Aortic aneurysm disease ranks consistently in the top 20 causes of death in the U.S. population. Thoracic aortic aneurysm is manifested as an abnormal bulging of thoracic aortic wall and it is a leading cause of death in adults. From the perspective of biomechanics, rupture occurs when the stress acting on the aortic wall exceeds the wall strength. Wall stress distribution can be obtained by computational biomechanical analyses, especially structural Finite Element Analysis. For risk assessment, probabilistic rupture risk of TAA can be calculated by comparing stress with material strength using a material failure model. Although these engineering tools are currently available for TAA rupture risk assessment on patient specific level, clinical adoption has been limited due to two major barriers: labor intensive 3D reconstruction current patient specific anatomical modeling still relies on manual segmentation, making it time consuming and difficult to scale to a large patient population, and computational burden traditional FEA simulations are resource intensive and incompatible with time sensitive clinical workflows. The second barrier was successfully overcome by our team through the development of the PyTorch FEA library and the FEA DNN integration framework. By incorporating the FEA functionalities within PyTorch FEA and applying the principle of static determinacy, we reduced the FEA based stress computation time to approximately three minutes per case. Moreover, by integrating DNN and FEA through the PyTorch FEA library, our approach further decreases the computation time to only a few seconds per case. This work focuses on overcoming the first barrier through the development of an end to end deep neural network capable of generating patient specific finite element meshes of the aorta directly from 3D CT images.

Deep learning models which perform well on images from their training distribution can degrade substantially when applied to new distributions. If a CT scanner introduces a new artifact not present in the training labels, the model may misclassify the images. Although modern CT scanners include design features which mitigate these artifacts, unanticipated or difficult-to-mitigate artifacts can still appear in practice. The direct solution of labeling images from this new distribution can be costly. As a more accessible alternative, this study evaluates domain adaptation as an approach for training models that maintain classification performance despite new artifacts, even without corresponding labels. We simulate ring artifacts from detector gain error in sinogram space and evaluate domain adversarial neural networks (DANN) against baseline and augmentation-based approaches on the OrganAMNIST abdominal CT dataset. Our results demonstrate that baseline models trained only on clean images fail to generalize to images with ring artifacts, and traditional augmentation with other distortion types provides no improvement on unseen artifact domains. In contrast, the DANN approach successfully maintains high classification accuracy on ring artifact images using only unlabeled artifact data during training, demonstrating the viability of domain adaptation for artifact robustness. The domain-adapted model achieved classification performance on ring artifact test data comparable to models explicitly trained with labeled artifact images, while also showing unexpected generalization to uniform noise. These findings provide empirical evidence that domain adaptation can effectively address distribution shift in medical imaging without requiring expensive expert labeling of new artifact distributions, suggesting promise for deployment in clinical settings where novel artifacts may emerge.

In benign tumors with potential for malignant transformation, sampling error during pre-operative biopsy can significantly change patient counseling and surgical planning. Sinonasal inverted papilloma (IP) is the most common benign soft tissue tumor of the sinuses, yet it can undergo malignant transformation to squamous cell carcinoma (IP-SCC), for which the planned surgery could be drastically different. Artificial intelligence (AI) could potentially help with this diagnostic challenge. CT images from 19 institutions were used to train the Google Cloud Vertex AI platform to distinguish between IP and IP-SCC. The model was evaluated on a holdout test dataset of images from patients whose data were not used for training or validation. Performance metrics of area under the curve (AUC), sensitivity, specificity, accuracy, and F1 were used to assess the model. Here we show CT image data from 958 patients and 41099 individual images that were labeled to train and validate the deep learning image classification model. The model demonstrated a 95.8 % sensitivity in correctly identifying IP-SCC cases from IP, while specificity was robust at 99.7 %. Overall, the model achieved an accuracy of 99.1%. A deep automated machine learning model, created from a publicly available artificial intelligence tool, using pre-operative CT imaging alone, identified malignant transformation of inverted papilloma with excellent accuracy.

