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.

This study aimed to develop radiomic-based machine learning models using computed tomography enterography (CTE) features derived from the intestinal wall, mesenteric fat, and visceral fat to differentiate between ulcerative colitis (UC) and colonic Crohn's disease (CD). Clinical and imaging data from 116 patients with inflammatory bowel disease (IBD) (68 with UC and 48 with colonic CD) were retrospectively collected. Radiomic features were extracted from venous-phase CTE images. Feature selection was performed via the intraclass correlation coefficient (ICC), correlation analysis, SelectKBest, and least absolute shrinkage and selection operator (LASSO) regression. Support vector machine models were constructed using features from individual and combined regions, with model performance evaluated using the area under the ROC curve (AUC). The combined radiomic model, integrating features from all three regions, exhibited superior classification performance (AUC= 0.857, 95% CI, 0.732-0.982), with a sensitivity of 0.762 (95% CI, 0.547-0.903) and specificity of 0.857 (95% CI, 0.601-0.960) in the testing cohort. The models based on features from the intestinal wall, mesenteric fat, and visceral fat achieved AUCs of 0.847 (95% CI, 0.710-0.984), 0.707 (95% CI, 0.526-0.889), and 0.731 (95% CI, 0.553-0.910), respectively, in the testing cohort. The intestinal wall model demonstrated the best calibration. This study demonstrated the feasibility of constructing machine learning models based on radiomic features of the intestinal wall, mesenteric fat, and visceral fat to distinguish between UC and colonic CD.

Accurate and efficient classification of brain tumors, including gliomas, meningiomas, and pituitary adenomas, is critical for early diagnosis and treatment planning. Magnetic resonance imaging (MRI) is a key diagnostic tool, and deep learning models have shown promise in automating tumor classification. However, challenges remain in achieving high accuracy while maintaining interpretability for clinical use. This study explores the use of transfer learning with pre-trained architectures, including VGG16, DenseNet121, and Inception-ResNet-v2, to classify brain tumors from MRI images. An ensemble-based classifier was developed using a majority voting strategy to improve robustness. To enhance clinical applicability, explainability techniques such as Grad-CAM++ and Integrated Gradients were employed, allowing visualization of model decision-making. The ensemble model outperformed individual Convolutional Neural Network (CNN) architectures, achieving an accuracy of 86.17% in distinguishing gliomas, meningiomas, pituitary adenomas, and benign cases. Interpretability techniques provided heatmaps that identified key regions influencing model predictions, aligning with radiological features and enhancing trust in the results. The proposed ensemble-based deep learning framework improves the accuracy and interpretability of brain tumor classification from MRI images. By combining multiple CNN architectures and integrating explainability methods, this approach offers a more reliable and transparent diagnostic tool to support medical professionals in clinical decision-making.

To explore the value of machine learning models based on MRI radiomics and automated habitat analysis in predicting bone metastasis and high-grade pathological Gleason scores in prostate cancer. This retrospective study enrolled 214 patients with pathologically diagnosed prostate cancer from May 2013 to January 2025, including 93 cases with bone metastasis and 159 cases with high-grade Gleason scores. Clinical, pathological and MRI data were collected. An nnUNet model automatically segmented the prostate in MRI scans. K-means clustering identified subregions within the entire prostate in T2-FS images. Senior radiologists manually segmented regions of interest (ROIs) in prostate lesions. Radiomics features were extracted from these habitat subregions and lesion ROIs. These features combined with clinical features were utilized to build multiple machine learning classifiers to predict bone metastasis and high-grade Gleason scores while a K-means clustering method was applied to obtain habitat subregions within the whole prostate. Finally, the models underwent interpretable analysis based on feature importance. The nnUNet model achieved a mean Dice coefficient of 0.970 for segmentation. Habitat analysis using 2 clusters yielded the highest average silhouette coefficient (0.57). Machine learning models based on a combination of lesion radiomics, habitat radiomics, and clinical features achieved the best performance in both prediction tasks. The Extra Trees Classifier achieved the highest AUC (0.900) for predicting bone metastasis, while the CatBoost Classifier performed best (AUC 0.895) for predicting high-grade Gleason scores. The interpretability analysis of the optimal models showed that the PSA clinical feature was crucial for predictions, while both habitat radiomics and lesion radiomics also played important roles. The study proposed an automated prostate habitat analysis for prostate cancer, enabling a comprehensive analysis of tumor heterogeneity. The machine learning models developed achieved excellent performance in predicting the risk of bone metastasis and high-grade Gleason scores in prostate cancer. This approach overcomes the limitations of manual feature extraction, and the inadequate analysis of heterogeneity often encountered in traditional radiomics, thereby improving model performance.

