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

Garrett Skinner; Tina Chen; Gabriel Jentis; Yao Liu; Christopher McCulloh; Alan Harzman; Emily Huang; Matthew Kalady; Peter Kim

doi:10.1038/s41746-024-01095-8

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

NPJ Digit Med. 2024 Apr 22;7(1):99. doi: 10.1038/s41746-024-01095-8.

Authors

Garrett Skinner^{1

2}, Tina Chen², Gabriel Jentis², Yao Liu^{2

3}, Christopher McCulloh², Alan Harzman⁴, Emily Huang⁴, Matthew Kalady⁴, Peter Kim^{5

6}

Affiliations

¹ Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
² Activ Surgical, University at Buffalo, Buffalo, NY, USA.
³ Warren Alpert Medical School Alpert Medical School of Brown University, Providence, RI, USA.
⁴ The Ohio State University Wexner Medical Center, Columbus, OH, USA.
⁵ Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA. pckim@buffalo.edu.
⁶ Activ Surgical, University at Buffalo, Buffalo, NY, USA. pckim@buffalo.edu.

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 deployment of multimodal surgical anatomy AI model in colorectal surgery.