Vascular inflammation plays a critical role in the development of coronary artery disease (CAD). Measurement of coronary inflammation from coronary computed tomography angiography (CCTA) using the perivascular fat attenuation index (FAI) Score could provide unique prognostic information and guide the clinical management of patients. In this context, we also refer to an artificial intelligence-based risk prediction tool (AI-Risk algorithm), which integrates FAI Score with clinical risk factors and plaque burden to estimate the long-term probability of a fatal cardiac event. A 69-year-old male presented with symptoms of new onset angina. Past medical history included coronary artery bypass grafting (CABG) in 2001. Initial evaluation with CCTA showed patent arterial graft to left anterior descending (LAD) artery and two occluded venous grafts to obtuse marginal and diagonal branches, respectively, were identified. The non-grafted right coronary artery (RCA) was non-obstructive with moderate mid-vessel stenosis and the patient was discharged on optimal medical therapy. However, the patient was intolerant to statin. Eight years later, the patient was admitted to the hospital with a non-ST segment elevation myocardial infarction (NSTEMI) and the invasive coronary angiography showed occlusion of the non-grafted RCA. After few months of guidelines directed medical therapy, the patient developed progressive heart failure due to ischaemic cardiomyopathy and mitral regurgitation that led to his death. Retrospective perivascular FAI measurement of the non-grafted RCA captured the significantly elevated residual inflammatory risk. The utilization of perivascular FAI Score and AI-Risk algorithm to capture inflammatory risk and predict future events beyond the current clinical risk stratification and CCTA interpretation, especially in the absence of obstructive CAD, could offer an important adjunct to current strategies in preventive cardiology, pending further validation. In this case report, our patient's management plan could have been adjusted had these technologies been available during initial evaluation, and the high inflammatory burden of the non-grafted RCA was timely captured.

Deep learning models are prone to failure when inferring upon out-of-distribution (OOD) data, i.e., data whose features fundamentally differ from those in the training set. Existing OOD measures often lack sensitivity to the subtle image variations encountered within clinical settings. In this work, we investigate a post hoc, information-based approach to OOD detection-termed InfoOOD-which iteratively quantifies the amount of embedded feature information that can be shared between the training data and test data without degrading the model output.&#xD;Approach. Abdominal CT images from patients with metastatic liver lesions were used. A 3D U-Net was trained to segment liver organs and lesions using N=157 images. Physics-based artifacts-low dose, sparse view angles, and rings artifacts-were simulated on a separate set of N=40 test images at three intensity magnitudes. Segmentation performance and the ability of the InfoOOD measure to detect the artifact-induced OOD data were evaluated. An additional N=131 test images were used to assess the correlation between the InfoOOD measure and segmentation model performance metrics. In all evaluations, InfoOOD was compared with established embedded feature-based and reconstruction-based OOD detection methods. &#xD;Results. Artifact simulation significantly degraded segmentation model performance across all artifact types and magnitudes (ρ<0.001), with model performance worsening as artifact magnitude increased. The InfoOOD measure consistently outperformed the embedded feature-based measures in detecting OOD data (e.g., AUC=0.93 vs. AUC=0.57 for the strong rings artifact) and surpassed the reconstruction-based measure across weak magnitude artifacts (e.g., AUC=0.75 vs. AUC=0.61 for the weak sparse view artifact). The InfoOOD measure also achieved stronger, negative correlations with segmentation performance metrics (e.g., ρ=-0.52 vs. ρ≥-0.11 for the lesion sensitivity metric). In both assessments, InfoOOD measure performance increased considerably with information bottleneck optimization iterations. &#xD;Significance. This work introduces and validates a novel, highly sensitive, and clinically relevant information-theoretic approach for medical image OOD detection, supporting the safe deployment of deep learning models in clinical settings.

