# Machine Learning Algorithms for Classification of Microcirculation Images from Septic and Non-Septic Patients

arXiv: https://arxiv.org/abs/1811.02659 · HTML: https://mlsanity.com/paper/1811.02659/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [CapillaryNet: An Automated System to Quantify Skin Capillary Density and Red Blood Cell Velocity from Handheld Vital Microscopy](https://mlsanity.com/paper/2104.11574/) (2021)
- [CapillaryX: A Software Design Pattern for Analyzing Medical Images in Real-time using Deep Learning](https://mlsanity.com/paper/2204.08462/) (2022)
- [Deep Learning and Computer Vision Techniques for Microcirculation Analysis: A Review](https://mlsanity.com/paper/2205.05493/) (2022)
- [A Sequence Agnostic Multimodal Preprocessing for Clogged Blood Vessel Detection in Alzheimer's Diagnosis](https://mlsanity.com/paper/2211.03109/) (2022)

## Abstract (arXiv)

Sepsis is a life-threatening disease and one of the major causes of death in hospitals. Imaging of microcirculatory dysfunction is a promising approach for automated diagnosis of sepsis. We report a machine learning classifier capable of distinguishing non-septic and septic images from dark field microcirculation videos of patients. The classifier achieves an accuracy of 89.45%. The area under the receiver operating characteristics of the classifier was 0.92, the precision was 0.92 and the recall was 0.84. Codes representing the learned feature space of trained classifier were visualized using t-SNE embedding and were separable and distinguished between images from critically ill and non-septic patients. Using an unsupervised convolutional autoencoder, independent of the clinical diagnosis, we also report clustering of learned features from a compressed representation associated with healthy images and those with microcirculatory dysfunction. The feature space used by our trained classifier to distinguish between images from septic and non-septic patients has potential diagnostic application.