Automated classification of coronary atherosclerotic plaque in optical frequency domain imaging based on deep learning

Development and Validation of Artificial Intelligence-Based Algorithms for Predicting the Segments Debulked by Rotational Atherectomy Using Intravascular Ultrasound Images

We develop and evaluate an artificial intelligence (AI)-based algorithm that uses pre-rotation atherectomy (RA) intravascular ultrasound (IVUS) images to automatically predict regions debulked by RA. A total of 2106 IVUS cross-sections from 60 patients with de novo severely calcified coronary lesions who underwent IVUS-guided RA were consecutively collected. The 2 identical IVUS images of pre- and post-RA were merged, and the orientations of the debulked segments identified in the merged images were marked on the outer circle of each IVUS image. The AI model was developed based on ResNet (deep residual learning for image recognition). The architecture connected 36 fully connected layers, each corresponding to 1 of the 36 orientations segmented every 10°, to a single feature extractor. In each cross-sectional analysis, our AI model achieved an average sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 81%, 72%, 46%, 90%, and 75%, respectively. In conclusion, the AI-based algorithm can use information from pre-RA IVUS images to accurately predict regions debulked by RA and will assist interventional cardiologists in determining the treatment strategies for severely calcified coronary lesions.

Atherosclerosis plaque tissue classification using self-attention-based conditional variational auto-encoder generative adversarial network using OCT plaque image

Adults with coronary artery disease often have atherosclerosis, this is defined as the accumulation of plaque in the tissues of the arterial wall. Cardiologists utilize optical coherence tomography (OCT), a light-based imaging method, to examine the layers of intracoronary tissue along pathological formations, such as plaque accumulation. Intracoronary cross-sectional images produced by state-of-the-art catheter-based imaging scheme have 10-15 µm high resolution. Nevertheless, interpretation of the obtained images depends on the operator, which takes a lot of time and is exceedingly error-prone from one observer to another. OCT image post-processing that automatically and accurately tags coronary plaques can help the technique become more widely used and lower the diagnostic error rate. To overcome these problems, Atherosclerosis plaque tissue classification using Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Network (APC-OCTPI-SACVAGAN) is proposed which classifies the Atherosclerosis plaque images as Fibro calcific plaque, Fibro atheroma, Thrombus, Fibrous plaque and Micro-vessel. The proposed APC-OCTPI-SACVAGAN technique is executed in MATLAB. The efficiency of proposed APC-OCTPI-SACVAGAN method attains 16.19 %, 17.93 %, 19.81 % and 1.57 % higher accuracy; 16.92 %, 11.54 %, 5.29 % and 1.946 % higher Area under curve; and 28.06 %, 25.32 %, 32.19 % and 39.185 % lower computational time comparing to the existing methods respectively.

Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images

SIGNIFICANCE

Optical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). However, unidentified calcified regions within a narrowed artery could impair the outcome of the treatment. Fast and objective identification is paramount to automatically procuring accurate readings on calcifications within the artery.

AIM

We aim to rapidly identify calcification in coronary OCT images using a bounding box and reduce the prediction bias in automated prediction models.

APPROACH

We first adopt a deep learning-based object detection model to rapidly draw the calcified region from coronary OCT images using a bounding box. We measure the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. To calibrate confidence scores of predictions, we implement dependent logistic calibration using each detection result's confidence and center coordinates.

RESULTS

We implemented an object detection module to draw the boundary of the calcified region at a rate of 140 frames per second. With the calibrated confidence score of each prediction, we lower the uncertainty of predictions in calcification detection and eliminate the estimation bias from various object detection methods. The calibrated confidence of prediction results in a confidence error of ∼ 0.13 , suggesting that the confidence calibration on calcification detection could provide a more trustworthy result.

CONCLUSIONS

Given the rapid detection and effective calibration of the proposed work, we expect that it can assist in clinical evaluation of treating the CAD during the imaging-guided procedure.

