Computed Tomography Angiography Images Research Articles

Prediction of endovascular leaks after thoracic endovascular aneurysm repair though machine learning applied to pre-procedural computed tomography angiographs.

To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- operative computed tomography angiography (CTA) to machine-learning. We evaluated 1-year follow-up CT scans (arterial and delayed phases) in patients who underwent TEVAR for the presence or absence of an endoleak. We evaluated the effect of machine learning of the patient age, sex, weight, and height, plus 22 vascular features on the ability to predict post-TEVAR endoleaks. The extreme Gradient Boosting (XGBoost) for ML system was trained on 14 patients with- and 131 without endoleaks. We calculated their importance by applying XGBoost to machine learning and compared our findings between with those of conventional vessel measurement-based methods such as the 22 vascular features by using the Pearson correlation coefficients. Pearson correlation coefficient and 95% confidence interval (CI) were r = 0.86 and 0.75 to 0.92 for the machine learning, r = -0.44 and -0.56 to -0.29 for the vascular angle, and r = -0.19 and -0.34 to -0.02 for the diameter between the subclavian artery and the aneurysm (Fig.3a-c, all: p < 0.05). With machine-learning, the univariate analysis was significant higher compared with the vascular angle and in the diameter between the subclavian artery and the aneurysm such as the conventional methods (p < 0.05). To predict the risk for post-TEVAR endoleaks, machine learning was superior to the conventional vessel measurement method when factors such as patient characteristics, and vascular features (vessel length, diameter, and angle) were evaluated on pre-TEVAR thoracic CTA images.

Abstract 2011: Development Of Machine Learning Model To Combine Clinical And Imaging Data For Prediction Of Endoleaks: Proof-of-concept Study

Objective: To develop a foundational ensemble machine learning (ML) classifier to train on computed tomography angiography (CTA) scans and clinical data to predict the occurrence of endoleaks after endovascular aneurysm repair (EVAR) of the abdominal aorta. Methods: An ensemble supervised ML model was developed by using the Convolutional Neural Network (CNN) classifier to transform imaging data into a vector that can be analyzed by a supervised ML model. To test this model, we applied CTA scans from 14 patients with an endoleak after infra-renal EVAR and 14 propensity-matched patients without an endoleak. Sixty DICOM images in batches of 3 were selected from each patient for a total of 20 batches per patient. These batches were assessed by the CNN model to output a singular representative vector for the respective patient (n=28). A supervised ML model was then trained on these cases using Leave-One-Out Cross Validation. To account for variability based on the random state, a for-loop was used to iterate through random states 0 to 49 and the average accuracies and AUC were taken from these 50 iterations. The clinical data was then standardized using a MinMaxScaler and appended to the representative vectors so that both the clinical and imaging data could be used as input for the supervised ML model. The ensemble model was assessed by testing accuracy and the area under the receiver operating characteristic curve (AUROC) and compared to the best ML model alone and imaging alone predictions for validation. Results: Using the clinical data alone, ML accuracy and AUROC performed with and accuracy of 57% and AUROC curve of 0.57. Using the imaging data alone, the final RFC model reported an accuracy of 59% and an AUROC curve of 0.59. Combining the imaging and clinical data, the final RFC model reported an accuracy of 60% and an area under the receiver operating curve of 0.60. Conclusions: Preliminary data from these models have shown proof-of-concept that a deep learning model such as a CNN can be combined with a supervised machine learning model to combine CTA imaging and clinical data to predict complications from an endovascular intervention. These models can serve as a proof of concept for future studies analyzing endovascular cases with larger datasets.

3D/2D Vessel Registration Based on Monte Carlo Tree Search and Manifold Regularization.

The augmented intra-operative real-time imaging in vascular interventional surgery, which is generally performed by projecting preoperative computed tomography angiography images onto intraoperative digital subtraction angiography (DSA) images, can compensate for the deficiencies of DSA-based navigation, such as lack of depth information and excessive use of toxic contrast agents. 3D/2D vessel registration is the critical step in image augmentation. A 3D/2D registration method based on vessel graph matching is proposed in this study. For rigid registration, the matching of vessel graphs can be decomposed into continuous states, thus 3D/2D vascular registration is formulated as a search tree problem. The Monte Carlo tree search method is applied to find the optimal vessel matching associated with the highest rigid registration score. For nonrigid registration, we propose a novel vessel deformation model based on manifold regularization. This model incorporates the smoothness constraint of vessel topology into the objective function. Furthermore, we derive simplified gradient formulas that enable fast registration. The proposed technique undergoes evaluation against seven rigid and three nonrigid methods using a variety of data - simulated, algorithmically generated, and manually annotated - across three vascular anatomies: the hepatic artery, coronary artery, and aorta. Our findings show the proposed method's resistance to pose variations, noise, and deformations, outperforming existing methods in terms of registration accuracy and computational efficiency. The proposed method demonstrates average registration errors of 2.14 mm and 0.34 mm for rigid and nonrigid registration, and an average computation time of 0.51 s.

