Radiologist's manual annotations limit robust deep learning in volumetric medical imaging. While supervised methods excel with large annotated datasets, few-shot learning performs well for large structures but struggles with small ones, such as lesions. This paper describes a novel method that leverages the advantages of both few-shot learning models and fully supervised models while reducing the cost of manual annotation. Our method inputs a small dataset of labeled scans and a large dataset of unlabeled scans and outputs a validated labeled dataset used to train a supervised model (nnU-Net). The estimated correction effort is reduced by having the radiologist correct a subset of the scan labels computed by a few-shot learning model (UniverSeg). The method uses an optimized support set of scan slice patches and prioritizes the resulting labeled scans that require the least correction. This process is repeated for the remaining unannotated scans until satisfactory performance is obtained. We validated our method on liver, lung, and brain lesions on CT and MRI scans (375 scans, 5933 lesions). It significantly reduces the estimated lesion detection correction effort by 34% for missed lesions, 387% for wrongly identified lesions, with 130% fewer lesion contour corrections, and 424% fewer pixels to correct in the lesion contours with respect to manual annotation from scratch. Our method effectively reduces the radiologist's annotation effort of small structures to produce sufficient high-quality annotated datasets to train deep learning models. The method is generic and can be applied to a variety of lesions in various organs imaged by different modalities.

Obesity is associated with functional alterations in the brain. Although spatial organisation changes in the brains of individuals with obesity have been widely studied, the temporal dynamics in their brains remain poorly understood. Therefore, in this study, we investigated variations in the intrinsic neural timescale (INT) across different degrees of obesity using resting-state functional and diffusion magnetic resonance imaging data from the enhanced Nathan Kline Institute Rockland Sample database. We examined the relationship between the INT and obesity phenotypes using supervised machine learning, controlling for age and sex. To further explore the structure-function characteristics of these regions, we assessed the modular network properties by analysing the participation coefficients and within-module degree derived from the structure-function coupling matrices. Finally, the INT values of the identified regions were used to predict eating behaviour traits. A significant negative correlation was observed, particularly in the default mode, limbic and reward networks. We found a negative association with the participation coefficients, suggesting that shorter INT values in higher-order association areas are related to reduced network integration. Moreover, the INT values of these identified regions moderately predicted eating behaviours, underscoring the potential of the INT as a candidate marker for obesity and eating behaviours. These findings provide insight into the temporal organisation of neural activity in obesity, highlighting the role of specific brain networks in shaping behavioural outcomes.

To assess whether the diagnostic performance of a commercial artificial intelligence (AI) algorithm for mammography differs between breast-level and lesion-level interpretations and to compare performance to a large population of specialised human readers. We retrospectively analysed 1200 mammograms from the NHS breast cancer screening programme using a commercial AI algorithm and assessments from 1258 trained human readers from the Personal Performance in Mammographic Screening (PERFORMS) external quality assurance programme. For breasts containing pathologically confirmed malignancies, a breast and lesion-level analysis was performed. The latter considered the locations of marked regions of interest for AI and humans. The highest score per lesion was recorded. For non-malignant breasts, a breast-level analysis recorded the highest score per breast. Area under the curve (AUC), sensitivity and specificity were calculated at the developer's recommended threshold for recall. The study was designed to detect a medium-sized effect (odds ratio 3.5 or 0.29) for sensitivity. The test set contained 882 non-malignant (73%) and 318 malignant breasts (27%), with 328 cancer lesions. The AI AUC was 0.942 at breast level and 0.929 at lesion level (difference -0.013, p < 0.01). The mean human AUC was 0.878 at breast level and 0.851 at lesion level (difference -0.027, p < 0.01). AI outperformed human readers at the breast and lesion level (ps < 0.01, respectively) according to the AUC. AI's diagnostic performance significantly decreased at the lesion level, indicating reduced accuracy in localising malignancies. However, its overall performance exceeded that of human readers. Question AI often recalls mammography cases not recalled by humans; to understand why, we as humans must consider the regions of interest it has marked as cancerous. Findings Evaluations of AI typically occur at the breast level, but performance decreases when AI is evaluated on a lesion level. This also occurs for humans. Clinical relevance To improve human-AI collaboration, AI should be assessed at the lesion level; poor accuracy here may lead to automation bias and unnecessary patient procedures.

