Foundation models are large models trained on big data which can be used for downstream tasks. In radiology, these models can potentially address several gaps in fairness and generalization, as they can be trained on massive datasets without labelled data and adapted to tasks requiring data with a small number of descriptions. This reduces one of the limiting bottlenecks in clinical model construction-data annotation-as these models can be trained through a variety of techniques that require little more than radiological images with or without their corresponding radiological reports. However, foundation models may be insufficient as they are affected-to a smaller extent when compared with traditional supervised learning approaches-by the same issues that lead to underperforming models, such as a lack of transparency/explainability, and biases. To address these issues, we advocate that the development of foundation models should not only be pursued but also accompanied by the development of a decentralized clinical validation and continuous training framework. This does not guarantee the resolution of the problems associated with foundation models, but it enables developers, clinicians and patients to know when, how and why models should be updated, creating a clinical AI ecosystem that is better capable of serving all stakeholders. CRITICAL RELEVANCE STATEMENT: Foundation models may mitigate issues like bias and poor generalization in radiology AI, but challenges persist. We propose a decentralized, cross-institutional framework for continuous validation and training to enhance model reliability, safety, and clinical utility. KEY POINTS: Foundation models trained on large datasets reduce annotation burdens and improve fairness and generalization in radiology. Despite improvements, they still face challenges like limited transparency, explainability, and residual biases. A decentralized, cross-institutional framework for clinical validation and continuous training can strengthen reliability and inclusivity in clinical AI.

The aim of this study was to evaluate the performance of the YOLOv8 deep learning model for detecting zygomatic fractures. Computed tomography scans with zygomatic fractures were collected, with all slices annotated to identify fracture lines across seven categories: zygomaticomaxillary suture, zygomatic arch, zygomaticofrontal suture, sphenozygomatic suture, orbital floor, zygomatic body, and maxillary sinus wall. The images were divided into training, validation, and test datasets in a 6:2:2 ratio. Performance metrics were calculated for each category. A total of 13,988 axial and 14,107 coronal slices were retrieved. The trained algorithm achieved accuracy of 94.2-97.9%. Recall exceeded 90% across all categories, with sphenozygomatic suture fractures having the highest value (96.6%). Average precision was highest for zygomatic arch fractures (0.827) and lowest for zygomatic body fractures (0.692). The highest F1 score was 96.7% for zygomaticomaxillary suture fractures, and the lowest was 82.1% for zygomatic body fractures. Area under the curve (AUC) values were also highest for zygomaticomaxillary suture (0.943) and lowest for zygomatic body fractures (0.876). The YOLOv8 model demonstrated promising results in the automated detection of zygomatic fractures, achieving the highest performance in identifying fractures of the zygomaticomaxillary suture and zygomatic arch.

Interstitial lung disease (ILD) prognostication incorporates clinical history, pulmonary function testing (PFTs), and chest CT pattern classifications. The machine learning classifier, Fibresolve, includes a model to help detect CT patterns associated with idiopathic pulmonary fibrosis (IPF). We developed and tested new Fibresolve software to predict outcomes in patients with ILD. Fibresolve uses a transformer (ViT) algorithm to analyze CT imaging that additionally embeds PFTs, age, and sex to produce an overall risk score. The model was trained to optimize risk score in a dataset of 602 subjects designed to maximize predictive performance via Cox proportional hazards. Validation was completed with the first hazard ratio assessment dataset, then tested in a second datatest set. 61 % of 220 subjects died in the validation set's study period, whereas 40 % of the 407 subjects died in the second dataset's. The validation dataset's mortality hazard ratio (HR) was 3.66 (95 % CI: 2.09-6.42) and 4.66 (CI: 2.47-8.77) for the moderate and high-risk groups. In the second dataset, Fibresolve was a predictor of mortality at initial visit, with a HR of 2.79 (1.73-4.49) and 5.82 (3.53-9.60) in the moderate and high-risk groups. Similar predictive performance was seen at follow-up visits, as well as with changes in the Fibresolve scores over sequential visits. Fibresolve predicts mortality by automatically assessing combined CT, PFTs, age, and sex into a ViT model. The new software algorithm affords accurate prognostication and demonstrates the ability to detect clinical changes over time.