Left posterior cerebral artery occlusion following minor head trauma: a case report

A 17-year-old male patient who received a punch behind the left ear 2 days ago, was admitted to the emergency room with a headache and dizziness. His neurological examination was normal. Brain CT images demonstrated diffuse hypodense areas and sulcus deletion in the left occipital lobe. On MR images, diffusion restriction for acute ischemia in the left PCA watershed area was determined, and hospitalization was recommended. However, he and his relatives left the hospital. Through the electronic information system called “e-nabız”, it was learned that the patient was hospitalized in an external center and treated with antiplatelet and anticoagulant regimens for ten days due to an acute thrombus at the left PCA P1 distal-P2 level observed on the brain CT and MR angiography images. In our clinic, his neurological examination was completely normal thirty days later. Still, a visual field examination revealed a total quadrianopsy in the upper temporal pole on the right and left eyes. An encephalomalacia in the left PCA watershed area was observed on CT images. The findings of this case showed that minor head trauma in children can cause large vessel occlusion and therefore close follow-up of these children for a certain time would be appropriate.

Anchura del bulbo de la arteria carótida interna: un nuevo parámetro potencial para la predicción de enfermedades vasculares cerebrales

ObjectiveTo investigate the relationship between the width of the internal carotid artery (ICA) bulb and cerebral vascular diseases including stroke and intracranial aneurysms. Material and methodsIn total 300 patients who had supra-aortic computed tomography angiography (CTA) were enrolled in this study from 2015 to 2021. The study groups consisted of 100 ischemic stroke patients, 100 patients with intracranial aneurysms, and 100 control subjects. The intracranial aneurysm patient group was divided into two subgroups according to the presence of subarachnoid hemorrhage (SAH). The largest diameters of the ICA C1 (cervical) and C2 (petrous) segments in all individuals were measured bilaterally on CTA images. The ICA diameter ratios of the cases were measured using the formula (C1-C2)/C1. The relationship between the age and ICA vessel analysis was evaluated as well. ResultsThe mean ICA bulb width values in the ischemic stroke patient group and the intracranial aneurysm patient group were significantly higher than the control group (P<.001). The ICA C1 and C2 segment diameter values and ICA diameter ratio were smaller in the intracranial aneurysm patients with SAH than those who had not (P= 0.7). There was a statistically significant but weak relationship between the age and ICA diameter ratios in all study groups (R-squared value of 0.26, P= 0.03). ConclusionICA bulb width is a parameter that can be easily evaluated with neuroimaging modalities and is a successful method that may be used for predicting the risk of ischemic stroke or the presence of an intracranial aneurysm.

Prognostic value of a novel artificial intelligence-based coronary CTA-derived ischemia algorithm among patients with normal or abnormal myocardial perfusion

BackgroundAmong patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA), downstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemia. A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) aims to predict ischemia directly from coronary CTA images. We aimed to study the prognostic value of AI-QCTischemia among patients with obstructive CAD on coronary CTA and normal or abnormal downstream PET perfusion. MethodsAI-QCTischemia was calculated by blinded analysts among patients from the retrospective coronary CTA cohort at Turku University Hospital, Finland, with obstructive CAD on initial visual reading (diameter stenosis ≥50%) being referred for downstream 15O-H2O-PET adenosine stress perfusion imaging. All coronary arteries with their side branches were assessed by AI-QCTischemia. Absolute stress myocardial blood flow ≤2.3 ​ml/g/min in ≥2 adjacent segments was considered abnormal. The primary endpoint was death, myocardial infarction, or unstable angina pectoris. The median follow-up was 6.2 [IQR 4.4–8.3] years. Results662 of 768 (86%) patients had conclusive AI-QCTischemia result. In patients with normal 15O-H2O-PET perfusion, an abnormal AI-QCTischemia result (n ​= ​147/331) vs. normal AI-QCTischemia result (n ​= ​184/331) was associated with a significantly higher crude and adjusted rates of the primary endpoint (adjusted HR 2.47, 95% CI 1.17–5.21, p ​= ​0.018). This did not pertain to patients with abnormal 15O-H2O-PET perfusion (abnormal AI-QCTischemia result (n ​= ​269/331) vs. normal AI-QCTischemia result (n ​= ​62/331); adjusted HR 1.09, 95% CI 0.58–2.02, p ​= ​0.794) (p-interaction ​= ​0.039). ConclusionAmong patients with obstructive CAD on coronary CTA referred for downstream 15O-H2O-PET perfusion imaging, AI-QCTischemia showed incremental prognostic value among patients with preserved perfusion by 15O-H2O-PET imaging, but not among those with reduced perfusion.