This study aimed to evaluate the diagnostic performance of machine learning (ML) algorithms based on radiomic features extracted from cone-beam computed tomography (CBCT) images in differentiating the nasopalatine duct (NPD) from the nasopalatine duct cyst (NPDC), and to compare their performance with that of a dentomaxillofacial radiologist. CBCT scans from 101 histopathologically confirmed NPDC cases and 101 age- and sex-matched controls with normal NPD were retrospectively analyzed. Manual segmentation was performed to extract 1037 radiomic features (original, Laplacian of Gaussian, and wavelet-transformed). After dimensionality reduction, five ML models (support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), and logistic regression (LR)) were trained using 5-fold cross-validation. Performance was evaluated using the area under the ROC curve (AUC), sensitivity, specificity, precision, recall, and F1-score. Among the 11 optimal features identified through feature selection, large area high gray level emphasis and zone variance from the gray level size zone matrix (GLSZM) class were the most prominent. SVM achieved the highest performance in the test set (AUC and all other metrics = 1.00). The radiologist showed comparable but slightly lower overall performance than SVM (AUC = 0.94, with other metrics between 0.93 and 0.95). Machine learning algorithms based on radiomic features extracted from CBCT images can effectively differentiate NPD from NPDC. Unlike standard visual interpretation, this approach analyzes quantitative image features via mathematical models, yielding objective and reproducible results. It may serve as a non-invasive, complementary decision-support tool, particularly in diagnostically challenging cases.

This study evaluated the accuracy of large language models (LLMs) in assigning Coronary Artery Disease Reporting and Data System (CAD-RADS) 2.0 categories and modifiers based on real-world coronary CT angiography (CCTA) reports and compared their accuracy with human readers. From 2752 eligible CCTA reports generated at an academic hospital between January and September 2024, 180 were randomly selected to fit a balanced distribution of categories and modifiers. The reference standard was established by consensus between two expert cardiac radiologists with 15 and 14 years of experience, respectively. Four LLMs (O1, GPT-4o, GPT-4, GPT-3.5-turbo) and four human readers (a cardiac radiologist, a fellow, two residents) independently assigned CAD-RADS categories and modifiers for each report. For LLMs, the input prompt consisted of the report and a summary of CAD-RADS 2.0. The accuracy of evaluators in full CAD-RADS categorization was compared with O1 using McNemar tests. O1 demonstrated the highest accuracy (90.7%) in full CAD-RADS categorization, outperforming GPT-4o (73.8%), GPT-4 (59.7%), GPT-3.5-turbo (25.8%), the fellow (83.3%), and resident 1 (83.3%; all P-values ≤ 0.01). However, there was no significant difference in accuracy when compared to the cardiac radiologist (86.1%; P = 0.12) and resident 2 (89.4%; P = 0.68). Processing time per report ranged 1.34-16.61 s for LLMs, whereas human readers required 32.10-55.06 s. In the external validation dataset (n = 327) derived from two independent institutions, O1 achieved 95.7% accuracy for full CAD-RADS categorization. In conclusion, compared to human readers, O1 exhibited similar or higher accuracy and shorter processing times to produce a full CAD-RADS 2.0 categorization based on CCTA reports.

To address the limitations of traditional CT-guided pulmonary nodule interventions, such as excessive radiation exposure, prolonged procedure times, and limited precision, we developed an electromagnetic navigation surgical robotic system (ENSRS) to enhance accuracy, efficiency, and safety in percutaneous procedures. The ENSRS integrates artificial intelligence to automate the segmentation of pulmonary nodules and surrounding anatomical structures, generating a detailed surgical environment. A customized path-planning algorithm facilitates minimally invasive access, whereas submillimeter localization using fiducial markers ensures precise coordinate registration. Adaptive multicalibration strategies and robust safety protocols enhance procedural reliability. System performance was evaluated through phantom and animal experiments, with comparisons to traditional CTguided techniques. The ENSRS achieved a groove localization error of 0.51 ± 0.27 mm across 63 patches and a classification accuracy of 100%. In phantom studies, it demonstrated significantly reduced puncture error (0.81 ± 0.98 mm vs. 3.50 ± 2.88 mm, p < 0.0001), required fewer CT scans (1.02 ± 0.25 vs. 1.53 ± 0.92) and shortened puncture times (39.01 ± 29.71 s). In animal experiments, ENSRS achieved improved accuracy (0.33 ± 0.74 mm vs. 1.86 ± 0.99 mm, p = 0.015). The safety outcomes were comparable between the groups, with one pneumothorax reported each. ENSRS improves the precision, efficiency, and safety of pulmonary nodule interventions, outperforming traditional CT-guided methods in phantom and animal models. This system offers a promising approach to pulmonary interventions by combining robotic precision with intelligent planning and tracking, potentially enhancing outcomes in minimally invasive procedures.

