Coronary computed tomography angiography (CCTA) is a first-line investigation for chest pain in patients with suspected obstructive coronary artery disease (CAD). However, many acute cardiac events occur in the absence of obstructive CAD. We assessed the lifetime cost-effectiveness of integrating a novel artificial intelligence-enhanced image analysis algorithm (AI-Risk) that stratifies the risk of cardiac events by quantifying coronary inflammation, combined with the extent of coronary artery plaque and clinical risk factors, by analysing images from routine CCTA. A hybrid decision-tree with population cohort Markov model was developed from 3393 consecutive patients who underwent routine CCTA for suspected obstructive CAD and followed up for major adverse cardiac events over a median (interquartile range) of 7.7(6.4-9.1) years. In a prospective real-world evaluation survey of 744 consecutive patients undergoing CCTA for chest pain investigation, the availability of AI-Risk assessment led to treatment initiation or intensification in 45% of patients. In a further prospective study of 1214 consecutive patients with extensive guidelines recommended cardiovascular risk profiling, AI-Risk stratification led to treatment initiation or intensification in 39% of patients beyond the current clinical guideline recommendations. Treatment guided by AI-Risk modelled over a lifetime horizon could lead to fewer cardiac events (relative reductions of 11%, 4%, 4%, and 12% for myocardial infarction, ischaemic stroke, heart failure, and cardiac death, respectively). Implementing AI-Risk Classification in routine interpretation of CCTA is highly likely to be cost-effective (incremental cost-effectiveness ratio £1371-3244), both in scenarios of current guideline compliance, or when applied only to patients without obstructive CAD. Compared with standard care, the addition of AI-Risk assessment in routine CCTA interpretation is cost-effective, by refining risk-guided medical management.

Lung cancer (LC) remains a leading cause of mortality worldwide, affecting individuals across all genders and age groups. Early and accurate diagnosis is critical for effective treatment and improved survival rates. Computed Tomography (CT) imaging is widely used for LC detection and classification. However, manual identification can be time-consuming and error-prone due to the visual similarities among various LC types. Deep learning (DL) has shown significant promise in medical image analysis. Although numerous studies have investigated LC detection using deep learning techniques, the effective extraction of highly correlated features remains a significant challenge, thereby limiting diagnostic accuracy. Furthermore, most existing models encounter substantial computational complexity and find it difficult to efficiently handle the high-dimensional nature of CT images. This study introduces an optimized CBAM-EfficientNet model to enhance feature extraction and improve LC classification. EfficientNet is utilized to reduce computational complexity, while the Convolutional Block Attention Module (CBAM) emphasizes essential spatial and channel features. Additionally, optimization algorithms including Gray Wolf Optimization (GWO), Whale Optimization (WO), and the Bat Algorithm (BA) are applied to fine-tune hyperparameters and boost predictive accuracy. The proposed model, integrated with different optimization strategies, is evaluated on two benchmark datasets. The GWO-based CBAM-EfficientNet achieves outstanding classification accuracies of 99.81% and 99.25% on the Lung-PET-CT-Dx and LIDC-IDRI datasets, respectively. Following GWO, the BA-based CBAM-EfficientNet achieves 99.44% and 98.75% accuracy on the same datasets. Comparative analysis highlights the superiority of the proposed model over existing approaches, demonstrating strong potential for reliable and automated LC diagnosis. Its lightweight architecture also supports real-time implementation, offering valuable assistance to radiologists in high-demand clinical environments.

Aimed to develop and validate a CT radiomics-based explainable machine learning model for diagnosing malignancy and benignity specifically in endometrial cancer (EC) patients. A total of 83 EC patients from two centers, including 46 with malignant and 37 with benign conditions, were included, with data split into a training set (n=59) and a testing set (n=24). The regions of interest (ROIs) were manually segmented from pre-surgical CT scans, and 1132 radiomic features were extracted from the pre-surgical CT scans using Pyradiomics. Six explainable machine learning modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. The diagnostic performance of the radiomic model was evaluated by using sensitivity, specificity, accuracy, precision, F1 score, confusion matrices, and ROC curves. To enhance clinical understanding and usability, we separately implemented SHAP analysis and feature mapping visualization, and evaluated the calibration curve and decision curve. By comparing six modeling strategies, the Random Forest model emerged as the optimal choice for diagnosing EC, with a training AUC of 1.00 and a testing AUC of 0.96. SHAP identified the most important radiomic features, revealing that all selected features were significantly associated with EC (P < 0.05). Radiomics feature maps also provide a feasible assessment tool for clinical applications. DCA indicated a higher net benefit for our model compared to the "All" and "None" strategies, suggesting its clinical utility in identifying high-risk cases and reducing unnecessary interventions. In conclusion, the CT radiomics-based explainable machine learning model achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of endometrial cancer.