Prediction of small intracranial aneurysm rupture status based on combined Clinical–Radiomics model

BackgroundAccumulating small unruptured intracranial aneurysms are detected due to the improved quality and higher frequency of cranial imaging, but treatment remains controversial. While surgery or endovascular treatment is effective for small aneurysms with a high risk of rupture, such interventions are unnecessary for aneurysms with a low risk of rupture. Consequently, it is imperative to accurately identify small aneurysms with a low risk of rupture. The purpose of this study was to develop a clinically practical model to predict small aneurysm ruptures based on a radiomics signature and clinical risk factors. MethodsA total of 293 patients having an aneurysm with a diameter of less than 5 mm, including 199 patients (67.9 %) with a ruptured aneurysm and 94 patients (32.1 %) without a ruptured aneurysm, were included in this study. Digital subtraction angiography or surgical treatment was required in all cases. Data on the clinical risk factors and the features on computed tomography angiography images associated with the aneurysm rupture status were collected simultaneously. We developed a clinical–radiomics model to predict aneurysm rupture status using multivariate logistic regression analysis. The combined clinical–radiomics model was constructed by nomogram analysis. The diagnostic performance, clinical utility, and model calibration were evaluated by operating characteristic curve analysis, decision curve analysis, and calibration analysis. ResultsA combined clinical–radiomics model (Area Under Curve [AUC], 0.85; 95 % confidence interval [CI], 0.757–0.947) showed effective performance in the operating characteristic curve analysis. In the validation cohort, the performance of the combined model was better than that of the radiomics model (AUC, 0.75; 95 % CI, 0.645–0.865; Delong's test p-value = 0.01) and the clinical model (AUC, 0.74; 95 % CI, 0.625–0.851; Delong's test p-value <0.01) alone. The results of the decision curve, nomogram, and calibration analyses demonstrated the clinical utility and good fitness of the combined model. ConclusionOur study demonstrated the effectiveness of a clinical–radiomics model for predicting rupture status in small aneurysms.

CT Perfusion Derived rCBV < 42% Lesion Volume Is Independently Associated with Followup FLAIR Infarct Volume in Anterior Circulation Large Vessel Occlusion.

Pretreatment CT Perfusion (CTP) parameter rCBV < 42% lesion volume has recently been shown to predict 90-day mRS. In this study, we aim to assess the relationship between rCBV < 42% and a radiographic follow-up infarct volume delineated on FLAIR images. In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 9 January 2017 and 10 January 2023. Follow-up FLAIR imaging was used to determine the final infarct volume. Student t, Mann-Whitney-U, and Chi-Square tests were used to assess differences. Spearman's rank correlation and linear regression analysis were used to assess associations between rCBV < 42% and follow-up infarct volume on FLAIR. In total, 158 patients (median age: 68 years, 52.5% female) met our inclusion criteria. rCBV < 42% (ρ = 0.56, p < 0.001) significantly correlated with follow-up-FLAIR infarct volume. On multivariable linear regression analysis, rCBV < 42% lesion volume (beta = 0.60, p < 0.001), ASPECTS (beta = -0.214, p < 0.01), mTICI (beta = -0.277, p < 0.001), and diabetes (beta = 0.16, p < 0.05) were independently associated with follow-up infarct volume. The rCBV < 42% lesion volume is independently associated with FLAIR follow-up infarct volume.

Predictors of severe intracerebral hemorrhage expansion.

