Skull damage caused by craniectomy or trauma necessitates accurate and precise Patient-Specific Implant (PSI) design to restore the cranial cavity. Conventional Computer-Aided Design (CAD)-based methods for PSI design are highly infrastructure-intensive, require specialised skills, and are time-consuming, resulting in prolonged patient wait times. Recent advancements in Artificial Intelligence (AI) provide automated, faster and scalable alternatives. This study introduces the Skull Completion using AI Network (SCAI-Net) framework, a deep-learning-based approach for automated cranial defect reconstruction using Computer Tomography (CT) images. The framework proposes two defect reconstruction variants: SCAI-Net-SDR (Subtraction-based Defect Reconstruction), which first reconstructs the full skull, then performs binary subtraction to obtain the reconstructed defect, and SCAI-Net-DDR (Direct Defect Reconstruction), which generates the reconstructed defect directly without requiring full-skull reconstruction. To enhance model robustness, the SCAI-Net was trained on an augmented dataset of 2760 images, created by combining MUG500+ and SkullFix datasets, featuring artificial defects across multiple cranial regions. Unlike subtraction-based SCAI-Net-SDR, which requires full-skull reconstruction before binary subtraction, and conventional CAD-based methods, which rely on interpolation or mirroring, SCAI-Net-DDR significantly reduces computational overhead. By eliminating the full-skull reconstruction step, DDR reduces training time by 66 % (85 min vs. 250 min for SDR) and achieves a 99.996 % faster defect reconstruction time compared to CAD (0.1s vs. 2400s). Based on the quantitative evaluation conducted on the SkullFix test cases, SCAI-Net-DDR emerged as the leading model among all evaluated approaches. SCAI-Net-DDR achieved the highest Dice Similarity Coefficient (DSC: 0.889), a low Hausdorff Distance (HD: 1.856 mm), and a superior Structural Similarity Index (SSIM: 0.897). Similarly, within the subset of subtraction-based reconstruction approaches evaluated, SCAI-Net-SDR demonstrated competitive performance, achieving the best HD (1.855 mm) and the highest SSIM (0.889), confirming its strong standing among methods using the subtraction paradigm. SCAI-Net generates reconstructed defects, which undergo post-processing to ensure manufacturing readiness. Steps include surface smoothing, thickness validation and edge preparation for secure fixation and seamless digital manufacturing compatibility. End-to-end implant generation time for DDR demonstrated a 96.68 % reduction (93.5 s), while SDR achieved a 96.64 % reduction (94.6 s), significantly outperforming CAD-based methods (2820s). Finite Element Analysis (FEA) confirmed the SCAI-Net-generated implants' robust load-bearing capacity under extreme loading (1780N) conditions, while edge gap analysis validated precise anatomical fit. Clinical validation further confirmed boundary accuracy, curvature alignment, and secure fit within cranial cavity. These results position SCAI-Net as a transformative, time-efficient, and resource-optimized solution for AI-driven cranial defect reconstruction and implant generation.

Coronary artery calcium (CAC) scoring is an important method for cardiovascular risk assessment. While artificial intelligence (AI) has been applied to automate CAC scoring in calcium scoring computed tomography (CSCT), its application to low-dose computed tomography (LDCT) scans, typically used for lung cancer screening, remains challenging due to the lower image quality and higher noise levels of LDCT. This study presents a diffusion model-based method for converting LDCT to CSCT images with the aim of improving CAC scoring accuracy from LDCT scans. A conditional diffusion model was developed to generate CSCT images from LDCT by modifying the denoising diffusion implicit model (DDIM) sampling process. Two main modifications were introduced: (1) random pointing, a novel sampling technique that enhances the trajectory guidance methodology of DDIM using stochastic Gaussian noise to optimize domain adaptation, and (2) intermediate sampling, an advanced methodology that strategically injects minimal noise into LDCT images prior to sampling to maximize structural preservation. The model was trained on LDCT and CSCT images obtained from the same patients but acquired separately at different time points and patient positions, and validated on 37 test cases. The proposed method showed superior performance compared to widely used image-to-image models (CycleGAN, CUT, DCLGAN, NEGCUT) across several evaluation metrics, including peak signal-to-noise ratio (39.93 ± 0.44), Local Normalized Cross-Correlation (0.97 ± 0.01), structural similarity index (0.97 ± 0.01), and Dice similarity coefficient (0.73 ± 0.10). The modifications to the sampling process reduced the number of iterations from 1000 to 10 while maintaining image quality. Volumetric analysis indicated a stronger correlation between the calcium volumes in the enhanced CSCT images and expert-verified annotations, as compared to the original LDCT images. The proposed method effectively transforms LDCT images to CSCT images while preserving anatomical structures and calcium deposits. The reduction in sampling time and the improved preservation of calcium structures suggest that the method could be applicable for clinical use in cardiovascular risk assessment.

