AI, OCT Operate in Tandem to Detect Plaque Erosion in the Heart

Researchers from the University of Electronic Science and Technology of China have developed an AI method that can automatically detect plaque erosion in the heart’s arteries using optical coherence tomography (OCT).

“If cholesterol plaque lining arteries starts to erode, it can lead to a sudden reduction in blood flow to the heart, known as acute coronary syndrome, which requires urgent treatment,” said research team leader Zhao Wang. “Our new method could help improve the clinical diagnosis of plaque erosion and be used to develop new treatments for patients with heart disease.”

Researchers developed an AI method to automatically detect plaque erosion in arteries using OCT images. This type of erosion can block blood flow to the heart, leading to a heart attack or other serious conditions. Courtesy of Zhao Wang/University of Electronic Science and Technology of China.

When OCT is integrated with a miniaturized catheter, it can be used within blood vessels to provide 3D images of the coronary arteries that supply blood to the heart. Although clinicians are increasingly using intravascular OCT to look for plaque erosion, the large amount of data produced and the complexity of visually interpreting the images has led to significant interobserver variability.

The AI-based method automatically detects the presence of plaque erosion using the originally obtained OCT images without additional input. “The ability to detect plaque erosion objectively and automatically will reduce the laborious manual assessment associated with diagnosis,” Wang said.

In the method, a trained neural network uses the original image and two pieces of shape information to predict regions of possible plaque erosion. The initial prediction is refined with a post-processing algorithm based on clinically interpretable features that mimic the knowledge physicians use to make a diagnosis.

“We had to develop a new AI model that incorporates explicit shape information, the key feature used to identify plaque erosion in OCT images,” Wang said. “The underlying intravascular OCT imaging technology is also crucial because it is currently the highest-resolution imaging modality that can be used to diagnose plaque erosion in living patients.”

When OCT is used for intravascular imaging, the imaging probe is automatically pulled backward inside a catheter, producing hundreds of images for each pullback. The researchers tested their method using 16 pullbacks of 5553 clinical OCT images with plaque erosion and 10 pullbacks of 3224 images without plaque erosion. The automated method correctly predicted 80% of the plaque erosion cases with a positive predictive value of 73%. They also found that diagnoses based on the automated method matched well with those from three experienced physicians.

The researchers said that further safety validation and regulatory approval are needed for stand-alone clinical use in patients. Still, without the need for time-consuming and tedious manual image analysis, the method has the potential to analyze massive amounts of existing OCT data. This could help scientists improve identification and treatment for plaque erosion. For example, a stent is often used to recover reduced blood flow in patients with acute coronary syndrome. Yet recent studies suggest that some medications might offer a less-invasive alternative.

Wang added that the newly developed approach could help physicians develop individualized treatment strategies for optimal management of patients with acute coronary syndrome.

The researchers are aiming to improve their technique by better incorporating 3D information and incorporating more unlabeled data to improve the AI model’s performance. They plan to use a larger data set that includes a global population for training and evaluating the algorithm.

The research was published in Biomedical Optics Express (www.doi.org/10.1364/BOE.459623).