Severe hematoma expansion (sHE) has the strongest impact on intracerebral hemorrhage (ICH) outcome. We investigated the predictors of sHE. Retrospective analysis of ICH patients admitted at nine sites in Italy, Germany, China, and Canada. The following imaging features were analyzed: non-contrast CT (NCCT) hypodensities, heterogeneous density, blend sign, irregular shape, and CT angiography (CTA) spot sign. The outcome of interest was sHE, defined as volume increase >66% and/or >12.5 from baseline to follow-up NCCT. Predictors of sHE were explored with logistic regression. A total of 1472 patients were included (median age 73, 56.6% males) of whom 223 (15.2%) had sHE. Age (odds ratio (OR) per year, 95% confidence interval (CI), 1.02 (1.01-1.04)), Anticoagulant treatment (OR 3.00, 95% CI 2.09-4.31), Glasgow Coma Scale (OR 0.93, 95% CI 0.89-0.98), time from onset/last known well to imaging, (OR per h 0.96, 95% CI 0.93-0.99), and baseline ICH volume, (OR per mL 1.02, 95% CI 1.02-1.03) were independently associated with sHE. Ultra-early hematoma growth (baseline volume/baseline imaging time) was also a predictor of sHE (OR per mL/h 1.01, 95% CI 1.00-1.02). All NCCT and CTA imaging markers were also predictors of sHE. Amongst imaging features NCCT hypodensities had the highest sensitivity (0.79) whereas the CTA spot sign had the highest positive predictive value (0.51). sHE is common in the natural history of ICH and can be predicted with few clinical and imaging variables. These findings might inform clinical practice and future trials targeting active bleeding in ICH.

Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection

PurposeThis study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR). MethodsWe retrospectively evaluated 369 patients treated with TEVAR for acute uTBAD from January 2015 to December 2022. A three-dimensional (3D) deep convolutional neural network (CNN) automated radiomic feature extraction from CTA images. Feature selection, using Analysis of Variance (ANOVA) and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, refined a radiomic score (Rad-Score). This score, alongside clinical parameters, was modelled via Extreme Gradient Boosting (XGBoost) analysis. Model calibration was assessed by calibration curves. ResultsThe integration of the Rad-Score with clinical factors including albumin and C-reactive protein levels moderately enhanced predictive efficiency, exhibiting an area under the curve (AUC) of 1.000 (95%CI, 1.000–1.000) in the training cohort and 0.990 (95%CI, 0.966–1.000) in the internal validation cohort. In an independent validation cohort from another hospital, the combined model yielded an AUC of 0.985 (95%CI, 0.965–1.000), with an accuracy, precision, sensitivity, and specificity of 0.92, 0.92, 0.94, and 0.91, respectively. ConclusionsThe synergistic application of deep learning-based radiomics from CTA and clinical indicators holds promise for anticipating AEs post-initial thoracic endovascular aortic repair in patients with acute uTBAD. The clinical utility of the constructed combined model, offering prognostic foresight during follow-up, has been substantiated.

Internal carotid artery bulb width: a novel potential parameter for the prediction of cerebral vascular diseases

ObjectiveTo investigate the relationship between the width of the internal carotid artery (ICA) bulb and cerebral vascular diseases including stroke and intracranial aneurysms. Material and methodsIn total 300 patients who had supra-aortic computed tomography angiography (CTA) were enrolled in this study from 2015 to 2021. The study groups consisted of 100 ischemic stroke patients, 100 patients with intracranial aneurysms, and 100 control subjects. The intracranial aneurysm patient group was divided into two subgroups according to the presence of subarachnoid hemorrhage (SAH). The largest diameters of the ICA C1 (cervical) and C2 (petrous) segments in all individuals were measured bilaterally on CTA images. The ICA diameter ratios of the cases were measured using the formula C1-C2C1. The relationship between the age and ICA vessel analysis was evaluated as well. ResultsThe mean ICA bulb width values in the ischemic stroke patient group and the intracranial aneurysm patient group were significantly higher than the control group (p < 0.001). The ICA C1 and C2 segment diameter values and ICA diameter ratio were smaller in the intracranial aneurysm patients with SAH than those who had not (p = 0.7). There was a statistically significant but weak relationship between the age and ICA diameter ratios in all study groups (R-squared value of 0.26, p = 0.03). ConclusionICA bulb width is a parameter that can be easily evaluated with neuroimaging modalities and is a successful method that may be used for predicting the risk of ischemic stroke or the presence of an intracranial aneurysm.

