Discrimination between arterial and venous bowel ischemia by computer-assisted analysis of the fluorescent signal

Arterial blood supply deficiency and venous congestion both play a role in anastomotic complications. Our aim was to evaluate a software-based analysis of the fluorescence signal to recognize the patterns of bowel ischemia.

In 18 pigs, two clips were applied on the inferior mesenteric artery (group A: n = 6) or vein (group V: n = 6) or on both (group A-V: n = 6). Three regions of interest (ROIs) were identified on the sigmoid: P = proximal to the first clip; C = central, between the two clips; and D = distal to the second clip. Indocyanine Green was injected intravenously. The fluorescence signal was captured by means of a near-infrared laparoscope. The time-to-peak (seconds) and the maximum fluorescence intensity were recorded using software. A normalized fluorescence intensity unit (NFIU: 0-to-1) was attributed, using a reference card. The NFIU's over-time variations were computed every 10 min for 50 min. Capillary lactates were measured on the sigmoid at the 3 ROIs. Various machine learning algorithms were applied for ischemia patterns recognition.

The time-to-peak at the ischemic ROI C was significantly longer in group A versus V (20.1 ± 13 vs. 8.43 ± 3.7; p = 0.04) and in group A-V versus V (20.71 ± 11.6 vs. 8.43 ± 3.7; p = 0.03). The maximal NIFU at ROI C, was higher in the V group (1.01 ± 0.21) when compared to A (0.61 ± 0.11; p = 0.002) and A-V (0.41 ± 0.2; p = 0.0005). Capillary lactates at ROI C were lower in V (1.3 ± 0.6) than in A (1.9 ± 0.5; p = 0.0071), and A-V (2.6 ± 1.5; p = 0.034). The K nearest neighbor and the Linear SVM algorithms provided both an accuracy of 75% in discriminating between A versus V and 85% in discriminating A versus A-V. The accuracy dropped to 70% when the ML had to identify the ROI and the type of ischemia simultaneously.

The computer-assisted dynamic analysis of the fluorescence signal enables the discrimination between different bowel ischemia models.