TY - JOUR AU - Mahmoud, Ossama AU - Janssen, GH AU - El-Sakka, Mahmoud R PY - 2020/04/03 Y2 - 2024/03/29 TI - Machine-Learning-Based Functional Microcirculation Analysis JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 08 SE - IAAI Technical Track: Emerging Papers DO - 10.1609/aaai.v34i08.7044 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7044 SP - 13326-13331 AB - <p>Analysis of microcirculation is an important clinical and research task. Functional analysis of the microcirculation allows researchers to understand how blood flowing in a tissues’ smallest vessels affects disease progression, organ function, and overall health. Current methods of manual analysis of microcirculation are tedious and time-consuming, limiting the quick turnover of results. There has been limited research on automating functional analysis of microcirculation. As such, in this paper, we propose a two-step machine-learning-based algorithm to functionally assess microcirculation videos. The first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures. While the second step uses a 3D-CNN to assess whether the vessel-like structures contained flowing blood. To our knowledge, this is the first application of machine learning for functional analysis of microcirculation. We use real-world labelled microcirculation videos to train and test our algorithm and assess its performance. More precisely, we demonstrate that our two-step algorithm can efficiently analyze real data with high accuracy (90%).</p> ER -