Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model

Non-invasive derivation of instantaneous free-wave ratio from invasive coronary angiography using a new deep learning artificial intelligence model and comparison with human operators' performance

Invasive coronary physiology is underused and carries risks/costs. Artificial Intelligence (AI) might enable non-invasive physiology from invasive coronary angiography (CAG), possibly outperforming humans, but has seldom been explored, especially for instantaneous wave-free Ratio (iFR). We aimed to develop binary iFR lesion classification AI models and compare them with human performance. single-center retrospective study of patients undergoing CAG and iFR. A validated encoder-decoder convolutional neural network (CNN) performed segmentation. Manual annotation of target vessel and pressure sensor location on a segmented telediastolic frame followed. Three AI models classified lesions as positive (≤ 0.89) or negative (> 0.89). Model 1 uses preprocessed vessel diameters with a transformer. Models 2/3 are EfficientNet-B5 CNNs using concatenated angiography and segmentation - Model 3 employs class-frequency-weighted Cross-Entropy Loss. Previous findings demonstrated Model 3's superiority for left anterior descending (LAD) and Model 1's for circumflex (Cx)/right coronary artery (RCA) - they were therefore unified into a vessel-based model. Ten-fold patient-level cross-validation enabled full sample training/testing. Three experienced operators performed binary iFR classification using single frames of raw/segmented images. Comparison metrics were accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Across 250 measurements, AI accuracy was 72%, PPV 48%, NPV 90%, sensitivity 77%, and specificity 71%. Human accuracy ranged from 54 to 74%. NPV was high for the Cx/RCA (AI: 96/98%; operators: 94/97%), but AI significantly outperformed humans in the LAD (78% vs. 60-64%). An AI model capable of binary iFR lesions classification mildly outperformed interventional cardiologists, supporting further validation studies.

Enhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation

Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address manual QCA's limitations in reproducibility and correction process. The present study aimed to assess the performance of an updated AI-QCA solution (MPXA-2000) in lesion detection and quantification using manual QCA as the reference standard, and to demonstrate its superiority over visual estimation. This multi-center retrospective study analyzed 1,076 coronary angiography images obtained from 420 patients, comparing AI-QCA and visual estimation against manual QCA as the reference standard. A lesion was classified as 'detected' when the minimum lumen diameter (MLD) identified by manual QCA fell within the boundaries of the lesion delineated by AI-QCA or visual estimation. The detected lesions were evaluated in terms of diameter stenosis (DS), MLD, and lesion length (LL). AI-QCA accurately detected lesions with a sensitivity of 93% (1705/1828) and showed strong correlations with manual QCA for DS, MLD, and LL (R² = 0.65, 0.83 and 0.71, respectively). In views targeting the major vessels, the proportion of undetected lesions by AI-QCA was less than 4% (56/1492). For lesions in the side branches, AI-QCA also demonstrated high sensitivity (> 92%) in detecting them. Compared to visual estimation, AI-QCA showed significantly better lesion detection capability (93% vs. 69%, p < 0.001), and had a higher probability of detecting all lesions in images with multiple lesions (86% vs. 33%, p < 0.001). The updated AI-QCA demonstrated robust performance in lesion detection and quantification without operator intervention, enabling reproducible vessel analysis. The automated process of AI-QCA has the potential to optimize angiography-guided interventions by providing quantitative metrics.

Digital health in cardiovascular medicine: An overview of key applications and clinical impact by the Portuguese Society of Cardiology Study Group on Digital Health

Digital health interventions including telehealth, mobile health, artificial intelligence, big data, robotics, extended reality, computational and high-fidelity bench simulations are an integral part of the path toward precision medicine. Current applications encompass risk factor modification, chronic disease management, clinical decision support, diagnostics interpretation, preprocedural planning, evidence generation, education, and training. Despite the acknowledged potential, their development and implementation have faced several challenges and constraints, meaning few digital health tools have reached daily clinical practice. As a result, the Portuguese Society of Cardiology Study Group on Digital Health set out to outline the main digital health applications, address some of the roadblocks hampering large-scale deployment, and discuss future directions in support of cardiovascular health at large.

Preliminary Results in the Investigation of In Vivo Iliac and Coronary Flow Collision, Vortex Formation, and Disorganized Flow Degeneration: Insights from Invasive Cardiology Based on Fluid Mechanics Principles and Practices

