Deep neural networks for ECG-free cardiac phase and end-diastolic frame detection on coronary angiographies

ECG data analysis to determine ST-segment elevation myocardial infarction and infarction territory type: an integrative approach of artificial intelligence and clinical guidelines

Introduction

Acute coronary syndrome (ACS) is one of the leading causes of death from cardiovascular diseases worldwide, with ST-segment elevation myocardial infarction (STEMI) representing a severe form of ACS that exhibits high prevalence and mortality rates. This study proposes a new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm.

Methods

Utilizing an ECG database consisting of 888 myocardial infarction (MI) patients, this study enhanced the generalization ability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately identified STE across all 12 leads, which is a crucial indicator for the clinical ECG diagnosis of STEMI. This detector was employed in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI).

Results

In the process of distinguishing between STEMI and NSTEMI, the average area under the receiver operating characteristic curve (AUROC) was 0.939, and the area under the precision-recall curve (AUPRC) was 0.977, demonstrating significant results. Furthermore, this detector exhibited the ability to accurately differentiate between various infarction territories in the ECG, including anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), lateral myocardial infarction (LMI), and suspected left main disease.

Discussion

These results suggest that integrating clinical domains into AI technology for ECG diagnosis can play a crucial role in the rapid treatment and improved prognosis of STEMI patients. This study provides an innovative approach for the diagnosis of cardiovascular diseases and contributes to enhancing the practical applicability of AI-based diagnostic tools in clinical settings.

A Bayesian network for simultaneous keyframe and landmark detection in ultrasonic cine

Accurate landmark detection in medical imaging is essential for quantifying various anatomical structures and assisting in diagnosis and treatment planning. In ultrasound cine, landmark detection is often associated with identifying keyframes, which represent the occurrence of specific events, such as measuring target dimensions at specific temporal phases. Existing methods predominantly treat landmark and keyframe detection as separate tasks without harnessing their underlying correlations. Additionally, owing to the intrinsic characteristics of ultrasound imaging, both tasks are constrained by inter-observer variability, leading to potentially higher levels of uncertainty. In this paper, we propose a Bayesian network to achieve simultaneous keyframe and landmark detection in ultrasonic cine, especially under highly sparse training data conditions. We follow a coarse-to-fine landmark detection architecture and propose an adaptive Bayesian hypergraph for coordinate refinement on the results of heatmap-based regression. In addition, we propose Order Loss for training bi-directional Gated Recurrent Unit to identify keyframes based on the relative likelihoods within the sequence. Furthermore, to exploit the underlying correlation between the two tasks, we use a shared encoder to extract features for both tasks and enhance the detection accuracy through the interaction of temporal and motion information. Experiments on two in-house datasets (multi-view transesophageal and transthoracic echocardiography) and one public dataset (transthoracic echocardiography) demonstrate that our method outperforms state-of-the-art approaches. The mean absolute errors for dimension measurements of the left atrial appendage, aortic annulus, and left ventricle are 2.40 mm, 0.83 mm, and 1.63 mm, respectively. The source code is available at github.com/warmestwind/ABHG.

Subtraction Improves the Accuracy of Coronary CT Angiography in Patients with Severe Calcifications in Identifying Moderate and Severe Stenosis: A Multicenter Study

RATIONALE AND OBJECTIVES

To investigate the diagnostic accuracy of subtraction coronary computed tomographic angiography (CCTAsub) in identifying ≥ 50% and ≥ 70% coronary stenosis in patients with different degrees of calcification.

MATERIALS AND METHODS

In this study, 180 patients with coronary calcified plaques who underwent both coronary CT angiography and invasive coronary angiography (ICA) were prospectively enrolled at five centers. Patients were divided into three groups according to the Agatston score: group A (low to moderate, < 400), group B (high, 400-999), and group C (very high, ≥ 1000). Diagnostic accuracies estimated by area under the receiver operating characteristic curve (AUC) were compared between conventional CCTA (CCTAcon) and CCTAsub, with ICA as a reference standard.

RESULTS

There were 86 patients in group A, 44 in group B, and 50 in group C. In identifying ≥ 70% coronary stenosis, subtraction improved the diagnostic accuracies on a per-segment basis in group B (AUC: 0.80 vs 0.92, p = 0.001) and group C (AUC: 0.75 vs 0.84, p = 0.001) after subtraction. When identifying ≥ 50% coronary stenosis, the per-segment AUC of CCTAsub in group B and C were significantly higher than that in CCTAcon (group B: 0.81 vs 0.92, p < 0.001; group C: 0.77 vs 0.88, p < 0.001). However, no improvement was observed in group A.

CONCLUSION

Subtraction achieved better diagnostic accuracy in patients with Agatston score ≥ 400, both in identifying ≥ 50% and ≥ 70% coronary stenosis, which was instructive for the application of subtraction in clinical practice.