To assess the technical success of radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC), an artificial intelligence (AI) model was developed to estimate the tumor coverage without the need for segmentation or registration tools. A secondary retrospective analysis of 550 patients in the multicenter and multinational OPTIMA trial (3-7 cm solidary HCC lesions, randomized to RFA or RFA + LTLD) identified 182 patients with well-defined pre-RFA tumor and 1-month post-RFA devascularized ablation zones on enhanced CT. The ground-truth, or percent tumor coverage, was determined based on the result of semi-automatic 3D tumor and ablation zone segmentation and elastic registration. The isocenter of the tumor and ablation was isolated on 2D axial CT images. Feature extraction was performed, and classification and linear regression models were built. Images were augmented, and 728 image pairs were used for training and testing. The estimated percent tumor coverage using the models was compared to ground-truth. Validation was performed on eight patient cases from a separate institution, where RFA was performed, and pre- and post-ablation images were collected. In testing cohorts, the best model accuracy was with classification and moderate data augmentation (AUC = 0.86, TPR = 0.59, and TNR = 0.89, accuracy = 69%) and regression with random forest (RMSE = 12.6%, MAE = 9.8%). Validation in a separate institution did not achieve accuracy greater than random estimation. Visual review of training cases suggests that poor tumor coverage may be a result of atypical ablation zone shrinkage 1 month post-RFA, which may not be reflected in clinical utilization. An AI model that uses 2D images at the center of the tumor and 1 month post-ablation can accurately estimate ablation tumor coverage. In separate validation cohorts, translation could be challenging.

Identification of the mandibular incisive canal (MIC) prior to anterior implant placement is often challenging. The present study aimed to validate an enhanced artificial intelligence (AI)-driven model dedicated to automated segmentation of MIC on cone beam computed tomography (CBCT) scans and to compare its accuracy and time efficiency with simultaneous segmentation of both mandibular canal (MC) and MIC by either human experts or a previously trained AI model. An enhanced AI model was developed based on 100 CBCT scans using expert-optimized MIC segmentation within the Virtual Patient Creator platform. The performance of the enhanced AI model was tested against human experts and a previously trained AI model using another 40 CBCT scans. Performance metrics included intersection over union (IoU), dice similarity coefficient (DSC), recall, precision, accuracy, and root mean square error (RSME). Time efficiency was also evaluated. The enhanced AI model had IoU of 93%, DSC of 93%, recall of 94%, precision of 93%, accuracy of 99%, and RMSE of 0.23 mm. These values were significantly higher than those of the previously trained AI model for all metrics, and for manual segmentation for IoU, DSC, recall, and accuracy (p < 0.0001). The enhanced AI model demonstrated significant time efficiency, completing segmentation in 17.6 s (125 times faster than manual segmentation) (p < 0.0001). The enhanced AI model proved to allow a unique and accurate automated MIC segmentation with high accuracy and time efficiency. Besides, its performance was superior to human expert segmentation and a previously trained AI model segmentation.

To evaluate the diagnostic performance of lumbar spine CT using deep learning denoising (DLD CT) for detecting disc herniation and spinal stenosis. This retrospective study included 47 patients (229 intervertebral discs from L1/2 to L5/S1; 18 men and 29 women; mean age, 69.1 ± 10.9 years) who underwent lumbar spine CT and MRI within 1 month. CT images were reconstructed using filtered back projection (FBP) and denoised using a deep learning algorithm (ClariCT.AI). Three radiologists independently evaluated standard CT and DLD CT at an 8-week interval for the presence of disc herniation, central canal stenosis, and neural foraminal stenosis. Subjective image quality and diagnostic confidence were also assessed using five-point Likert scales. Standard CT and DLD CT were compared using MRI as a reference standard. DLD CT showed higher sensitivity (60% (70/117) vs. 44% (51/117); p < 0.001) and similar specificity (94% (534/570) vs. 94% (538/570); p = 0.465) for detecting disc herniation. Specificity for detecting spinal canal stenosis and neural foraminal stenosis was higher in DLD CT (90% (487/540) vs. 86% (466/540); p = 0.003, 94% (1202/1272) vs. 92% (1171/1272); p < 0.001), while sensitivity was comparable (81% (119/147) vs. 77% (113/147); p = 0.233, 83% (85/102) vs. 81% (83/102); p = 0.636). Image quality and diagnostic confidence were superior for DLD CT (all comparisons, p < 0.05). Compared to standard CT, DLD CT can improve diagnostic performance in detecting disc herniation and spinal stenosis with superior image quality and diagnostic confidence. Question The accurate diagnosis of disc herniation and spinal stenosis is limited on lumbar spine CT because of the low soft-tissue contrast. Findings Lumbar spine CT using deep learning denoising (DLD CT) demonstrated superior diagnostic performance in detecting disc herniation and spinal stenosis compared to standard CT. Clinical relevance DLD CT can be used as a simple and cost-effective screening test.

