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.

Transcatheter heart valve (THV) durability is a critical concern, and its deformation may influence long-term performance. Current assessments rely on CT-based single-phase measurements and require a tedious analysis process, potentially overlooking deformation dynamics throughout the cardiac cycle. A fully automated artificial intelligence-based method was developed to assess THV deformation in post-transcatheter aortic valve implantation (TAVI) 4DCT scans. The approach involves segmenting the THV, extracting orthogonal cross-sections along its axis, fitting ellipses to these cross-sections, and computing eccentricity to analyze deformation over the cardiac cycle. The method was evaluated in 21 TAVI patients with different self-expandable THV models, using one post-TAVI 4DCT series per patient. The THV inflow level exhibited the greatest eccentricity variations (0.35-0.69 among patients with the same THV model at end-diastole). Additionally, eccentricity varied throughout the cardiac cycle (0.23-0.57), highlighting the limitations of single-phase assessments in characterizing THV deformation. This method enables automated THV deformation assessment based on cross-sectional eccentricity. Significant differences were observed at the inflow level, and cyclic variations suggest that full cardiac cycle analysis provides a more comprehensive evaluation than single-phase measurements. This approach may aid in optimizing THV durability and function while preventing related complications.

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.

Photon-counting detector CT (PCD-CT) represents a significant advancement in medical imaging, particularly for musculoskeletal (MSK) applications. Its primary innovation lies in enhanced spatial resolution, which facilitates improved detection of small anatomical structures such as trabecular bone, osteophytes, and subchondral cysts. PCD-CT enables high-quality imaging with reduced radiation doses, making it especially beneficial for populations requiring frequent imaging, such as pediatric patients and individuals with multiple myeloma. Additionally, PCD-CT supports advanced applications like bone quality assessment, which correlates well with gold-standard tests, and can aid in diagnosing osteoporosis and assessing fracture risk. Techniques such as spectral shaping and virtual monoenergetic imaging further optimize the technology, minimizing artifacts and enhancing material decomposition. These capabilities extend to conditions like gout and hematologic malignancies, offering improved detection and assessment. The integration of artificial intelligence could enhance PCD-CT's performance by reducing image noise and improving quantitative assessments. Ultimately, PCD-CT's superior resolution, reduced dose protocols, and multi-energy imaging capabilities will likely have a transformative impact on MSK imaging, improving diagnostic accuracy, patient care, and clinical outcomes.

Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms for detecting and diagnosing dental caries using cone-beam computed tomography (CBCT) with the Mask R-CNN architecture while comparing various hyperparameters to enhance detection. A total of 2,128 CBCT images were divided into training and validation and test datasets in a 7:1:1 ratio. For the verification of tooth recognition, the data from the validation set were randomly selected for analysis. Three groups of Mask R-CNN networks were compared: A scratch-trained baseline using randomly initialized weights (R group); A transfer learning approach with models pre-trained on COCO for object detection (C group); A variant pre-trained on ImageNetfor for object detection (I group). All configurations maintained identical hyperparameter settings to ensure fair comparison. The deep learning model used ResNet-50 as the backbone network and was trained to 300epoch respectively. We assessed training loss, detection and training times, diagnostic accuracy, specificity, positive and negative predictive values, and coverage precision to compare performance across the groups. Transfer learning significantly reduced training times compared to non-transfer learning approach (p < 0.05). The average detection time for group R was 0.269 ± 0.176 s, whereas groups I (0.323 ± 0.196 s) and C (0.346 ± 0.195 s) exhibited significantly longer detection times (p < 0.05). C-group, trained for 200 epochs, achieved a mean average precision (mAP) of 81.095, outperforming all other groups. The mAP for caries recognition in group R, trained for 300 epochs, was 53.328, with detection times under 0.5 s. Overall, C-group demonstrated significantly higher average precision across all epochs (100, 200, and 300) (p < 0.05). Neural networks pre-trained with COCO transfer learning exhibit superior annotation accuracy compared to those pre-trained with ImageNet. This suggests that COCO's diverse and richly annotated images offer more relevant features for detecting dental structures and carious lesions. Furthermore, employing ResNet-50 as the backbone architecture enhances the detection of teeth and carious regions, achieving significant improvements with just 200 training epochs, potentially increasing the efficiency of clinical image interpretation.