The objective of this study is to investigate the impact of deep learning reconstruction and accelerated acquisition on reproducibility and variability of radiomic features in abdominal MRI. Seventeen volunteers were prospectively included to undergo abdominal MRI on a 3-T scanner for axial T2-weighted, axial T2-weighted fat-suppressed, and coronal T2-weighted sequences. Each sequence was scanned for four times using clinical reference acquisition with standard reconstruction, clinical reference acquisition with deep learning reconstruction, accelerated acquisition with standard reconstruction, and accelerated acquisition with deep learning reconstruction, respectively. The regions of interest were drawn for ten anatomical sites with rigid registrations. Ninety-three radiomic features were extracted via PyRadiomics after z-score normalization. The reproducibility was evaluated using clinical reference acquisition with standard reconstruction as reference by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The variability among four scans was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Our study found that the median (first and third quartile) of overall ICC and CCC values were 0.451 (0.305, 0.583) and 0.450 (0.304, 0.582). The overall percentage of radiomic features with ICC > 0.90 and CCC > 0.90 was 8.1% and 8.1%, and was considered acceptable. The median (first and third quartile) of overall CV and QCD values was 9.4% (4.9%, 17.2%) and 4.9% (2.5%, 9.7%). The overall percentage of radiomic features with CV < 10% and QCD < 10% was 51.9% and 75.0%, and was considered acceptable. Without respect to clinical significance, deep learning reconstruction and accelerated acquisition led to a poor reproducibility of radiomic features, but more than a half of the radiomic features varied within an acceptable range.

Deep-learning models for prostate cancer detection typically require large datasets, limiting clinical applicability across institutions due to domain shift issues. This study aimed to develop a few-shot learning deep-learning model for prostate cancer detection on multiparametric MRI that requires minimal training data and to compare its diagnostic performance with experienced radiologists. In this retrospective study, we used 99 cases (80 positive, 19 negative) of biopsy-confirmed prostate cancer (2017-2022), with 20 cases for training, 5 for validation, and 74 for testing. A 2D transformer model was trained on T2-weighted, diffusion-weighted, and apparent diffusion coefficient map images. Model predictions were compared with two radiologists using Matthews correlation coefficient (MCC) and F1 score, with 95% confidence intervals (CIs) calculated via bootstrap method. The model achieved an MCC of 0.297 (95% CI: 0.095-0.474) and F1 score of 0.707 (95% CI: 0.598-0.847). Radiologist 1 had an MCC of 0.276 (95% CI: 0.054-0.484) and F1 score of 0.741; Radiologist 2 had an MCC of 0.504 (95% CI: 0.289-0.703) and F1 score of 0.871, showing that the model performance was comparable to Radiologist 1. External validation on the Prostate158 dataset revealed that ImageNet pretraining substantially improved model performance, increasing study-level ROC-AUC from 0.464 to 0.636 and study-level PR-AUC from 0.637 to 0.773 across all architectures. Our findings demonstrate that few-shot deep-learning models can achieve clinically relevant performance when using pretrained transformer architectures, offering a promising approach to address domain shift challenges across institutions.

The study aims to utilize interpretable machine learning models to predict the risk of myasthenia gravis onset in thymoma patients and investigate the intrinsic correlations and causal relationships between them. A comprehensive retrospective analysis was conducted on 172 thymoma patients diagnosed at two medical centers between 2018 and 2024. The cohort was bifurcated into a training set (n = 134) and test set (n = 38) to develop and validate risk predictive models. Radiomic and deep features were extracted from tumor regions across three CT phases: non-enhanced, arterial, and venous. Through rigorous feature selection employing Spearman's rank correlation coefficient and LASSO (Least Absolute Shrinkage and Selection Operator) regularization, 12 optimal imaging features were identified. These were integrated with 11 clinical parameters and one pathological subtype variable to form a multi-dimensional feature matrix. Six machine learning algorithms were subsequently implemented for model construction and comparative analysis. We utilized SHAP (SHapley Additive exPlanation) to interpret the model and employed doubly robust learner to perform a potential causal analysis between thymoma and myasthenia gravis (MG). All six models demonstrated satisfactory predictive capabilities, with the support vector machine (SVM) model exhibiting superior performance on the test cohort. It achieved an area under the curve (AUC) of 0.904 (95% confidence interval [CI] 0.798-1.000), outperforming other models such as logistic regression, multilayer perceptron (MLP), and others. The model's predictive result substantiates the strong correlation between thymoma and MG. Additionally, our analysis revealed the existence of a significant causal relationship between them, and high-risk tumors significantly elevated the risk of MG by an average treatment effect (ATE) of 9.2%. This implies that thymoma patients with types B2 and B3 face a considerably high risk of developing MG compared to those with types A, AB, and B1. The model provides a novel and effective tool for evaluating the risk of MG development in patients with thymoma. Furthermore, correlation and causal analysis have unveiled pathways that connect tumor to the risk of MG, with a notably higher incidence of MG observed in high risk pathological subtypes. These insights contribute to a deeper understanding of MG and drive a paradigm shift in medical practice from passive treatment to proactive intervention.

