Coronary artery calcium (CAC) is a great risk predictor of the atherosclerotic cardiovascular disease and CAC scores can be used to stratify the risk of heart disease. Current clinical analysis of CAC is performed using onsite semiautomated software. This semiautomated CAC analysis requires experienced radiologists and radiologic technologists and is both demanding and time-consuming. The purpose of this study is to develop a fully automated CAC detection model that can quantify CAC scores. A total of 1,811 cases of cardiac examinations involving contrast-free multidetector computed tomography were retrospectively collected. We divided the database into the Training Data Set, Validation Data Set, Testing Data Set 1, and Testing Data Set 2. The Training, Validation, and Testing Data Set 1 contained cases with clinically detected CAC; Testing Data Set 2 contained those without detected calcium. The intraclass correlation coefficients between the overall standard and model-predicted scores were 1.00 for both the Training Data Set and Testing Data Set 1. In Testing Data Set 2, the model was able to detect clinically undetected cases of mild calcium. The results suggested that the proposed model’s automated detection of CAC was highly consistent with clinical semiautomated CAC analysis. The proposed model demonstrated potential for clinical applications that can improve the quality of CAC risk stratification.

BACKGROUND AND PURPOSE: Quantitative data from DSA have become important tools for understanding hemodynamic changes of intracranial lesions. In this study, we evaluated 8 hemodynamic parameters in patients before and after carotid artery angioplasty. MATERIALS AND METHODS: DSA images of 34 patients with carotid stenosis who underwent angioplasty and stent placement were retrospectively analyzed. Eleven ROIs (M1, M2, A1, A2, the parietal vein, superior sagittal sinus, internal jugular vein, and 4 in the ICA) were selected on color-coded DSA. Eight hemodynamic parameters (bolus arrival time, TTP, relative TTP, full width at half maximum, wash-in slope, washout slope, maximum enhancement, and area under the curve) were measured from the time-concentration curves of these ROIs. The dependent t test for paired samples was applied to these parameters before and after stent placement. RESULTS: We found that the treatment significantly reduced TTP, relative TTP, bolus arrival time, and washout slope at all arterial ROIs and full width at half maximum and area under the curve at some arterial ROIs. Bolus arrival time was significantly reduced after treatment for all arterial ROIs, the parietal vein, and the superior sagittal sinus. The maximum enhancement and wash-in slope did not show significant changes after treatment. After treatment, the relative TTP from the ICA to M1, M2, and the parietal vein returned to normal values. CONCLUSIONS: In addition to TTP and relative TTP, other parameters can be used to evaluate peritherapeutic cerebral hemodynamic changes. Bolus arrival time has the potential to evaluate brain circulation at arterial and venous sites, especially when TTP cannot be measured because of an incomplete time-concentration curve.

Purpose: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. Materials and methods: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. Results: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms’ performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. Conclusion: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms’ diagnostic effectiveness in lung cancer. Clinical relevance statement: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. Key Points: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

To thoroughly analyze factors affecting the generalization ability of deep learning algorithms on brain tumor detection and segmentation models. We searched PubMed, Embase, Web of Science, Cochrane Library, and IEEE from inception to July 25, 2023, and 19 studies with 12,000 patients were identified. The criteria required studies to use magnetic resonance imaging (MRI) for brain tumor detection and segmentation, offer clear performance metrics, and use external validation data sets. The study focused on outcomes such as sensitivity and Dice score. Study quality was assessed using QUADAS-2 and CLAIM tools. The meta-analysis evaluated varying algorithms and their performance across different validation data sets. MRI hardware as the manufacturer may contribute to data set diversity, impacting AI model generalizability. The study found that the best algorithms had a pooled lesion-wise Dice score of 84%, with pooled sensitivities of 87% (patient-wise) and 86% (lesion-wise). Post-2022 methodologies highlighted evolving artificial intelligence techniques. Performance differences were evident among tumor types, likely due to size disparities. 3D models outperformed their 2D and ensemble counterparts in detection. Although specific preprocessing techniques improved segmentation outcomes, some hindered detection. The study underscores the potential of deep learning in improving brain tumor diagnostics and treatment planning. We also identify the need for further research, including developing a comprehensive diversity index, expanded meta-analyses, and using generative adversarial networks for data diversification, paving the way for AI-driven advancements in oncological patient care. PROPERO (CRD42023459108).