Aortic dissection (AD) is a rare condition with a high mortality rate, necessitating accurate and rapid diagnosis. This study develops an automated deep learning pipeline for identifying, segmenting, and Stanford subtyping AD using computed tomography angiography (CTA) images. This pipeline consists of four interconnected modules: aorta segmentation, AD identification, true lumen (TL) and false lumen (FL) segmentation, and Stanford subtyping. In the aorta segmentation module, a 3D full-resolution nnU-Net is trained. The segmented aorta's boundary is extracted using morphological operations and projected from multiple views in the AD identification module. AD identification is then performed using the multi-view projection data. For AD cases, a 3D nnU-Net is further trained for TL/FL segmentation based on the segmented aorta. Finally, a network is trained for Stanford subtyping using multi-view maximum density projections of the segmented TL/FL. A total of 386 CTA scans were collected for training, validation, and testing of the pipeline. For AD identification, the method achieved an accuracy of 0.979. The TL/FL segmentation for TypeA-AD and Type-B-AD achieved average Dice coefficient of 0.968 for TL and 0.971 for FL. For Stanford subtyping, the multi-view method achieved an accuracy of 0.990. The automated pipeline enables rapid and accurate identification, segmentation, and Stanford subtyping of AD using CTA images, potentially accelerating the diagnosis and treatment. The segmented aorta and TL/FL can also serve as references for physicians. The code, models, and pipeline are publicly available at https://github.com/zhuangCJ/A-pipeline-of-AD.git .

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.

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.

This study aims to evaluate the predictive value of multiparameter characteristics of coronary computed tomography angiography (CCTA) plaque and the ratio of coronary artery volume to myocardial mass (V/M) in guiding percutaneous coronary stent implantation (PCI) in patients diagnosed with unstable angina. Patients who underwent CCTA and coronary angiography (CAG) within 2 months were retrospectively analyzed. According to CAG results, patients were divided into a medical therapy group (n=41) and a PCI revascularization group (n=37). The plaque characteristics and V/M were quantitatively evaluated. The parameters included minimum lumen area at stenosis (MLA), maximum area stenosis (MAS), maximum diameter stenosis (MDS), total plaque burden (TPB), plaque length, plaque volume, and each component volume within the plaque. Fractional flow reserve (FFR) and pericoronary fat attenuation index (FAI) were calculated based on CCTA. Artificial intelligence software was employed to compare the differences in each parameter between the 2 groups at both the vessel and plaque levels. The PCI group had higher MAS, MDS, TPB, FAI, noncalcified plaque volume and lipid plaque volume, and significantly lower V/M, MLA, and CT-derived fractional flow reserve (FFRCT). V/M, TPB, MLA, FFRCT, and FAI are important influencing factors of PCI. The combined model of MLA, FFRCT, and FAI had the largest area under the ROC curve (AUC=0.920), and had the best performance in predicting PCI. The integration of AI-derived multiparameter features from one-stop CCTA significantly enhances the accuracy of predicting PCI in angina pectoris patients, evaluating at the plaque, vessel, and patient levels.

Despite elective repair of a large portion of stable abdominal aortic aneurysms (AAAs), the diameter criterion cannot prevent all small AAA ruptures. Since rupture depends on many factors, this study explored whether machine learning (ML) models (logistic regression [LogR], linear and non-linear support vector machine [SVM-Lin and SVM-Nlin], and Gaussian Naïve Bayes [GNB]) might improve the diameter based risk assessment by comparing already ruptured (diameter 52.8 - 174.5 mm) with asymptomatic (diameter 40.4 - 95.5 mm) aortas. A retrospective case-control observational study included ruptured AAAs from two centres (2010 - 2012) with computed tomography angiography images for finite element analysis. Clinical patient data and geometric and biomechanical AAA properties were fed into ML models, whose output was compared with the results from intact cases. Classifications were explored for all cases and those having diameters below 70 mm. All data trained and validated the ML models, with a five fold cross-validation. SHapley Additive exPlanations (SHAP) analysis ranked the factors for rupture identification. One hundred and seven ruptured (20% female, mean age 77 years, mean diameter 86.3 mm) and 200 non-ruptured aneurysmal infrarenal aortas (22% female, mean age 74 years, mean diameter 57 mm) were investigated through cross-validation methods. Given the entire dataset, the diameter threshold of 55 mm in men and 50 mm in women provided a 58% accurate rupture classification. It was 99% sensitive (AAA rupture identified correctly) and 36% specific (intact AAAs identified correctly). ML models improved accuracy (LogR 90.2%, SVM-Lin 89.48%, SVM-Nlin 88.7%, and GNB 86.4%); accuracy decreased when trained on the ≤ 70 mm group (55/50 mm diameter threshold 44.2%, LogR 82.5%, SVM-Lin 83.6%, SVM-Nlin 65.9%, and GNB: 84.7%). SHAP ranked biomechanical parameters other than the diameter as the most relevant. A multiparameter estimate enhanced the purely diameter based approach. The proposed predictability method should be further tested in longitudinal studies.