Early and mid-term results after endovascular repair of thoracoabdominal aortic aneurysms using the off-the-shelf multibranched t-Branch device: a national multi-center study

Objective: The multibranched off-the-shelf Zenith® t-Branch (Cook Medical, Bloomington, IN) device is commonly chosen for endovascular repair of thoracoabdominal aortic aneurysms. The aim of this study was to report early and mid-term outcomes in all patients treated with the t-Branch in Norway; Design and Methods: A retrospective multicenter study with Norwegian centers performing complex endovascular aortic repair was undertaken. T-Branch patients from 2014 to 2020 were included. All postoperative computed tomography angiography images were reviewed, and demographic, anatomical, perioperative and follow-up data were analyzed; Results: Seventy patients were treated in a single-step (n = 55) or staged (n = 15) procedure. Symptomatic presentation was seen in 20 patients, six of which had a contained rupture. Technical success was 87% (n = 59), with failures caused by unsuccessful bridging of target vessels (n = 4), target vessel bleeding (n = 3), persisting type 1c endoleak (n = 1) and t-Branch malrotation (n = 1). 30-day mortality was 9% (n = 6) and was associated with high BMI (p = .038). The spinal cord ischemia rate was 21% (n = 15) and was associated with type II aneurysms (OR 5.4, 95% CI 1.1–26.7, p = .04), smoking (OR 6.0, 95% CI 1.3–27.6, p = .02) and intraoperative blood loss (OR 1.1, 95% CI 1.0–1.3, p = .01). Survival at one, two and three years was 84 ± 4%, 70 ± 6% and 67 ± 6%, respectively. Freedom from aortic-related reinterventions at one, two and three years was 80 ± 5%, 65 ± 7% and 50 ± 8%, respectively; Conclusion: The study showed low early mortality (9%) and satisfactory mid-term survival. Technical success was achieved in acceptable 87% of procedures. The rate of spinal cord ischemia was high, occurring in 21% of patients.

Improving radiomic modeling for the identification of symptomatic carotid atherosclerotic plaques using deep learning-based 3D super-resolution CT angiography

Rationale and objectivesRadiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. Materials and methodsA total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups. SR CTA images were derived from NR CTA images using deep learning-based three-dimensional SR technology. Handcrafted radiomic features were extracted from both the SR and NR CTA images and three risk models were developed based on manually measured quantitative CTA characteristics and NR and SR radiomic features. Model performances were assessed via receiver operating characteristic, calibration, and decision curve analyses. ResultsThe SR model exhibited the optimal performance (area under the curve [AUC] 0.820, accuracy 0.802, sensitivity 0.854, F1 score 0.847) in the testing cohort, outperforming the other two models. The calibration curve analyses and Hosmer–Lemeshow test demonstrated that the SR model exhibited the best goodness of fit, and decision curve analysis revealed that SR model had the highest clinical value and potential patient benefits. ConclusionsDeep learning-based three-dimensional SR technology could improve the CTA-based radiomic models in identifying symptomatic carotid plaques, potentially providing more accurate and valuable information to guide clinical decision-making to reduce the risk of ischemic stroke.

756 A 3D Deep-learning Based Algorithm for Cerebral Vessel and Aneurysm Segmentation From CTA Images

INTRODUCTION: Computed tomography angiography (CTA) is the most widely used imaging modality for intracranial aneurysm (IA) management, yet it remains inferior to digital subtraction angiography (DSA) for IA detection, particularly of small IAs in the cavernous carotid region. METHODS: Using 50 paired CTA-DSA images, we trained (n = 27), validated (n = 3) and tested (n = 20) a deep-learning model (3D-DeepMedic) for cerebrovasculature segmentation from CTA. A landmark-based co-registration algorithm was used for registration and up-sampling of CTAs to paired DSAs. Segmented vessels from DSAs were used as the ground-truth. Accuracy of the model was evaluated using conventional metrics (dice similarity coefficient-DSC) and vessel-specific metrics, like connectivity-area-length (CAL). On the test cases (20 IAs), 3 experts detected and segmented IAs. For each rater, we recorded the rate of IA detection. For detected IAs, raters segmented IAs and calculated morphology parameters to quantify the differences in IA segmentation by raters to DeepMedic. The rater agreements were assessed using Krippendorf’s alpha. RESULTS: In testing, the DeepMedic model yielded a DSC and CAL of 0.971 ± 0.007 and 0.868 ± 0.008, respectively. The model delineated all IAs with errors &lt;10% for all IA morphometrics. Conversely, average IA detection accuracy from raters was 0.653 (undetected IAs were present to a significantly greater degree on the ICA cavernous region and were significantly smaller). Error rates for IA morphometrics in rater-segmentations were significantly higher than in DeepMedic-segmentations, particularly for neck (p = 0.003), and surface-area (p = 0.003). For IA morphology, agreement between the raters was acceptable for metrics except UI (α=0.36) and NSI (α=0.69). The agreement between DeepMedic and ground truth was consistently higher. CONCLUSIONS: Our CTA segmentation algorithm provides a high-fidelity solution for CTA vessel segmentation, particularly for vessels and IAs in the carotid cavernous region.