Artificial intelligence (AI) tools for large vessel occlusion (LVO) detection are increasingly used in acute stroke triage to expedite diagnosis and intervention. However, variability in access and workflow integration limits their potential impact. This study assessed current usage patterns, access disparities, and integration levels across U.S. stroke programs. Cross-sectional, web-based survey of 97 multidisciplinary stroke care providers from diverse institutions. Descriptive statistics summarized demographics, AI tool usage, access, and integration. Two-proportion Z-tests assessed differences across institutional types. Most respondents (97.9%) reported AI tool use, primarily Viz AI and Rapid AI, but only 62.1% consistently used them for triage prior to radiologist interpretation. Just 37.5% reported formal protocol integration, and 43.6% had designated personnel for AI alert response. Access varied significantly across departments, and in only 61.7% of programs did all relevant team members have access. Formal implementation of the AI detection tools did not differ based on the certification (z = -0.2; <i>p</i> = 0.4) or whether the program was academic or community-based (z =-0.3; <i>p</i> = 0.3). AI-enabled LVO detection tools have the potential to improve stroke care and patient outcomes by expediting workflows and reducing treatment delays. This survey effectively evaluated current utilization of these tools and revealed widespread adoption alongside significant variability in access, integration, and workflow standardization. Larger, more diverse samples are needed to validate these findings across different hospital types, and further prospective research is essential to determine how formal integration of AI tools can enhance stroke care delivery, reduce disparities, and improve clinical outcomes.

Predicting the final infarct after an extended time window mechanical thrombectomy (MT) is beneficial for treatment planning in acute ischemic stroke (AIS). By introducing guidance from prior knowledge, this study aims to improve the accuracy of the deep learning model for post-MT infarct prediction using pre-MT brain perfusion data. This retrospective study collected CT perfusion data at admission for AIS patients receiving MT over 6 hours after symptom onset, from January 2020 to December 2024, across three centers. Infarct on post-MT diffusion weighted imaging served as ground truth. Five Swin transformer based models were developed for post-MT infarct segmentation using pre-MT CT perfusion parameter maps: BaselineNet served as the basic model for comparative analysis, CollateralFlowNet included a collateral circulation evaluation score, InfarctProbabilityNet incorporated infarct probability mapping, ArterialTerritoryNet was guided by artery territory mapping, and UnifiedNet combined all prior knowledge sources. Model performance was evaluated using the Dice coefficient and intersection over union (IoU). A total of 221 patients with AIS were included (65.2% women) with a median age of 73 years. Baseline ischemic core based on CT perfusion threshold achieved a Dice coefficient of 0.50 and IoU of 0.33. BaselineNet improved to a Dice coefficient of 0.69 and IoU of 0.53. Compared with BaselineNet, models incorporating medical knowledge demonstrated higher performance: CollateralFlowNet (Dice coefficient 0.72, IoU 0.56), InfarctProbabilityNet (Dice coefficient 0.74, IoU 0.58), ArterialTerritoryNet (Dice coefficient 0.75, IoU 0.60), and UnifiedNet (Dice coefficient 0.82, IoU 0.71) (all P<0.05). In this study, integrating medical knowledge into deep learning models enhanced the accuracy of infarct predictions in AIS patients undergoing extended time window MT.

Scanner-related changes in data quality are common in medical imaging, yet monitoring their impact on diagnostic AI performance remains challenging. In this study, we performed standardized consistency testing of an FDA-cleared and CE-marked AI for triage and notification of intracranial aneurysms across changes in image data quality caused by dose and image reconstruction. Our assessment was based on repeated examinations of a head CT phantom designed for AI evaluation, replicating a patient with three intracranial aneurysms in the anterior, middle and posterior circulation. We show that the AI maintains stable performance within the medium dose range but produces inconsistent results at reduced dose and, unexpectedly, at higher dose when filtered back projection is used. Data quality standards required for AI are stricter than those for neuroradiologists, who report higher aneurysm visibility rates and experience performance degradation only at substantially lower doses, with no decline at higher doses.

Chronic Obstructive Pulmonary Disease (COPD) is a significant public health challenge globally, with Asia facing unique burdens due to varying demographics, healthcare access, and socioeconomic conditions. Recognizing the limitations of pulmonary function tests (PFTs) in early detection and comprehensive evaluation, the Asian Society of Thoracic Radiology (ASTR) presents this recommendations to guide the use of chest computed tomography (CT) in COPD diagnosis and management. This document consolidates evidence from an extensive literature review and surveys across Asia, highlighting the need for standardized CT protocols and practices. Key recommendations include adopting low-dose paired respiratory phase CT scans, utilizing qualitative and quantitative assessments for airway, vascular, and parenchymal evaluation, and emphasizing structured reporting to enhance clinical decision-making. Advanced technologies, including dual-energy CT and artificial intelligence, are proposed to refine diagnosis, monitor disease progression, and guide personalized interventions. These recommendations aim to improve the early detection of COPD, address its heterogeneity, and reduce its socioeconomic impact by establishing consistent and effective imaging practices across the region. This recommendations underscore the pivotal role of chest CT in advancing COPD care in Asia, providing a foundation for future research and practice refinement.