Diagnostic Accuracy of Artificial Intelligence-Based Angiography-Derived Fractional Flow Reserve Using Pressure Wire-Based Fractional Flow Reserve as a Reference

BACKGROUND

Angiographic fractional flow reserve (angioFFR) is a novel artificial intelligence (AI)-based angiography-derived fractional flow reserve (FFR) application. We investigated the diagnostic accuracy of angioFFR to detect hemodynamically relevant coronary artery disease.

METHODS AND RESULTS

Consecutive patients with 30-90% angiographic stenoses and invasive FFR measurements were included in this prospective, single-center study conducted between November 2018 and February 2020. Diagnostic accuracy was assessed using invasive FFR as the reference standard. In patients undergoing percutaneous coronary intervention, gradients of invasive FFR and angioFFR in the pre-senting segments were compared. We assessed 253 vessels (200 patients). The accuracy of angioFFR was 87.7% (95% confidence interval [CI] 83.1-91.5%), with a sensitivity of 76.8% (95% CI 67.1-84.9%), specificity of 94.3% (95% CI 89.5-97.4%), and area under the curve of 0.90 (95% CI 0.86-0.93%). AngioFFR was well correlated with invasive FFR (r=0.76; 95% CI 0.71-0.81; P<0.001). The agreement was 0.003 (limits of agreement: -0.13, 0.14). The FFR gradients of angioFFR and invasive FFR were comparable (n=51; mean [±SD] 0.22±0.10 vs. 0.22±0.11, respectively; P=0.87).

CONCLUSIONS

AI-based angioFFR showed good diagnostic accuracy for detecting hemodynamically relevant stenosis using invasive FFR as the reference standard. The gradients of invasive FFR and angioFFR in the pre-stenting segments were comparable.

Automatic identification of end-diastolic and end-systolic cardiac frames from invasive coronary angiography videos

BACKGROUND

Automatic identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms (ICA) is important to assess blood flow during a cardiac cycle, reconstruct the 3D arterial anatomy from bi-planar views, and generate the complementary fusion map with myocardial images. The current identification method primarily relies on visual interpretation, making it not only time-consuming but also less reproducible.

OBJECITVE

In this paper, we propose a new method to automatically identify angiographic image frames associated with the ED and ES cardiac phases.

METHOD

A detection algorithm is first used to detect the key points (i.e. landmarks) of coronary arteries, and then an optical flow method is employed to track the trajectories of the selected key points. The ED and ES frames are identified based on all these trajectories. Our method was tested with 62 ICA videos from two separate medical centers.

RESULTS

Comparing consensus interpretations by two human expert readers, excellent agreement was achieved by the proposed algorithm: the agreement rates within a one-frame range were 92.99% and 92.73% for the automatic identification of the ED and ES image frames, respectively.

CONCLUSION

The proposed automated method showed great potential for being an integral part of automated ICA image analysis.

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

PURPOSE OF REVIEW

Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA).

RECENT FINDINGS

Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31-14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.

Robotics, imaging, and artificial intelligence in the catheterisation laboratory

The catheterisation laboratory today combines diagnosis and therapeutics, through various imaging modalities and a prolific list of interventional tools, led by balloons and stents. In this review, we focus primarily on advances in image-based coronary interventions. The X-ray images that are the primary modality for diagnosis and interventions are combined with novel tools for visualisation and display, including multi-imaging co-registration modalities with three- and four-dimensional presentations. Interpretation of the physiologic significance of coronary stenosis based on prior angiographic images is being explored and implemented. Major efforts to reduce X-ray exposure to the staff and the patients, using computer-based algorithms for image processing, and novel methods to limit the radiation spread are being explored. The use of artificial intelligence (AI) and machine learning for better patient care requires attention to universal methods for sharing and combining large data sets and for allowing interpretation and analysis of large cohorts of patients. Barriers to data sharing using integrated and universal protocols should be overcome to allow these methods to become widely applicable. Robotic catheterisation takes the physician away from the ionising radiation spot, enables coronary angioplasty and stenting without compromising safety, and may allow increased precision. Remote coronary procedures over the internet, that have been explored in virtual and animal studies and already applied to patients in a small pilot study, open possibilities for sharing experience across the world without travelling. Application of those technologies to neurovascular, and particularly stroke interventions, may be very timely in view of the need for expert neuro-interventionalists located mostly in central areas.

End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences

Dynamic reconstructions (3D+T) of coronary arteries could give important perfusion details to clinicians. Temporal matching of the different views, which may not be acquired simultaneously, is a prerequisite for an accurate stereo-matching of the coronary segments. In this paper, we show how a neural network can be trained from angiographic sequences to synchronize different views during the cardiac cycle using raw x-ray angiography videos exclusively. First, we train a neural network model with angiographic sequences to extract features describing the progression of the cardiac cycle. Then, we compute the distance between the feature vectors of every frame from the first view with those from the second view to generate distance maps that display stripe patterns. Using pathfinding, we extract the best temporally coherent associations between each frame of both videos. Finally, we compare the synchronized frames of an evaluation set with the ECG signals to show an alignment with 96.04% accuracy.