Automatic 3D coronary artery segmentation based on local region active contour model.

Segmentation of coronary arteries in computed tomography angiography (CTA) images plays a key role in the diagnosis and treatment of coronary-related diseases. However, manually analyzing the large amount of data is time-consuming, and interpreting this data requires the prior knowledge and expertise of radiologists. Therefore, an automatic method is needed to separate coronary arteries from a given CTA dataset. Firstly, an anisotropic diffusion filter was employed to smooth the noise while preserving the vessel boundaries. The coronary skeleton was then extracted using a two-step process based on the intensity of the coronary. In the first step, the thick vessel skeleton was extracted by clustering, improved vesselness filtering and region growing, while in the second step, the thin vessel skeleton was extracted by the height ridge traversal method guided by the cylindrical model. Next, the vesselness measure, representing vessel a priori information, was incorporated into the local region active contour model based on the vessel geometry. Finally, the initial contour of the active contour model was generated using the coronary artery skeleton for effective segmentation of the three-dimensional (3D) coronary arteries. Experimental results on chest CTA images show that the method is able to segment coronary arteries effectively with an average precision, recall and dice similarity coefficient (DSC) of 86.64%, 91.26% and 79.13%, respectively, and has a good performance in thin vessel extraction. The method does not require manual selection of vessel seeds or setting of initial contours, and allows for the extraction of a successful coronary artery skeleton and eventual effective segmentation of the coronary arteries.

Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography.

It is crucial to distinguish unstable from stable intracranial aneurysms (IAs) as early as possible to derive optimal clinical decision-making for further treatment or follow-up. The aim of this study was to investigate the value of a deep learning model (DLM) in identifying unstable IAs from computed tomography angiography (CTA) images and to compare its discriminatory ability with that of a conventional logistic regression model (LRM). From August 2011 to May 2021, a total of 1,049 patients with 681 unstable IAs and 556 stable IAs were retrospectively analyzed. IAs were randomly divided into training (64%), internal validation (16%), and test sets (20%). Convolutional neural network (CNN) analysis and conventional logistic regression (LR) were used to predict which IAs were unstable. The area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the discriminating ability of the models. One hundred and ninety-seven patients with 229 IAs from Banan Hospital were used for external validation sets. The conventional LRM showed 11 unstable risk factors, including clinical and IA characteristics. The LRM had an AUC of 0.963 [95% confidence interval (CI): 0.941-0.986], a sensitivity, specificity and accuracy on the external validation set of 0.922, 0.906, and 0.913, respectively, in predicting unstable IAs. In predicting unstable IAs, the DLM had an AUC of 0.771 (95% CI: 0.582-0.960), a sensitivity, specificity and accuracy on the external validation set of 0.694, 0.929, and 0.782, respectively. The CNN-based DLM applied to CTA images did not outperform the conventional LRM in predicting unstable IAs. The patient clinical and IA morphological parameters remain critical factors for ensuring IA stability. Further studies are needed to enhance the diagnostic accuracy.

An unknown wide persistent ductus arteriosus debuting with atrial fibrillation in older adult: a case report

BackgroundThe persistently patent ductus arteriosus represents a well-known common congenital heart defect; it is uncommon in adult patients, and in any case, it debuts with atrial fibrillation.Case presentationA 75-year-old woman suffering from persistent ductus arteriosus (PDA) was admitted to the cardiology department because of atrial fibrillation, dyspnea and exercise intolerance. A PDA was detected on echocardiography and globally assessed through ECG-gated CT angiography.ConclusionsPatent ductus arteriosus is an uncommon clinical finding in adulthood, and atrial fibrillation, as a consequence of chronic, progressive left atrial enlargement, may be the initial symptom. We describe the ECG-gated CT angiography imaging features of unknown patent ductus arteriosus in an elderly patient who debuted with atrial fibrillation.