Aortic valve calcium scoring is an essential tool for diagnosing, treating, monitoring, and assessing the risk of aortic stenosis. The current gold standard for determining the aortic valve calcium score is computed tomography (CT). However, CT is costly and exposes patients to ionizing radiation, making it less ideal for frequent monitoring. Echocardiography, a safer and more affordable alternative that avoids radiation, is more widely accessible, but its variability between and within experts leads to subjective interpretations. Given these limitations, there is a clear need for an automated, objective method to measure the aortic valve calcium score from echocardiography, which could reduce costs and improve patient safety. In this paper, we first employ the YOLOv5 method to detect the region of interest in the aorta within echocardiography images. Building on this, we propose a novel approach that combines UNet and diffusion model architectures to segment calcified areas within the identified region, forming the foundation for automated aortic valve calcium scoring. This architecture leverages UNet's localization capabilities and the diffusion model's strengths in capturing fine-grained structures, enhancing both segmentation accuracy and consistency. The proposed method achieves 85.08% precision, 80.01% recall, and 71.13% Dice score on a novel dataset comprising 160 echocardiography images from 86 distinct patients. This system enables cardiologists to focus more on critical aspects of diagnosis by providing a faster, more objective, and cost-effective method for aortic valve calcium scoring and eliminating the risk of radiation exposure.

The urgency to accelerate PE management and minimize patient risk has driven the development of artificial intelligence (AI) algorithms designed to provide a swift and accurate diagnosis in dedicated chest imaging (computed tomography pulmonary angiogram; CTPA) for suspected PE; however, the accuracy of AI algorithms in the detection of incidental PE in non-dedicated CT imaging studies remains unclear and untested. This study explores the potential for a commercial AI algorithm to identify incidental PE in non-dedicated contrast-enhanced CT chest imaging studies. The Viz PE algorithm was deployed to identify the presence of PE on 130 dedicated and 63 non-dedicated contrast-enhanced CT chest exams. The predictions for non-dedicated contrast-enhanced chest CT imaging studies were 90.48% accurate, with a sensitivity of 0.14 and specificity of 1.00. Our findings reflect that the Viz PE algorithm demonstrated an overall accuracy of 90.16%, with a specificity of 96% and a sensitivity of 41%. Although the high specificity is promising for ruling in PE, the low sensitivity highlights a limitation, as it indicates the algorithm may miss a substantial number of true-positive incidental PEs. This study demonstrates that commercial AI detection tools hold promise as integral support for detecting PE, particularly when there is a strong clinical indication for their use; however, current limitations in sensitivity, especially for incidental cases, underscore the need for ongoing radiologist oversight.

Systemic sclerosis (SSc) is a complex inflammatory vasculopathy with diverse symptoms and variable disease progression. Despite its known impact on body composition (BC), clinical decision-making has yet to incorporate these biomarkers. This study aims to extract quantitative BC imaging biomarkers from CT scans to assess disease severity, define BC phenotypes, track changes over time and predict survival. CT exams were extracted from a prospectively maintained cohort of 452 SSc patients. 128 patients with at least one CT exam were included. An artificial intelligence-based 3D body composition analysis (BCA) algorithm assessed muscle volume, different adipose tissue compartments, and bone mineral density. These parameters were analysed with regard to various clinical, laboratory, functional parameters and survival. Phenotypes were identified performing K-means cluster analysis. Longitudinal evaluation of BCA changes employed regression analyses. A regression model using BCA parameters outperformed models based on Body Mass Index and clinical parameters in predicting survival (area under the curve (AUC)=0.75). Longitudinal development of the cardiac marker enabled prediction of survival with an AUC=0.82. Patients with altered BCA parameters had increased ORs for various complications, including interstitial lung disease (p<0.05). Two distinct BCA phenotypes were identified, showing significant differences in gastrointestinal disease manifestations (p<0.01). This study highlights several parameters with the potential to reshape clinical pathways for SSc patients. Quantitative BCA biomarkers offer a means to predict survival and individual disease manifestations, in part outperforming established parameters. These insights open new avenues for research into the mechanisms driving body composition changes in SSc and for developing enhanced disease management tools, ultimately leading to more personalised and effective patient care.

Clinical medical images often suffer from compromised quality, which negatively impacts the diagnostic process by both clinicians and AI algorithms. While GAN-based enhancement methods have been commonly developed in recent years, delicate model training is necessary due to issues with artifacts, mode collapse, and instability. Diffusion models have shown promise in generating high-quality images superior to GANs, but challenges in training data collection and domain gaps hinder applying them for medical image enhancement. Additionally, preserving fine structures in enhancing medical images with diffusion models is still an area that requires further exploration. To overcome these challenges, we propose structure-preserved diffusion models for generalizable medical image enhancement (GEDM). GEDM leverages joint supervision from enhancement and segmentation to boost structure preservation and generalizability. Specifically, synthetic data is used to collect high-low quality paired training data with structure masks, and the Laplace transform is employed to reduce domain gaps and introduce multi-scale conditions. GEDM conducts medical image enhancement and segmentation jointly, supervised by high-quality references and structure masks from the training data. Four datasets of two medical imaging modalities were collected to implement the experiments, where GEDM outperformed state-of-the-art methods in image enhancement, as well as follow-up medical analysis tasks.