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

Garrett Skinner^1,2, Tina Chen², Gabriel Jentis², Yao Liu^2,3, Christopher McCulloh², Alan Harzman⁴, Emily Huang⁴, Matthew Kalady⁴, Peter Kim^5,6

¹Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.

²Activ Surgical, University at Buffalo, Buffalo, NY, USA.

³Warren Alpert Medical School Alpert Medical School of Brown University, Providence, RI, USA.

⁴The Ohio State University Wexner Medical Center, Columbus, OH, USA.

⁵Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA. pckim@buffalo.edu.

⁶Activ Surgical, University at Buffalo, Buffalo, NY, USA. pckim@buffalo.edu.

PubMed PMC DOI PDF

Abstract

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 deplo…

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