Retinal fundus imaging is crucial for diagnosing and monitoring eye diseases, which are often linked to systemic health conditions such as diabetes and hypertension. Current deep learning techniques often narrowly focus on segmenting retinal blood vessels, lacking a more comprehensive analysis and characterization of the retinal vascular system. This study fills this gap by proposing a novel, integrated approach that leverages multiple stages to accurately determine vessel paths and extract informative features from them. The segmentation of veins and arteries, achieved through a deep semantic segmentation network, is used by a newly designed algorithm to reconstruct individual vessel paths. The reconstruction process begins at the optic disc, identified by a localization network, and uses a recurrent neural network to predict the vessel paths at various junctions. The different stages of the proposed approach are validated both qualitatively and quantitatively, demonstrating robust performance. The proposed approach enables the extraction of critical features at the individual vessel level, such as vessel tortuosity and diameter. This work lays the foundation for a comprehensive retinal image evaluation, going beyond isolated tasks like vessel segmentation, with significant potential for clinical diagnosis.

Extracting clinical entities from unstructured medical documents is critical for improving clinical decision support and documentation workflows. This study examines the performance of various encoder and decoder models trained for Named Entity Recognition (NER) of clinical parameters in pathology and radiology reports, highlighting the applicability of Large Language Models (LLMs) for this task. Three NER methods were evaluated: (1) flat NER using transformer-based models, (2) nested NER with a multi-task learning setup, and (3) instruction-based NER utilizing LLMs. A dataset of 2013 pathology reports and 413 radiology reports, annotated by medical students, was used for training and testing. The performance of encoder-based NER models (flat and nested) was superior to that of LLM-based approaches. The best-performing flat NER models achieved F1-scores of 0.87-0.88 on pathology reports and up to 0.78 on radiology reports, while nested NER models performed slightly lower. In contrast, multiple LLMs, despite achieving high precision, yielded significantly lower F1-scores (ranging from 0.18 to 0.30) due to poor recall. A contributing factor appears to be that these LLMs produce fewer but more accurate entities, suggesting they become overly conservative when generating outputs. LLMs in their current form are unsuitable for comprehensive entity extraction tasks in clinical domains, particularly when faced with a high number of entity types per document, though instructing them to return more entities in subsequent refinements may improve recall. Additionally, their computational overhead does not provide proportional performance gains. Encoder-based NER models, particularly those pre-trained on biomedical data, remain the preferred choice for extracting information from unstructured medical documents.

Acute ischemic stroke (AIS) remains the leading cause of mortality and disability worldwide. While revascularization therapies-such as intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT)-have significantly improved outcomes, their success is strongly influenced by the status of cerebral collateral circulation. Collateral vessels sustain cerebral perfusion during vascular occlusion, limiting infarct growth and extending therapeutic windows. Despite this recognized importance, standardized methods for assessing collateral status and integrating it into treatment strategies are still evolving. This narrative review synthesizes current evidence on the role of collateral circulation in AIS, focusing on its impact on infarct dynamics, treatment efficacy, and functional recovery. We highlight findings from major clinical trials-including MR CLEAN, DAWN, DEFUSE-3, and SWIFT PRIME which consistently demonstrate that robust collateral networks are associated with improved outcomes and expanded eligibility for reperfusion therapies. Advances in neuroimaging, such as multiphase CTA and perfusion MRI, alongside emerging AI-driven automated collateral grading, are reshaping patients' selection and clinical decision-making. We also discuss novel therapeutic strategies aimed at enhancing collateral flow, such as vasodilators, neuroprotective agents, statins, and stem cell therapies. Despite growing evidence supporting collateral-based treatment approaches, real-time clinical implementation remains limited by challenges in standardization and access. Cerebral collateral circulation is a critical determinant of stroke prognosis and treatment response. Incorporating collateral assessment into acute stroke workflows-supported by advanced imaging, artificial intelligence, and personalized medicine-offers a promising pathway to optimize outcomes. As the field moves beyond a strict "time is brain" model, the emerging paradigm of "time is collaterals" may better reflect the dynamic interplay between perfusion, tissue viability, and therapeutic opportunity in AIS management.

