Optimization of anastomotic technique and gastric conduit perfusion with hyperspectral imaging and machine learning in an experimental model for minimally invasive esophagectomy.

INTRODUCTION: Esophagectomy is the mainstay of esophageal cancer treatment, but anastomotic insufficiency related morbidity and mortality remain challenging for patient outcome. Therefore, the objective of this work was to optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. MATERIAL AND METHODS: A live porcine model (n = 58) for MIE was used with gastric conduit formation and simulation of linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and simulation of anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic simulation site was evaluated using HSI and was validated with histopathology. RESULTS: The tissue oxygenation (ΔStO2) after the anastomotic simulation remained constant only for the short stapler in caudal position (-0.4 ± 4.4%, n.s.) while it was impaired markedly in the other groups (short-cranial: -15.6 ± 11.5%, p = 0.0002; long-cranial: -20.4 ± 7.6%, p = 0.0126; long-caudal: -16.1 ± 9.4%, p < 0.0001). Tissue samples from avascular stomach as measured by HSI showed correspondent eosinophilic pre-necrotic changes in 35.7 ± 9.7% of the surface area. CONCLUSION: Tissue oxygenation at the site of anastomotic simulation of the gastric conduit during MIE is influenced by stapling technique. Optimal oxygenation was achieved with a short stapler (3 cm) and sufficient distance of the simulated anastomosis to the cranial end of the gastric conduit. HSI tissue deoxygenation corresponded to histopathologic necrotic tissue changes. The experimental model with HSI and ML allow for systematic optimization of gastric conduit perfusion and anastomotic technique while clinical translation will have to be proven.

Application of a semiautomatic boundary detection algorithm for the assessment of amniotic fluid quantity from ultrasound images.

The aim of this study was to develop a computer-assisted method to evaluate amniotic fluid volume (AFV). This was done by automatically detecting the boundaries of the amniotic fluid portion in 2-D ultrasonographic images. The study population consisted of 36 low-risk patients that were selected at random from a healthy population undergoing routine pregnancy follow-up. For each patient, images of the four quadrants of the uterus were digitized into a PC. The amniotic fluid portion in each ultrasonographic image was automatically detected, and its area was calculated. Its area was also manually determined by an expert physician (R. T.). The areas automatically detected by the algorithm were highly correlated with the areas manually delimited by the expert: r2 = 0.9722 (p < 0.01). The areas calculated by the program provide a good measure for the areas determined by the expert and may, therefore, be used for calculating the actual amniotic fluid volume.