A new convolutional neural network-construct for sepsis enhances pattern identification of microcirculatory dysfunction

Background

Triggers of organ dysfunction have been associated with the worsening of microcirculatory dysfunction in sepsis, and because microcirculatory changes occur before macro-hemodynamic abnormalities, they can potentially detect disease progression early on. The difficulty in distinguishing altered microcirculatory characteristics corresponding to varying stages of sepsis severity has been a limiting factor for the use of microcirculatory imaging as a diagnostic and prognostic tool in sepsis. The aim of this study was to develop a convolutional neural network (CNN) based on progressive sublingual microcirculatory dysfunction images in sepsis, and test its diagnostic accuracy for these progressive stages.

Methods

Sepsis was induced in Wistar rats (2 mL of E. coli 10⁸ CFU/mL inoculation into the jugular vein), and 2 mL saline injection in sham animals was the control. Sublingual microvessels of all animals with surrounding tissue images were captured by Sidestream dark field imaging (SDF) at T0 (basal) and T2, T4, and T6 h after sepsis induction. From a total of 137 videos, 37.930 frames were extracted; a part (29.341) was used for the training of Resnet-50 (CNN-construct), and the remaining (8.589) was used for validation of accuracy.

Results

The CNN-construct successfully classified the various stages of sepsis with a high accuracy (97.07%). The average AUC value of the ROC curve was 0.9833, and the sensitivity and specificity ranged from 94.57% to 99.91%, respectively, at all time points.

Conclusions

By blind testing with new sublingual microscopy images captured at different periods of the acute phase of sepsis, the CNN-construct was able to accurately diagnose the four stages of sepsis severity. Thus, this new method presents the diagnostic potential for different stages of microcirculatory dysfunction and enables the prediction of clinical evolution and therapeutic efficacy. Automated simultaneous assessment of multiple characteristics, both microvessels and adjacent tissues, may account for this diagnostic skill. As such a task cannot be analyzed with human visual criteria only, CNN is a novel method to identify the different stages of sepsis by assessing the distinct features of each stage.