2-7 Cobb angle in radiographic imaging. Further analysis revealed that the C2-6 Cobb angle is approximately 6Ā° smaller than the C2-7 Cobb angle at the population level, which may serve as an important reference for standardized interpretation in clinical evaluation. Machine-learning models achieved high predictive accuracy for estimating the C2-7 Cobb angle, with the best performing model (Lasso regression) achieving an MAE of 2.57, offering an alternative clinical application option. To facilitate clinical use, we provide a freely available online tool ( http://c2-7cobbanglepredictionsystem.online ) that will be maintained for at least 15 years. Level III, diagnostic study.
A novel hybrid segmentation method coupled with deep learning for coronary artery extraction from coronary CT angiography.
Coronary computed tomographic angiography (CCTA) is a non-invasive imaging technique widely used for diagnosing coronary artery disease (CAD), one of the leading causes of mortality in developed countries. Accurate and automatic segmentation of coronary arteries from CCTA is essential for extracting both anatomical and pathological information. Existing deep learning methods suffer from noise artifacts and vessel discontinuities, while classical image processing methods including fixed Hounsfield unit (HU) threshold are highly dependent on scanner characteristics. In this study, we proposed a novel hybrid method that integrated deep learning with our unique mathematical integration of image processing filters, featuring a contour detection algorithm that exploited intensity gradients. The performance of our method was quantitatively evaluated using CCTA scans from 84 patients (internal validation set) and 40 patients from a public dataset (external validation set), with segmentation results compared against manually annotated reference data. We also evaluated existing deep learning-only and classical fixed HU threshold methods against the same reference data for comparison. Our hybrid method demonstrated superior performance with a Dice score of 0.92 (95% confidence interval [CI]: 0.91ā0.93), significantly outperforming deep learning-only (0.68, 95% CI: 0.66ā0.69, pā<ā0.001) and fixed HU threshold methods (0.55, 95% CI: 0.53ā0.56, pā<ā0.001). External validation on public datasets confirmed significantly better performance with a Dice score of 0.82 (95% CI: 0.81ā0.82) compared to deep learning-only (0.76, 95% CI: 0.74ā0.77, pā<ā0.001) and fixed HU threshold methods (0.76, 95% CI: 0.75ā0.77, pā<ā0.001). These results indicate that our hybrid method enables robust and consistent automatic coronary artery segmentation from CCTA, demonstrating potential to aid CAD assessment in clinical practice. The online version contains supplementary material available at 10.1007/s10554-026-03643-7.
Artificial Intelligence, LLM-based generation of synthetic patients with Parkinson's Disease: towards a digital twin paradigm for in silico studies
Heterogeneity in sporadic Parkinsons Disease (PD) is a critical problem that drives variable rates of progression and treatment response and complicates clinical trials. Access to large PD datasets that may help in clustering this heterogeneity is restricted by privacy and regulatory constraints. Simulated patients or digital twins may offer a solution. We developed a large language model (LLM)-framework to generate high-fidelity synthetic PD patients from the Parkinsons Progression Markers Initiative (PPMI) dataset based on the open-source Qwen3-8B-Base model. Using a relational, tree-structured representation and domain-specific fine-tuning, the model produces patient-level records with longitudinal clinical, imaging, and biomarker data. Fidelity was assessed through distributional similarity, correlation structure, and neurologist review. Utility was tested by training diagnostic classifiers, reproducing a published pharmacometric disease progression model applied to in silico trials, and by extracting a stringent dopamine-motor impairment relationship at early PD stages. Privacy was evaluated via identical match share, distance-to-closest-record, and membership inference attacks. These findings support the use of a dedicated LLM framework for patient simulation, contributing to the foundations of digital twins for PD in silico trials.
From the RadAI FDA Approvals Database
Siemens Medical Solutions USA, Inc.
ARTIS icono floor; ARTIS icono biplane; ARTIS icono ceiling; ARTIS pheno
The ARTIS icono and ARTIS pheno are advanced interventional fluoroscopic X-ray systems used by clinicians for real-time imaging during medical procedures. These systems provide enhanced imaging capabilities to guide interventions, improving precision and patient outcomes.
Broncus Medical, Inc.
LungPoint Virtual Bronchoscopic Navigation (VBN) Software
LungPoint Virtual Bronchoscopic Navigation (VBN) Software is designed to assist clinicians by providing virtual navigation through the bronchial pathways of the lungs using medical imaging data. This helps in planning and guiding bronchoscopic procedures by enhancing visualization of the bronchial structures.
ABBOTT MEDICAL
OPTIS Mobile Next Imaging System (1014932); OPTIS Integrated Next Imaging System (1014933); Ultreon 3.0 Software Upgrade Kit (ULTR300001)
The OPTIS Mobile Next and Integrated Next Imaging Systems along with the Ultreon 3.0 Software Upgrade Kit are advanced OCT imaging systems designed for cardiovascular imaging. These devices assist clinicians by providing high-resolution images of blood vessels, aiding in diagnosis and treatment planning for cardiovascular conditions.
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