Deep-learning-driven optical coherence tomography analysis for cardiovascular outcome prediction in patients with acute coronary syndrome.

ABSTRACT:

Aims

Optical coherence tomography (OCT) can identify high-risk plaques indicative of worsening prognosis in patients with acute coronary syndrome (ACS). However, manual OCT analysis has several limitations. In this study, we aim to construct a deep-learning model capable of automatically predicting ACS prognosis from patient OCT images following percutaneous coronary intervention (PCI).

Methods and results

Post-PCI OCT images from 418 patients with ACS were input into a deep-learning model comprising a convolutional neural network (CNN) and transformer. The primary endpoint was target vessel failure (TVF). Model performances were evaluated using Harrell's C-index and compared against conventional models based on human observation of quantitative (minimum lumen area, m

SUBMITTER: Hamana T 

PROVIDER: S-EPMC11570387 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature