Machine-Learning-Based Functional Microcirculation Analysis


  • Ossama Mahmoud Western University
  • GH Janssen Western University
  • Mahmoud R El-Sakka Western University



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%).




How to Cite

Mahmoud, O., Janssen, G., & El-Sakka, M. R. (2020). Machine-Learning-Based Functional Microcirculation Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 34(08), 13326-13331.



IAAI Technical Track: Emerging Papers