Nonlinear biomechanics of diseased carotid arteries

The aim of this article is to analyse the nonlinear biomechanics of diseased carotid arteries as a potential tool for predicting the onset of cerebral strokes. A two-way coupled three-dimensional (3D) hyperelastic fluid-structure interaction (FSI) analysis of a diseased carotid artery with abnormal luminal projections at the carotid bulb known as carotid web (CaW) is conducted on the geometry of a patient artery, built upon employing CT angiography images. The blood-flow model incorporates non-Newtonian pulsatile turbulent fluid, and the artery wall is considered hyperelastic subject to blood-induced motion. The hemodynamics of artery induced by transient boundary conditions is determined, specifically focusing on shifts in crucial hemodynamic parameters such as wall shear stress (WSS) and alterations in the blood velocity pattern. Structural assessment of the artery wall involves quantifying the von Mises (VM) stress and deformation field. The analysis demonstrates that different CaW models result in different flow patterns for a selection of time steps in a cardiac cycle. The findings reveal that the presence of the web, as the most common disease among younger adults, can significantly influence the hemodynamic parameters and potentially accelerate the formation of thrombus and atherosclerosis. Hemodynamic analysis can potentially predict specific sites prone to plaque formation and rupture within the carotid artery.

The correlation study between the length and angle of ascending aortic and the incidence risk of acute type A aortic dissection.

This study utilized computed tomography angiography (CTA) to assess the risk of acute type A aortic dissection (ATAAD) by analyzing the imaging morphology indicators of the ascending aorta, along with the relevant risk factors associated with aortic dissection. The study utilized a retrospective observational research design. The population consisted of 172 patients who received treatment in the Department of Cardiothoracic Surgery at Qilu Hospital, Shandong University, from January 2018 to December 2022. The patients were divided into two groups: the ATAAD group (n = 97) and the thoracic aortic aneurysm group (TAA, n = 75). Demographic data and ascending aorta CTA measurements were collected from all patients. Single factor and multivariate logistic regression were employed to analyze the statistical differences in clinical data and ascending aorta CTA imaging morphology indicators between the two groups. The variables were included in logistic multivariate analysis for further screening, indicating that the length of the ascending aorta (LAA) before ATAAD (OR = 3.365; 95% CI :1.742-6.500, P<0.001), ascending arch angle (asc-arch angle, OR = 0.902; 95% CI: 0.816-0.996, P = 0.042) and the maximum aortic diameter (MAD) before ATAAD, (OR = 0.614; 95% CI: 0.507-0.743, P<0.001) showed statistically significant differences. This study suggests that increased LAA and MAD, as well as a smaller asc-arch angle may be high-risk factors for the onset of ATAAD.

The ARIA trial protocol: a randomised controlled trial to assess the clinical, technical, and cost-effectiveness of a cloud-based, ARtificially Intelligent image fusion system in comparison to standard treatment to guide endovascular Aortic aneurysm repair

BackgroundEndovascular repair of aortic aneurysmal disease is established due to perceived advantages in patient survival, reduced postoperative complications, and shorter hospital lengths of stay. High spatial and contrast resolution 3D CT angiography images are used to plan the procedures and inform device selection and manufacture, but in standard care, the surgery is performed using image-guidance from 2D X-ray fluoroscopy with injection of nephrotoxic contrast material to visualise the blood vessels. This study aims to assess the benefit to patients, practitioners, and the health service of a novel image fusion medical device (Cydar EV), which allows this high-resolution 3D information to be available to operators at the time of surgery.MethodsThe trial is a multi-centre, open label, two-armed randomised controlled clinical trial of 340 patient, randomised 1:1 to either standard treatment in endovascular aneurysm repair or treatment using Cydar EV, a CE-marked medical device comprising of cloud computing, augmented intelligence, and computer vision. The primary outcome is procedural time, with secondary outcomes of procedural efficiency, technical effectiveness, patient outcomes, and cost-effectiveness. Patients with a clinical diagnosis of AAA or TAAA suitable for endovascular repair and able to provide written informed consent will be invited to participate.DiscussionThis trial is the first randomised controlled trial evaluating advanced image fusion technology in endovascular aortic surgery and is well placed to evaluate the effect of this technology on patient outcomes and cost to the NHS.Trial registrationISRCTN13832085. Dec. 3, 2021