To establish and validate the utility of computed tomography (CT) radiomics for the prognosis of patients with non-small cell lung cancer (NSCLC). Overall, 215 patients with pathologic diagnosis of NSCLC were included, chest CT images and clinical data were collected before treatment, and follow-up was conducted to assess brain metastasis and survival. Radiomics characteristics were extracted from the chest CT lung window images of each patient, key characteristics were screened, the radiomics score (Radscore) was calculated, and radiomics, clinical, and combined models were constructed using clinically independent predictive factors. A nomogram was constructed based on the final joint model to visualize prediction results. Predictive efficacy was evaluated using the concordance index (C-index), and survival (Kaplan-Meier) and calibration curves were drawn to further evaluate predictive efficacy. The training set included 151 patients (43 with brain metastasis and 108 without brain metastasis) and 64 patients (18 with brain metastasis and 46 without). Multivariate analysis revealed that lymph node metastasis, lymphocyte percentage, and neuron-specific enolase (NSE) were independent predictors of brain metastasis in patients with NSCLC. The area under the curve (AUC) of the these models were 0.733, 0.836, and 0.849, respectively, in the training set and were 0.739, 0.779, and 0.816, respectively, in the validation set. Multivariate Cox regression analysis revealed that the number of brain metastases, distant metastases elsewhere, and C-reactive protein levels were independent predictors of postoperative survival in patients with brain metastases (<i>P</i> < 0.05). The calibration curve exhibited that the predicted values of the prognostic prediction model agreed well with the actual values. The model based on CT radiomics characteristics can effectively predict NSCLC brain metastasis and its prognosis and provide guidance for individualized treatment of NSCLC patients.

Intracerebral hemorrhage (ICH) is a life-threatening condition caused by bleeding in the brain, with high mortality rates, particularly in the acute phase. Accurate diagnosis through medical image segmentation plays a crucial role in early intervention and treatment. However, existing segmentation methods, such as region-growing, clustering, and deep learning, face significant limitations when applied to complex images like ICH, especially in multi-threshold image segmentation (MTIS). As the number of thresholds increases, these methods often become computationally expensive and exhibit degraded segmentation performance. To address these challenges, this paper proposes an Elite-Adaptive-Turbulent Hiking Optimization Algorithm (EATHOA), an enhanced version of the Hiking Optimization Algorithm (HOA), specifically designed for high-dimensional and multimodal optimization problems like ICH image segmentation. EATHOA integrates three novel strategies including Elite Opposition-Based Learning (EOBL) for improving population diversity and exploration, Adaptive k-Average-Best Mutation (AKAB) for dynamically balancing exploration and exploitation, and a Turbulent Operator (TO) for escaping local optima and enhancing the convergence rate. Extensive experiments were conducted on the CEC2017 and CEC2022 benchmark functions to evaluate EATHOA's global optimization performance, where it consistently outperformed other state-of-the-art algorithms. The proposed EATHOA was then applied to solve the MTIS problem in ICH images at six different threshold levels. EATHOA achieved peak values of PSNR (34.4671), FSIM (0.9710), and SSIM (0.8816), outperforming recent methods in segmentation accuracy and computational efficiency. These results demonstrate the superior performance of EATHOA and its potential as a powerful tool for medical image analysis, offering an effective and computationally efficient solution for the complex challenges of ICH image segmentation.

Magnetic Resonance Imaging (MRI) is widely utilized in medical imaging due to its high resolution and non-invasive nature. However, the prolonged acquisition time significantly limits its clinical applicability. Although traditional compressed sensing (CS) techniques can accelerate MRI acquisition, they often lead to degraded reconstruction quality under high undersampling rates. Deep learning-based methods, including CNN- and GAN-based approaches, have improved reconstruction performance, yet are limited by their local receptive fields, making it challenging to effectively capture long-range dependencies. Moreover, these models typically exhibit high computational complexity, which hinders their efficient deployment in practical scenarios. To address these challenges, we propose a lightweight Multi-scale Context-Aware Generative Adversarial Network (MCA-GAN), which enhances MRI reconstruction through dual-domain generators that collaboratively optimize both k-space and image-domain representations. MCA-GAN integrates several lightweight modules, including Depthwise Separable Local Attention (DWLA) for efficient local feature extraction, Adaptive Group Rearrangement Block (AGRB) for dynamic inter-group feature optimization, Multi-Scale Spatial Context Modulation Bridge (MSCMB) for multi-scale feature fusion in skip connections, and Channel-Spatial Multi-Scale Self-Attention (CSMS) for improved global context modeling. Extensive experiments conducted on the IXI, MICCAI 2013, and MRNet knee datasets demonstrate that MCA-GAN consistently outperforms existing methods in terms of PSNR and SSIM. Compared to SepGAN, the latest lightweight model, MCA-GAN achieves a 27.3% reduction in parameter size and a 19.6% reduction in computational complexity, while attaining the shortest reconstruction time among all compared methods. Furthermore, MCA-GAN exhibits robust performance across various undersampling masks and acceleration rates. Cross-dataset generalization experiments further confirm its ability to maintain competitive reconstruction quality, underscoring its strong generalization potential. Overall, MCA-GAN improves MRI reconstruction quality while significantly reducing computational cost through a lightweight architecture and multi-scale feature fusion, offering an efficient and accurate solution for accelerated MRI.