Frequency and Distribution of Sheet and Nodular Calcification in Coronary Arteries in Japanese Patients

Whether a nodular calcification (NC), which is the precursor to intracoronary thrombosis, is focally or diffusely distributed in the coronary tree has major implications for ongoing efforts to identify. This study aimed to investigate the frequency and spatial distribution patterns of sheet calcification (SC) and NC in a 3-vessel examination of autopsied human hearts.A total of 323 coronary artery specimens from 110 cadavers were obtained from autopsy cases. After fixation and decalcification, the coronary artery trees were cut every 5 mm into 4-μm transverse cross-sections for histological assessment. An SC was defined as a plate-like calcification of > 1 quadrant of the vessel or > 3 mm in diameter, and NC as nodular calcium deposits separated by fibrin, and a deposit size > 1 mm in diameter.Of the 6,306 histological cross-sections, SCs and NCs were identified in 1,627 (26%) and 233 (4%) cross-sections, respectively. SCs and NCs had a similar distribution pattern in all 3 coronary arteries. In the left anterior descending artery (LAD), NCs were predominantly located in the proximal segment: the first 45 mm from the LAD ostium (72%) and the first 60 mm from the LAD ostium (84%), respectively. However, NCs were evenly distributed throughout the length of the coronary artery in the right coronary artery (RCA) and left circumflex artery (LCX).NCs coexisted with SCs, and tended to cluster in predictable parts within the proximal segments of the LAD, but were evenly distributed throughout the RCA and LCX in coronary arteries from cadavers.

Ultrasonic Imaging of Cardiovascular Disease Based on Image Processor Analysis of Hard Plaque Characteristics

Cardiovascular disease detection and analysis using ultrasonic imaging expels errors in manual clinical trials with precise outcomes. It requires a combination of smart computing systems and intelligent image processors. The disease characteristics are analyzed based on the configuration and precise tuning of the processing device. In this article, a characteristic extraction technique (CET) using knowledge learning (KL) is introduced to improve the analysis precision. The proposed method requires optimal selection of disease features and trained similar datasets for improving the characteristic extraction. The disease attributes and accuracy are identified using the standard knowledge update. The image and data features are segmented using the variable processor configuration to prevent false rates. The false rates due to unidentifiable plaque characteristics result in weak knowledge updates. Therefore, the segmentation and data extraction are unanimously performed to prevent feature misleads. The knowledge base is updated using the extracted and identified plaque characteristics for consecutive image analysis. The processor configurations are manageable using the updated knowledge and characteristics to improve precision. The proposed method is verified using precision, characteristic update, training rate, extraction ratio, and time factor.

Optical coherence tomography in coronary atherosclerosis assessment and intervention

Since optical coherence tomography (OCT) was first performed in humans two decades ago, this imaging modality has been widely adopted in research on coronary atherosclerosis and adopted clinically for the optimization of percutaneous coronary intervention. In the past 10 years, substantial advances have been made in the understanding of in vivo vascular biology using OCT. Identification by OCT of culprit plaque pathology could potentially lead to a major shift in the management of patients with acute coronary syndromes. Detection by OCT of healed coronary plaque has been important in our understanding of the mechanisms involved in plaque destabilization and healing with the rapid progression of atherosclerosis. Accurate detection by OCT of sequelae from percutaneous coronary interventions that might be missed by angiography could improve clinical outcomes. In addition, OCT has become an essential diagnostic modality for myocardial infarction with non-obstructive coronary arteries. Insight into neoatherosclerosis from OCT could improve our understanding of the mechanisms of very late stent thrombosis. The appropriate use of OCT depends on accurate interpretation and understanding of the clinical significance of OCT findings. In this Review, we summarize the state of the art in cardiac OCT and facilitate the uniform use of this modality in coronary atherosclerosis. Contributions have been made by clinicians and investigators worldwide with extensive experience in OCT, with the aim that this document will serve as a standard reference for future research and clinical application.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.