Dynamic computed tomography (CT) reconstruction faces significant challenges in addressing motion artifacts, particularly for nonperiodic rapid movements such as cardiac imaging with fast heart rates. Traditional methods struggle with the extreme limited-angle problems inherent in nonperiodic cases. Deep learning methods have improved performance but face generalization challenges. Recent implicit neural representation (INR) techniques show promise through self-supervised deep learning, but have critical limitations: computational inefficiency due to forward-warping modeling, difficulty balancing DVF complexity with anatomical plausibility, and challenges in preserving fine details without additional patient-specific pre-scans. This paper presents a novel INR-based framework, BIRD, for nonperiodic dynamic CT reconstruction. It addresses these challenges through four key contributions: (1) backward-warping deformation that enables direct computation of each dynamic voxel with significantly reduced computational cost, (2) diffeomorphism-based DVF regularization that ensures anatomically plausible deformations while maintaining representational capacity, (3) motion-compensated analytical reconstruction that enhances fine details without requiring additional pre-scans, and (4) dimensional-reduction design for efficient 4D coordinate encoding. Through various simulations and practical studies, including digital and physical phantoms and retrospective patient data, we demonstrate the effectiveness of our approach for nonperiodic dynamic CT reconstruction with enhanced details and reduced motion artifacts. The proposed framework enables more accurate dynamic CT reconstruction with potential clinical applications, such as one-beat cardiac reconstruction, cinematic image sequences for functional imaging, and motion artifact reduction in conventional CT scans.

Active stress models in cardiac biomechanics account for the mechanical deformation caused by muscle activity, thus providing a link between the electrophysiological and mechanical properties of the tissue. The accurate assessment of active stress parameters is fundamental for a precise understanding of myocardial function but remains difficult to achieve in a clinical setting, especially when only displacement and strain data from medical imaging modalities are available. This work investigates, through an in-silico study, the application of physics-informed neural networks (PINNs) for inferring active contractility parameters in time-dependent cardiac biomechanical models from these types of imaging data. In particular, by parametrising the sought state and parameter field with two neural networks, respectively, and formulating an energy minimisation problem to search for the optimal network parameters, we are able to reconstruct in various settings active stress fields in the presence of noise and with a high spatial resolution. To this end, we also advance the vanilla PINN learning algorithm with the use of adaptive weighting schemes, ad-hoc regularisation strategies, Fourier features, and suitable network architectures. In addition, we thoroughly analyse the influence of the loss weights in the reconstruction of active stress parameters. Finally, we apply the method to the characterisation of tissue inhomogeneities and detection of fibrotic scars in myocardial tissue. This approach opens a new pathway to significantly improve the diagnosis, treatment planning, and management of heart conditions associated with cardiac fibrosis.

Accurate classification of focal liver lesions is crucial for diagnosis and treatment in hepatology. However, traditional supervised deep learning models depend on large-scale annotated datasets, which are often limited in medical imaging. Recently, Vision-Language models (VLMs) such as Contrastive Language-Image Pre-training model (CLIP) has been applied to image classifications. Compared to the conventional convolutional neural network (CNN), which classifiers image based on visual information only, VLM leverages multimodal learning with text and images, allowing it to learn effectively even with a limited amount of labeled data. Inspired by CLIP, we pro-pose a Liver-VLM, a model specifically designed for focal liver lesions (FLLs) classification. First, Liver-VLM incorporates class information into the text encoder without introducing additional inference overhead. Second, by calculating the pairwise cosine similarities between image and text embeddings and optimizing the model with a cross-entropy loss, Liver-VLM ef-fectively aligns image features with class-level text features. Experimental results on MPCT-FLLs dataset demonstrate that the Liver-VLM model out-performs both the standard CLIP and MedCLIP models in terms of accuracy and Area Under the Curve (AUC). Further analysis shows that using a lightweight ResNet18 backbone enhances classification performance, particularly under data-constrained conditions.

We propose a multimodal spatiotemporal graph neural network (STG) framework to predict colorectal cancer liver metastasis (CRLM) progression. Current clinical models do not effectively integrate the tumor's spatial heterogeneity, dynamic evolution, and complex multimodal data relationships, limiting their predictive accuracy. Our STG framework combines preoperative CT imaging and clinical data into a heterogeneous graph structure, enabling joint modeling of tumor distribution and temporal evolution through spatial topology and cross-modal edges. The framework uses GraphSAGE to aggregate spatiotemporal neighborhood information and leverages supervised and contrastive learning strategies to enhance the model's ability to capture temporal features and improve robustness. A lightweight version of the model reduces parameter count by 78.55%, maintaining near-state-of-the-art performance. The model jointly optimizes recurrence risk regression and survival analysis tasks, with contrastive loss improving feature representational discriminability and cross-modal consistency. Experimental results on the MSKCC CRLM dataset show a time-adjacent accuracy of 85% and a mean absolute error of 1.1005, significantly outperforming existing methods. The innovative heterogeneous graph construction and spatiotemporal decoupling mechanism effectively uncover the associations between dynamic tumor microenvironment changes and prognosis, providing reliable quantitative support for personalized treatment decisions.

