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Real-time AI 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.
We validate this by training and deploying a highly accurate, real-time AI-assisted multimodal imaging platform to increase precision when assessing tissue perfusion which may help reduce complications such as anastomotic leak in bowel surgery 6, 7. This accelerated model development using crowdsource annotations will further enable additional applications of AI-assisted multimodal imaging data for enhanced real-time clinical decision support for safer surgery and improved outcomes. Skinner, G. et al. Clinical Utility of Laser Speckle Contrast Imaging (LSCI) Compared to Indocyanine Green (ICG) and Quantification of Bowel Perfusion in Minimally Invasive, Left-Sided Colorectal Resections.
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