# Review and Recommendations for using Artificial Intelligence in Intracoronary Optical Coherence Tomography Analysis

arXiv: https://arxiv.org/abs/2501.18614 · HTML: https://mlsanity.com/paper/2501.18614/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 84 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [REFORMS: Reporting Standards for Machine Learning Based Science](https://mlsanity.com/paper/2308.07832/) (2023)
- [Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images](https://mlsanity.com/paper/1702.02223/) (2017)
- [Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries](https://mlsanity.com/paper/2204.10162/) (2022)
- [Automated segmentation of microvessels in intravascular OCT images using deep learning](https://mlsanity.com/paper/2210.00166/) (2022)
- [Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images](https://mlsanity.com/paper/2311.06202/) (2023)

## Abstract (arXiv)

Artificial intelligence (AI) methodologies hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and February 2023 describing AI-based diagnosis of CAD using IVOCT. Our search identified 5,576 studies, with 513 included after initial screening and 35 studies included in the final systematic review after quality screening. Our findings indicate that most of the identified models are not currently suitable for clinical use, primarily due to methodological flaws and underlying biases. To address these issues, we provide recommendations to improve model quality and research practices to enhance the development of clinically useful AI products.