期刊
SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES
卷 33, 期 6, 页码 1988-1997出版社
SPRINGER
DOI: 10.1007/s00464-018-6512-6
关键词
Fluorescence angiography; Fluorescence-based Enhanced Reality; Computer-assisted analysis of fluorescence signal; Tissue perfusion; Machine learning
类别
资金
- ARC Foundation for Cancer Research, a French foundation
BackgroundArterial 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.MethodsIn 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 10min for 50min. Capillary lactates were measured on the sigmoid at the 3 ROIs. Various machine learning algorithms were applied for ischemia patterns recognition.ResultsThe time-to-peak at the ischemic ROI C was significantly longer in group A versus V (20.113 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.Conclusionsp id=Par4The computer-assisted dynamic analysis of the fluorescence signal enables the discrimination between different bowel ischemia models.
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