This study was undertaken to develop and validate an automatic, artificial intelligence-enhanced software tool for hippocampal sclerosis (HS) detection, using a variety of standard magnetic resonance imaging (MRI) protocols from different MRI scanners for routine clinical practice. First, MRI scans of 36 epilepsy patients with unilateral HS and 36 control patients with epilepsy of other etiologies were analyzed. MRI features, including hippocampal subfield volumes from three-dimensional (3D) magnetization-prepared rapid acquisition gradient echo (MPRAGE) scans and fluid-attenuated inversion recovery (FLAIR) intensities, were calculated. Hippocampal subfield volumes were corrected for total brain volume and z-scored using a dataset of 256 healthy controls. Hippocampal subfield FLAIR intensities were z-scored in relation to each subject's mean cortical FLAIR signal. Additionally, left-right ratios of FLAIR intensities and volume features were obtained. Support vector classifiers were trained on the above features to predict HS presence and laterality. In a second step, the algorithm was validated using two independent, external cohorts, including 118 patients and 116 controls in sum, scanned with different MRI scanners and acquisition protocols. Classifiers demonstrated high accuracy in HS detection and lateralization, with slight variations depending on the input image availability. The best cross-validation accuracy was achieved using both 3D MPRAGE and 3D FLAIR scans (mean accuracy = 1.0, confidence interval [CI] = .939-1.0). External validation of trained classifiers in two independent cohorts yielded accuracies of .951 (CI = .902-.980) and .889 (CI = .805-.945), respectively. In both validation cohorts, the additional use of FLAIR scans led to significantly better classification performance than the use of MPRAGE data alone (p = .016 and p = .031, respectively). A further model was trained on both validation cohorts and tested on the former training cohort, providing additional evidence for good validation performance. Comparison to a previously published algorithm showed no significant difference in performance (p = 1). The method presented achieves accurate automated HS detection using standard clinical MRI protocols. It is robust and flexible and requires no image processing expertise.

Advances in biomarkers and pathophysiology of vascular contributions to cognitive impairment and dementia (VCID) are expected to bring greater mechanistic insights, more targeted treatments, and potentially disease-modifying therapies. The 2025 Annual Workshop of the Albert Research Institute for White Matter and Cognition, sponsored by the Leo and Anne Albert Charitable Trust since 2015, focused on novel biomarkers for VCID. The meeting highlighted the complexity of dementia, emphasizing that the majority of cases involve multiple brain pathologies, with vascular pathology typically present. Potential novel approaches to diagnosis of disease processes and progression that may result in VCID included measures of microglial senescence and retinal changes, as well as artificial intelligence (AI) integration of multimodal datasets. Proteomic studies identified plasma proteins associated with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL; a rare genetic disorder affecting brain vessels) and age-related vascular pathology that suggested potential therapeutic targets. Blood-based microglial and brain-derived extracellular vesicles are promising tools for early detection of brain inflammation and other changes that have been associated with cognitive decline. Imaging measures of blood perfusion, oxygen extraction, and cerebrospinal fluid (CSF) flow were discussed as potential VCID biomarkers, in part because of correlations with classic pathological Alzheimer's disease (AD) biomarkers. MRI-visible perivascular spaces, which may be a novel imaging biomarker of sleep-driven glymphatic waste clearance dysfunction, are associated with vascular risk factors, lower cognitive function, and various brain pathologies including Alzheimer's, Parkinson's and cerebral amyloid angiopathy (CAA). People with Down syndrome are at high risk for dementia. Individuals with Down syndrome who develop dementia almost universally experience mixed brain pathologies, with AD pathology and cerebrovascular pathology being the most common. This follows the pattern in the general population where mixed pathologies are also predominant in the brains of people clinically diagnosed with dementia, including AD dementia. Intimate partner violence-related brain injury, hypertension's impact on dementia risk, and the promise of remote ischemic conditioning for treating VCID were additional themes.

In brain gliomas, non-invasive biomarkers reflecting tumor cellularity would be useful to guide supramarginal resections and to plan stereotactic biopsies. We aim to validate a previously-trained machine learning algorithm that generates cellularity prediction maps (CPM) from multiparametric MRI data to an independent, retrospective external cohort of gliomas undergoing image-guided biopsies, and to compare the performance of CPM and diffusion MRI apparent diffusion coefficient (ADC) in predicting cellularity. A cohort of patients with treatment-naïve or recurrent gliomas were prospectively studied. All patients underwent pre-surgical MRI according to the standardized brain tumor imaging protocol. The surgical sampling site was planned based on image-guided biopsy targets and tissue was stained with hematoxylin-eosin for cell density count. The correlation between MRI-derived CPM values and histological cellularity, and between ADC and histological cellularity, was evaluated both assuming independent observations and accounting for non-independent observations. Sixty-six samples from twenty-seven patients were collected. Thirteen patients had treatment-naïve tumors and fourteen had recurrent lesions. CPM value accurately predicted histological cellularity in treatment-naïve patients (b = 1.4, R² = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cell/mm²), but not in the recurrent sub-cohort. Similarly, ADC values showed a significant association with histological cellularity only in treatment-naive patients (b = 1.3, R² = 0.22, p = 0.007; rho = -0.37, p = 0.03), not statistically different from the CPM correlation. These findings were confirmed with statistical tests accounting for non-independent observations. MRI-derived machine learning generated cellularity prediction maps (CPM) enabled a non-invasive evaluation of tumor cellularity in treatment-naïve glioma patients, although CPM did not clearly outperform ADC alone in this cohort.