Integration of AI and XR in TAVR is revolutionizing the management of severe aortic stenosis by enhancing diagnostic accuracy, risk stratification, and pre-procedural planning. Advanced algorithms now facilitate precise electrocardiographic, echocardiographic, and CT-based assessments that reduce observer variability and enable patient-specific risk prediction. Immersive XR technologies, including augmented, virtual, and mixed reality, improve spatial visualization of complex cardiac anatomy and support real-time procedural guidance. Despite these advancements, standardized protocols, regulatory frameworks, and ethical safeguards remain necessary for widespread clinical adoption.

Efficient and accurate preoperative assessment of the right-sided heart structural complex (RSHSc) is crucial for planning transcatheter tricuspid valve replacement (TTVR). However, current manual methods remain time-consuming and inconsistent. To address this unmet clinical need, this study aimed to develop and validate TRI-PLAN, the first fully automated, deep learning (DL)-based framework for pre-TTVR assessment. A total of 140 preprocedural computed tomography angiography (CTA) scans (63,962 slices) from patients with severe tricuspid regurgitation (TR) at two high-volume cardiac centers in China were retrospectively included. The patients were divided into a training cohort (n = 100), an internal validation cohort (n = 20), and an external validation cohort (n = 20). TRI-PLAN was developed by a dual-stage right heart assessment network (DRA-Net) to segment the RSHSc and localize the tricuspid annulus (TA), followed by automated measurement of key anatomical parameters and right ventricular ejection fraction (RVEF). Performance was comprehensively evaluated in terms of accuracy, interobserver benchmark comparison, clinical usability, and workflow efficiency. TRI-PLAN achieved expert-level segmentation accuracy (volumetric Dice 0.952/0.955; surface Dice 0.934/0.940), precise localization (standard deviation 1.18/1.14 mm), excellent measurement agreement (ICC 0.984/0.979) and reliable RVEF evaluation (R = 0.97, bias<5 %) across internal and external cohorts. In addition, TRI-PLAN obtained a direct acceptance rate of 80 % and reduced total assessment time from 30 min manually to under 2 min (>95 % time saving). TRI-PLAN provides an accurate, efficient, and clinically applicable solution for pre-TTVR assessment, with strong potential to streamline TTVR planning and enhance procedural outcomes.

Bone health is a critical determinant of spine surgery outcomes, yet many patients undergo procedures without adequate preoperative assessment due to limitations in current bone quality assessment methods. This study aimed to develop and validate an artificial intelligence-based algorithm that predicts Vertebral Bone Quality (VBQ) scores from routine MRI scans, enabling improved preoperative identification of patients at risk for poor surgical outcomes. This study utilized 257 lumbar spine T1-weighted MRI scans from the SPIDER challenge dataset. VBQ scores were calculated through a three-step process: selecting the mid-sagittal slice, measuring vertebral body signal intensity from L1-L4, and normalizing by cerebrospinal fluid signal intensity. A YOLOv8 model was developed to automate region of interest placement and VBQ score calculation. The system was validated against manual annotations from 47 lumbar spine surgery patients, with performance evaluated using precision, recall, mean average precision, intraclass correlation coefficient, Pearson correlation, RMSE, and mean error. The YOLOv8 model demonstrated high accuracy in vertebral body detection (precision: 0.9429, recall: 0.9076, [email protected]: 0.9403, mAP@[0.5:0.95]: 0.8288). Strong interrater reliability was observed with ICC values of 0.95 (human-human), 0.88 and 0.93 (human-AI). Pearson correlations for VBQ scores between human and AI measurements were 0.86 and 0.9, with RMSE values of 0.58 and 0.42 respectively. The AI-based algorithm accurately predicts VBQ scores from routine lumbar MRIs. This approach has potential to enhance early identification and intervention for patients with poor bone health, leading to improved surgical outcomes. Further external validation is recommended to ensure generalizability and clinical applicability.

This study employed machine learning models to quantitatively analyze liver fat content from MRI images for the evaluation of liver fibrosis and disease severity in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). A total of 26 confirmed MAFLD cases, along with MRI image sequences obtained from public repositories, were included to perform a comprehensive assessment. Radiomics features-such as contrast, correlation, homogeneity, energy, and entropy-were extracted and used to construct a random forest classification model with optimized hyperparameters. The model achieved outstanding performance, with an accuracy of 96.8%, sensitivity of 95.7%, specificity of 97.8%, and an F1-score of 96.8%, demonstrating its strong capability in accurately evaluating the degree of liver fibrosis and overall disease severity in MAFLD patients. The integration of machine learning with MRI-based analysis offers a promising approach to enhancing clinical decision-making and guiding treatment strategies, underscoring the potential of advanced technologies to improve diagnostic precision and disease management in MAFLD.