Cerebral aneurysm is a silent yet prevalent condition that affects a substantial portion of the global population. Aneurysms can develop due to various factors and present differently, necessitating diverse treatment approaches. Choosing the appropriate treatment upon diagnosis is paramount, as the severity of the disease dictates the course of action. The vulnerability of an aneurysm, particularly in the circle of Willis, is a critical concern; rupture can lead to irreversible consequences, including death. The primary objective of this study is to predict the rupture status of cerebral aneurysms using a comprehensive dataset that includes clinical, morphological, and hemodynamic data extracted from blood flow simulations of patients with actual vessels. Our goal is to provide valuable insights that can aid in treatment decision-making and potentially save the lives of future patients. Diagnosing and predicting the rupture status of aneurysms based solely on brain scans poses a significant challenge, often with limited accuracy, even for experienced physicians. However, harnessing statistical and machine learning (ML) techniques can enhance rupture prediction and treatment strategy selection. We employed a diverse set of supervised and unsupervised algorithms, training them on a database comprising over 700 cerebral aneurysms, which included 55 different parameters: 3 clinical, 35 morphological, and 17 hemodynamic features. Two of our models including stochastic gradient descent (SGD) and multi-layer perceptron (MLP) achieved a maximum area under the curve (AUC) of 0.86, a precision rate of 0.86, and a recall rate of 0.90 for prediction of cerebral aneurysm rupture. Given the sensitivity of the data and the critical nature of the condition, recall is a more vital parameter than accuracy and precision; our study achieved an acceptable recall score. Key features for rupture prediction included ellipticity index, low shear area ratio, and irregularity. Additionally, a one-dimensional CNN model predicted rupture status along a continuous spectrum, achieving 0.78 accuracy on the testing dataset, providing nuanced insights into rupture propensity.

Tricuspid regurgitation jet velocity (TRJV) on echocardiography is used for screening patients with suspected pulmonary hypertension (PH). Artificial intelligence (AI) tools, such as the US2.AI, have been developed for automated evaluation of echocardiograms and can yield measurements that aid PH detection. This study evaluated the performance and utility of the US2.AI in a consecutive cohort of patients with suspected PH. 1031 patients who had been investigated for suspected PH between 2009-2021 were retrospectively identified from the ASPIRE registry. All patients had undergone echocardiography and right heart catheterisation (RHC). Based on RHC results, 771 (75%) patients with a mean pulmonary arterial pressure >20 mmHg were classified as having a diagnosis of PH (as per the 2022 European guidelines). Echocardiograms were evaluated manually and by the US2.AI tool to yield TRJV measurements. The AI tool demonstrated high interpretation yield, successfully measuring TRJV in 87% of echocardiograms. Manually and automatically derived TRJV values showed excellent agreement (intraclass correlation coefficient 0.94, 95% CI 0.94-0.95) with minimal bias (Bland-Altman analysis). Automated TRJV measurements showed equally high diagnostic accuracy for PH as manual measurements (area under the curve 0.88, 95% CI 0.84-0.90 <i>versus</i> 0.88, 95% CI 0.86-0.91). Automated TRJV measurements on echocardiography were similar to manual measurements, with similarly high and noninferior diagnostic accuracy for PH. These findings demonstrate that automated measurement of TRJV on echocardiography is feasible, accurate and reliable and support the implementation of AI-based approaches to echocardiogram evaluation and diagnostic imaging for PH.

Progression free survival (PFS) is a critical clinical outcome endpoint during cancer management and treatment evaluation. Yet, PFS is often missing from publicly available datasets due to the current subjective, expert, and time-intensive nature of generating PFS metrics. Given emerging research in multi-modal machine learning (ML), we explored the benefits and challenges associated with mining different electronic health record (EHR) data modalities and automating extraction of PFS metrics via ML algorithms. We analyzed EHR data from 92 pathology-proven GBM patients, obtaining 233 corticosteroid prescriptions, 2080 radiology reports, and 743 brain MRI scans. Three methods were developed to derive clinical PFS: 1) frequency analysis of corticosteroid prescriptions, 2) natural language processing (NLP) of reports, and 3) computer vision (CV) volumetric analysis of imaging. Outputs from these methods were compared to manually annotated clinical guideline PFS metrics. Employing data-driven methods, standalone progression rates were 63% (prescription), 78% (NLP), and 54% (CV), compared to the 99% progression rate from manually applied clinical guidelines using integrated data sources. The prescription method identified progression an average of 5.2 months later than the clinical standard, while the CV and NLP algorithms identified progression earlier by 2.6 and 6.9 months, respectively. While lesion growth is a clinical guideline progression indicator, only half of patients exhibited increasing contrast-enhancing tumor volumes during scan-based CV analysis. Our results indicate that data-driven algorithms can extract tumor progression outcomes from existing EHR data. However, ML methods are subject to varying availability bias, supporting contextual information, and pre-processing resource burdens that influence the extracted PFS endpoint distributions. Our scan-based CV results also suggest that the automation of clinical criteria may not align with human intuition. Our findings indicate a need for improved data source integration, validation, and revisiting of clinical criteria in parallel to multi-modal ML algorithm development.

