OCTs is a novel imaging modality that provides high resolution pictures of the coronary arteries. Unlike other diagnostic tools, OCTs reveal finer details of the innermost layers of the blood vessels and can be used to draw conclusions beyond the mere presence of plaques in the arteries. We can now characterize and classify plaques and lesions based on their associated risk to provide a more solid treatment strategy. However, there are challenges preventing the proliferation of this new imaging modality. Reading a full OCT pullback is a is a rather time consuming task, especially if the characterization of lesions and extraction of quantifiable measures is required. Furthermore, there is often a lack of consistent consensus among medical specialists when it comes to interpreting OCT results. Finally, the presence of artefacts and low image quality, often caused by improper acquisition by the medical staff, can make the images unusable requiring to repeat this invasive procedure. This project explores the use of deep learning (DL) for speeding up the reading of OCT pullbacks and providing a dependable second opinion, providing healthcare professionals with a trustworthy and easily interpretable solution. The opportunity to perform real-time image quality assessment can also ensure that the procedure will not have to be repeated. This integration of AI promises to standardize OCT procedures, making them faster, more reliable, and ultimately more accessible for patients in need.