Federated learning has significantly advanced distributed training of machine learning models across decentralized data sources. However, existing frameworks often lack comprehensive solutions that combine uncertainty quantification, interpretability, and robustness. To address this, we propose FedNAM+, a federated learning framework that integrates Neural Additive Models (NAMs) with a novel conformal prediction method to enable interpretable and reliable uncertainty estimation. Our method introduces a dynamic level adjustment technique that utilizes gradient-based sensitivity maps to identify key input features influencing predictions. This facilitates both interpretability and pixel-wise uncertainty estimates. Unlike traditional interpretability methods such as LIME and SHAP, which do not provide confidence intervals, FedNAM+ offers visual insights into prediction reliability. We validate our approach through experiments on CT scan, MNIST, and CIFAR datasets, demonstrating high prediction accuracy with minimal loss (e.g., only 0.1% on MNIST), along with transparent uncertainty measures. Visual analysis highlights variable uncertainty intervals, revealing low-confidence regions where model performance can be improved with additional data. Compared to Monte Carlo Dropout, FedNAM+ delivers efficient and global uncertainty estimates with reduced computational overhead, making it particularly suitable for federated learning scenarios. Overall, FedNAM+ provides a robust, interpretable, and computationally efficient framework that enhances trust and transparency in decentralized predictive modeling.

Considerable challenges exist in managing lung cancer and mesothelioma, including diagnostic complexity, treatment stratification, early detection and imaging quantification. Variable incidence in mesothelioma also makes equitable provision of high-quality care difficult. In this context, artificial intelligence (AI) offers a range of assistive/automated functions that can potentially enhance clinical decision-making, while reducing inequality and pathway delay. In this state-of-the-art narrative review, we synthesise evidence on this topic, focusing particularly on tools that ingest routine pathology and radiology images. We summarise the strengths and weaknesses of AI applied to common multidisciplinary team (MDT) functions, including histological diagnosis, therapeutic response prediction, radiological detection and quantification, and survival estimation. We also review emerging methods capable of generating novel biological insights and current barriers to implementation, including access to high-quality training data and suitable regulatory and technical infrastructure. Neural networks trained on pathology images have proven utility in histological classification, prognostication, response prediction and survival. Self-supervised models can also generate new insights into biological features responsible for adverse outcomes. Radiology applications include lung nodule tools, which offer critical pathway support for imminent lung cancer screening and urgent referrals. Tumour segmentation AI offers particular advantages in mesothelioma, where response assessment and volumetric staging are difficult using human readers due to tumour size and morphological complexity. AI is also critical for radiogenomics, permitting effective integration of molecular and radiomic features for discovery of non-invasive markers for molecular subtyping and enhanced stratification. AI solutions offer considerable potential benefits across the MDT, particularly in repetitive or time-consuming tasks based on pathology and radiology images. Effective leveraging of this technology is critical for lung cancer screening and efficient delivery of increasingly complex diagnostic and predictive MDT functions. Future AI research should involve transparent and interpretable outputs that assist in explaining the basis of AI-supported decision making.

The COVID-19 pandemic plays a significant roles in the global health, highlighting the imperative for effective management of post-recovery symptoms. Within this context, Ground Glass Opacity (GGO) in lung computed tomography CT scans emerges as a critical indicator for early intervention. Recently, most researchers have investigated initially a challenge to refine techniques for GGO segmentation. These approaches aim to scrutinize and juxtapose cutting-edge methods for analyzing lung CT images of patients recuperating from COVID-19. While many methods in this challenge utilize the nnU-Net architecture, its general approach has not concerned completely GGO areas such as marking infected areas, ground-glass opacity, irregular shapes and fuzzy boundaries. This research has investigated a specialized machine learning algorithm, advancing the nn-UNet framework to accurately segment GGO in lung CT scans of post-COVID-19 patients. We propose a novel approach for two-stage image segmentation methods based on nnU-Net 2D and 3D models including lung and shadow image segmentation, incorporating the attention mechanism. The combination models enhance automatic segmentation and models' accuracy when using different error function in the training process. Experimental results show that the proposed model's outcomes DSC score ranks fifth among the compared results. The proposed method has also the second-highest sensitivity value among the methods, which shows that this method has a higher true segmentation rate than most of the other methods. The proposed method has achieved a Hausdorff95 of 54.566, Surface dice of 0.7193, Sensitivity of 0.7528, and Specificity of 0.7749. As compared with the state-of-the-art methods, the proposed model in experimental results is improved much better than the current methods in term of segmentation of infected areas. The proposed model has been deployed in the case study of real-world problems with the combination of 2D and 3D models. It is demonstrated the capacity to comprehensively detect lung lesions correctly. Additionally, the boundary loss function has assisted in achieving more precise segmentation for low-resolution images. Initially segmenting lung area has reduced the volume of images requiring processing, while diminishing for training process.

In silico analysis is key to understanding bone structure-function relationships in orthopedics and evolutionary biology, but its potential is limited by a lack of standardized, high-quality human bone morphology datasets. This absence hinders research reproducibility and the development of reliable computational models. To overcome this, BoneDat has been developed. It is a comprehensive database containing standardized bone morphology data from 278 clinical lumbopelvic CT scans (pelvis and lower spine). The dataset includes individuals aged 16 to 91, balanced by sex across ten age groups. BoneDat provides curated segmentation masks, normalized bone geometry (volumetric meshes), and reference morphology templates organized by sex and age. By offering standardized reference geometry and enabling shape normalization, BoneDat enhances the repeatability and credibility of computational models. It also allows for integrating other open datasets, supporting the training and benchmarking of deep learning models and accelerating their path to clinical use.

Cone-beam computed tomography (CBCT) using only a few X-ray projection views enables faster scans with lower radiation dose, but the resulting severe under-sampling causes strong artifacts and poor spatial coverage. We address these challenges in a unified framework. First, we replace conventional UNet/ResNet encoders with TransUNet, a hybrid CNN-Transformer model. Convolutional layers capture local details, while self-attention layers enhance global context. We adapt TransUNet to CBCT by combining multi-scale features, querying view-specific features per 3D point, and adding a lightweight attenuation-prediction head. This yields Trans-CBCT, which surpasses prior baselines by 1.17 dB PSNR and 0.0163 SSIM on the LUNA16 dataset with six views. Second, we introduce a neighbor-aware Point Transformer to enforce volumetric coherence. This module uses 3D positional encoding and attention over k-nearest neighbors to improve spatial consistency. The resulting model, Trans$^2$-CBCT, provides an additional gain of 0.63 dB PSNR and 0.0117 SSIM. Experiments on LUNA16 and ToothFairy show consistent gains from six to ten views, validating the effectiveness of combining CNN-Transformer features with point-based geometry reasoning for sparse-view CBCT reconstruction.

Ablation for atrial fibrillation targets an arrhythmogenic substrate in the left atrium (LA) myocardium with therapeutic energy, resulting in a scar tissue. Although a global LA function typically improves after ablation, the injured tissue is stiffer and non-contractile. The local functional impact of ablation has not been thoroughly investigated. This study retrospectively analyzed the LA mechanics of 15 subjects who received a four-dimensional computed tomography (4DCT) scan pre- and post-ablation for atrial fibrillation. LA volumes were automatically segmented at every frame by a trained neural network and converted into surface meshes. A local endocardial strain was computed at a resolution of 2 mm from the deforming meshes. The LA endocardial surface was automatically divided into five walls and further into 24 sub-segments using the left atrial positioning system. Intraoperative notes gathered during the ablation procedure informed which regions received ablative treatment. In an average of 18 months after ablation, the strain is decreased by 16.3% in the septal wall and by 18.3% in the posterior wall. In subjects who were imaged in sinus rhythm both before and after the procedure, the effect of ablation reduced the regional strain by 15.3% (p = 0.012). Post-ablation strain maps demonstrated spatial patterns of reduced strain which matched the ablation pattern. This study demonstrates the capability of 4DCT to capture high-resolution changes in the left atrial strain in response to tissue damage and explores the quantification of a regionally reduced LA function from the scar tissue.

Neurosurgical evaluation is required in the setting of spinal metastases at high risk for leading to a vertebral body fracture. Both irradiated and nonirradiated vertebrae are affected. Understanding fracture risk is critical in determining management, including follow-up timing and prophylactic interventions. Herein, the authors report the results of a machine learning model that predicts the development or progression of a pathological vertebral compression fracture (VCF) in metastatic tumor-infiltrated thoracolumbar vertebrae in an all-comer population. A multi-institutional all-comer cohort of patients with tumor containing vertebral levels spanning T1 through L5 and at least 1 year of follow-up was included in the study. Clinical features of the patients, diseases, and treatments were collected. CT radiomic features of the vertebral bodies were extracted from tumor-infiltrated vertebrae that did or did not subsequently fracture or progress. Recursive feature elimination (RFE) of both radiomic and clinical features was performed. The resulting features were used to create a purely clinical model, purely radiomic model, and combined clinical-radiomic model. A Spine Instability Neoplastic Score (SINS) model was created for a baseline performance comparison. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity (with 95% confidence intervals) with tenfold cross-validation. Within 1 year from initial CT, 123 of 977 vertebrae developed VCF. Selected clinical features included SINS, SINS component for < 50% vertebral body collapse, SINS component for "none of the prior 3" (i.e., "none of the above" on the SINS component for vertebral body involvement), histology, age, and BMI. Of the 2015 radiomic features, RFE selected 19 to be used in the pure radiomic model and the combined clinical-radiomic model. The best performing model was a random forest classifier using both clinical and radiomic features, demonstrating an AUROC of 0.86 (95% CI 0.82-0.9), sensitivity of 0.78 (95% CI 0.70-0.84), and specificity of 0.80 (95% CI 0.77-0.82). This performance was significantly higher than the best SINS-alone model (AUROC 0.75, 95% CI 0.70-0.80) and outperformed the clinical-only model but not in a statistically significant manner (AUROC 0.82, 95% CI 0.77-0.87). The authors developed a clinically generalizable machine learning model to predict the risk of a new or progressing VCF in an all-comer population. This model addresses limitations from prior work and was trained on the largest cohort of patients and vertebrae published to date. If validated, the model could lead to more consistent and systematic identification of high-risk vertebrae, resulting in faster, more accurate triage of patients for optimal management.