Background: In the research of coronary artery disease, the precise initial injury that starts the atherosclerotic cascade remains unidentified. Moreover, the mechanisms governing the progression or regression of coronary plaque are not yet fully understood. Based on the concept that the cardiovascular system is a network of pumps and pipes, could fluid mechanics principles and practices elucidate the question of atherosclerosis using flow dynamics images from a novel angiographic technique, focusing on antegrade and retrograde flows and their collisions in iliac and coronary arteries? Methods: From January 2023 to May 2024, coronary angiograms of all hemodynamically stable patients with stable or unstable angina were screened. The angiograms displaying either no lesions (normal) or mild-to-moderate lesions were selected. Each patient underwent an evaluation of flow dynamics and arterial phenomena in both iliac and right coronary arteries. For each artery, data were categorized based on the following parameters: laminar versus non-laminar flow, presence versus absence of collisions, and presence versus absence of retrograde flow. Additionally, in two sub-studies, we analyzed the relationship between retrograde flow and blood pressure, and artificial intelligence algorithms were used to detect the retrograde flow in the right coronary artery. Results: A total of 95 patients were screened, and 51 were included in this study. The results comprised quantitative data (prevalence of laminar flows, collisions, and retrograde flows) and qualitative data (morphological characteristics of antegrade laminar flow, retrograde contrast flow, and instances of flow collision). The results showed that in the iliac artery, laminar flow was observed in 47.06% (24/51) of cases, with collisions noted in 23.53% (12/51). Retrograde flow was present in 47.06% (24/51) of cases, and notably, 75% (18/24) of these cases were associated with uncontrolled diastolic blood pressure (DBP) above 80 mmHg (p < 0.001). Conversely, in the RCA, laminar flow was observed in 54.9% (28/51) of cases, with collisions noted in only 3.92% (2/51). Retrograde flow was identified in 7.84% (4/51) of cases, and all these cases (100%, 4/4) were associated with uncontrolled systolic blood pressure (SBP) above 120 mmHg, though statistical significance was not reached due to the small sample size (p > 0.05). Conclusions: Based on the concept that the cardiovascular system is a network of pumps and pipes, this research methodology provides intriguing insights into arterial flow behaviors by integrating fluid mechanics practices with novel angiographic observations. The preliminary results of this study identified laminar flow as the predominant pattern, with retrograde flow and collisions occurring infrequently. The implications of vortex, collision, and disorganized flow highlight potential mechanisms for endothelial damage and atherosclerosis initiation. Moreover, the correlation with blood pressure underscores the critical role of hypertension management in preventing adverse hemodynamic events. Future directions include refining imaging techniques and further exploring the mechanistic links between flow dynamics and vascular pathophysiology to enhance diagnostic and therapeutic strategies for cardiovascular diseases.

How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives

The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images

Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.

Validation of artificial intelligence-based quantitative coronary angiography

Background

Coronary angiography is fundamental for the diagnosis and treatment of coronary artery disease. Manual quantitative coronary angiography (QCA) is accurate and reproducible; however, it is time-consuming and labor-intensive. However, recent advancements in artificial intelligence (AI) have enabled automated and rapid analysis of medical images, addressing the need for real-time quantitative coronary analysis.

Aims

This study aimed to evaluate the accuracy of AI-based QCA (AI-QCA) compared with that via manual QCA and clinician acceptance.

Methods

This retrospective, single-center study was conducted in two phases. Phase 1 was a pilot study comparing AI-QCA with manual QCA and visual estimation. It involved 15 patients who underwent coronary angiography at Seoul National University Bundang Hospital between September 2011 and July 2021. Phase 2 included a larger cohort of 762 patients, with 1002 coronary angiograms analyzed between May 2020 and April 2021.

Results

In phase 1, AI-QCA and manual QCA consistency varied among the observers, with AI-QCA showing superior consistency compared with visual estimation. However, a strong correlation between AI-QCA and manual-QCA was found in phase 2. AI-QCA accurately identified and quantitatively analyzed multiple lesions in the major vessels, providing results comparable with those of manual QCA.

Conclusions

AI-QCA demonstrated high concordance with manual QCA, offering real-time analysis and reduced workload. Therefore, AI-QCA has the potential to be a valuable tool for diagnosing and treating coronary artery disease, necessitating further studies for clinical validation.

One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning

It is rare to use the one-stage model without segmentation for the automatic detection of coronary lesions. This study sequentially enrolled 200 patients with significant stenoses and occlusions of the right coronary and categorized their angiography images into two angle views: The CRA (cranial) view of 98 patients with 2453 images and the LAO (left anterior oblique) view of 176 patients with 3338 images. Randomization was performed at the patient level to the training set and test set using a 7:3 ratio. YOLOv5 was adopted as the key model for direct detection. Four types of lesions were studied: Local Stenosis (LS), Diffuse Stenosis (DS), Bifurcation Stenosis (BS), and Chronic Total Occlusion (CTO). At the image level, the precision, recall, mAP@0.1, and mAP@0.5 predicted by the model were 0.64, 0.68, 0.66, and 0.49 in the CRA view and 0.68, 0.73, 0.70, and 0.56 in the LAO view, respectively. At the patient level, the precision, recall, and F1scores predicted by the model were 0.52, 0.91, and 0.65 in the CRA view and 0.50, 0.94, and 0.64 in the LAO view, respectively. YOLOv5 performed the best for lesions of CTO and LS at both the image level and the patient level. In conclusion, the one-stage model without segmentation as YOLOv5 is feasible to be used in automatic coronary lesion detection, with the most suitable types of lesions as LS and CTO.