It is unknown whether prediction models for lung function decline using computed tomography (CT) imaging-derived features from the upper lobes improve performance compared with globally derived features in individuals at risk of and with COPD. Individuals at risk (current or former smokers) and those with COPD from the Canadian Cohort Obstructive Lung Disease (CanCOLD) retrospective study, were investigated. A total of 103 CT features were extracted globally and regionally, and were used with 12 clinical features (demographics, questionnaires and spirometry) to predict rapid lung function decline for individuals at risk and those with COPD. Machine-learning models were evaluated in a hold-out test set using the area under the receiver operating characteristic curve (AUC) with DeLong's test for comparison. A total of 780 participants were included (n=276 at risk; n=298 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 COPD; n=206 GOLD 2+ COPD). For predicting rapid lung function decline in those at risk, the upper-lobe CT model obtained a significantly higher AUC (AUC=0.80) than the lower-lobe CT model (AUC=0.63) and global model (AUC=0.66; p<0.05). For predicting rapid lung function decline in COPD, there was no significant differences between the upper-lobe (AUC=0.63), lower-lobe (AUC=0.59) or global CT features model (AUC=059; p>0.05). CT features extracted from the upper lobes obtained significantly improved prediction performance compared with globally extracted features for rapid lung function decline in early/mild COPD.

While several artificial intelligence (AI)-assisted medical imaging applications are reported in the recent orthopaedic literature, comparison of the clinical efficacy and utility of these applications is currently lacking. The aim of this systematic review is to evaluate the effectiveness and reliability of AI applications in orthopaedic imaging, focusing on their impact on diagnostic accuracy, image segmentation and operational efficiency across various imaging modalities. Based on the PRISMA guidelines, a comprehensive literature search of PubMed, Cochrane and Scopus databases was performed, using combinations of keywords and MeSH descriptors ('AI', 'ML', 'deep learning', 'orthopaedic surgery' and 'imaging') from inception to March 2024. Included were studies published between September 2018 and February 2024, which evaluated machine learning (ML) model effectiveness in improving orthopaedic imaging. Studies with insufficient data regarding the output variable used to assess the reliability of the ML model, those applying deterministic algorithms, unrelated topics, protocol studies, and other systematic reviews were excluded from the final synthesis. The Joanna Briggs Institute (JBI) Critical Appraisal tool and the Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) tool were applied for the assessment of bias among the included studies. The 53 included studies reported the use of 11.990.643 images from several diagnostic instruments. A total of 39 studies reported details in terms of the Dice Similarity Coefficient (DSC), while both accuracy and sensitivity were documented across 15 studies. Precision was reported by 14, specificity by nine, and the F1 score by four of the included studies. Three studies applied the area under the curve (AUC) method to evaluate ML model performance. Among the studies included in the final synthesis, Convolutional Neural Networks (CNN) emerged as the most frequently applied category of ML models, present in 17 studies (32%). The systematic review highlights the diverse application of AI in orthopaedic imaging, demonstrating the capability of various machine learning models in accurately segmenting and analysing orthopaedic images. The results indicate that AI models achieve high performance metrics across different imaging modalities. However, the current body of literature lacks comprehensive statistical analysis and randomized controlled trials, underscoring the need for further research to validate these findings in clinical settings. Systematic Review; Level of evidence IV.

Artificial intelligence (AI) systems have been implemented to improve the diagnostic yield and operators' skills within endoscopy. Similar AI systems are now emerging in bronchoscopy. Our objective was to identify and describe AI systems in bronchoscopy. A systematic review was performed using MEDLINE, Embase and Scopus databases, focusing on two terms: bronchoscopy and AI. All studies had to evaluate their AI against human ratings. The methodological quality of each study was assessed using the Medical Education Research Study Quality Instrument (MERSQI). 1196 studies were identified, with 20 passing the eligibility criteria. The studies could be divided into three categories: nine studies in airway anatomy and navigation, seven studies in computer-aided detection and classification of nodules in endobronchial ultrasound, and four studies in rapid on-site evaluation. 16 were assessment studies, with 12 showing equal performance and four showing superior performance of AI compared with human ratings. Four studies within airway anatomy implemented their AI, all favouring AI guidance to no AI guidance. The methodological quality of the studies was moderate (mean MERSQI 12.9 points, out of a maximum 18 points). 20 studies developed AI systems, with only four examining the implementation of their AI. The four studies were all within airway navigation and favoured AI to no AI in a simulated setting. Future implementation studies are warranted to test for the clinical effect of AI systems within bronchoscopy.