After total hip arthroplasty (THA), dual-energy x-ray absorptiometry (DXA) is used as necessary to assess the bone mineral density (BMD) in the Gruen zones around the femoral stem implants. Although periprosthetic BMD may serve as a potential indicator for evaluating stress adaptive remodelling and stem fixation, several factors can introduce measurement errors. Therefore, an automated method was applied using quantitative CT, verified for the total hip with correlation coefficient > 0.9, for BMD assessment in the Gruen zones. This was a retrospective analysis of 71 hips from 58 participants (9 male and 49 female) who underwent THA using the same taper-wedge type stem. Preoperative and postoperative CT scans were acquired alongside DXA measurements of the Gruen zones. A deep-learning method was used to measure BMD in the Gruen zones from preoperative CT images by embedding the stem position information acquired from postoperative CT images through iterative closest point registration. CT images were rotated to the neutral position and were projected anteroposteriorly to generate a digitally reconstructed radiograph to measure the BMD at each zone (CT-aBMD). Correlations between CT-aBMD and DXA measurements were assessed for each zone. The correlations between CT-aBMD and DXA measurements for zones 1 to 7 were 0.924, 0.783, 0.817, 0.921, 0.731, 0.847, and 0.677, respectively (p < 0.001 for all). Our results based on CT analysis suggest that DXA is generally reliable for assessing BMD in the Gruen zones. However, caution may be advised for zones 5 and 7 because of limited correlations. As zone 7 plays a crucial role in stem fixation, during longitudinal evaluation of post-THA stress adaptive remodelling, we recommend ensuring cautious interpretation and consistent BMD measurements using the image attached to the DXA report. It is imperative to calculate the least significant change for accurate BMD evaluation.

To evaluate the role of chest CT radiomics in classifying mediastinal lymphadenopathy caused by hematologic malignancies and abdominopelvic solid cancers. A total of 231 patients with mediastinal lymphadenopathy were selected from the Mediastinal-Lymph-Node-SEG collection in The Cancer Imaging Archive, including 145 patients with hematologic malignancies (74 with chronic lymphocytic leukemia and 71 with lymphoma) and 86 with abdominopelvic solid cancers. Patients were randomly stratified into train and test sets in a 7:3 ratio. Radiomics features were extracted from enhanced CT images of mediastinal lymph nodes, followed by feature selection using univariate analysis and least absolute shrinkage and selection operator regression. A support vector machine algorithm was used to develop classification models, with performance evaluated using the area under the receiver operating characteristic curve (AUC-ROC), accuracy, and 95% CI. For differentiating mediastinal lymphadenopathy between hematologic malignancies and abdominopelvic solid cancers, the model incorporated 23 features and achieved an AUC-ROC of 0.931 (95% CI: 0.891-0.971) and an accuracy of 0.866 in the train set, and an AUC-ROC of 0.830 (95% CI: 0.730-0.929) and an accuracy of 0.759 in the test set. For distinguishing chronic lymphocytic leukemia from lymphoma, the model utilized 4 features, achieving an AUC-ROC of 0.880 (95% CI: 0.813-0.947) and an accuracy of 0.752 in the train set, and an AUC-ROC of 0.872 (95% CI: 0.763-0.982) and an accuracy of 0.836 in the test set. Chest CT radiomics shows promise for classifying mediastinal lymphadenopathy in patients with hematologic malignancies and abdominopelvic solid cancers.