Improving efficiency and operator proficiency during percutaneous coronary interventions utilizing a standardized optical coherence tomography workflow.

Objectives: This study was designed to compare efficiency and quality metrics between percutaneous coronary intervention (PCI) procedures using optical coherence tomography (OCT) guided by a variable workflow versus a standardized workflow in a real-world population.

Background: The LightLab (LL) Initiative was designed to evaluate the impact of a standardized OCT workflow during PCI to address barriers to adoption.

Methods: The LL Initiative was a multicenter, prospective, observational study.

Self-supervised ARTMAP.

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semi-supervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced.