Dye-less quantification of tissue perfusion by laser speckle contrast imaging is equivalent to quantified indocyanine green in a porcine model.

Introduction: Subjective surgeon interpretation of near-infrared perfusion video is limited by low inter-observer agreement and poor correlation to clinical outcomes. In contrast, quantification of indocyanine green fluorescence video (Q-ICG) correlates with histologic level of perfusion as well as clinical outcomes. Measuring dye volume over time, however, has limitations, such as it is not on-demand, has poor spatial resolution, and is not easily repeatable.

Real-time near infrared artificial intelligence using scalable non-expert crowdsourcing in colorectal surgery.

Surgical artificial intelligence (AI) has the potential to improve patient safety and clinical outcomes. To date, training such AI models to identify tissue anatomy requires annotations by expensive and rate-limiting surgical domain experts. Herein, we demonstrate and validate a methodology to obtain high quality surgical tissue annotations through crowdsourcing of non-experts, and real-time deployment of multimodal surgical anatomy AI model in colorectal surgery.

Clinical Utility of Laser Speckle Contrast Imaging and Real-Time Quantification of Bowel Perfusion in Minimally Invasive Left-Sided Colorectal Resections.

Background: Left-sided colorectal surgery demonstrates high anastomotic leak rates, with tissue ischemia thought to influence outcomes. Indocyanine green is commonly used for perfusion assessment, but evidence remains mixed for whether it reduces colorectal anastomotic leaks. Laser speckle contrast imaging provides dye-free perfusion assessment in real-time through perfusion heat maps and quantification.