DARTS

The University at Buffalo Data Augmented Research Technology in Surgery (DARTS) laboratory is a unique collaboration between surgeon innovators and machine learning scientists. We inspire to democratize surgery by building a scalable and accessible surgical data science paradigm. We develop and deliver novel applications of computer vision, advanced intraoperative imaging, and artificial intelligence including multimodal large language models in surgery to improve patient outcomes, to educate and train the next generation of surgeons, and to enhance surgical expertise and excellence.

  • SurgiVdoNet digital commons grant accepted for AI for Health seed funding by SUNY at Buffalo, PI: G Yang & P Ham.

  • “Real-time near infrared artificial intelligence using scalable non-expert crowdsourcing in colorectal surgery” in npj Digital Medicine. Skinner et al.

Surgical Intelligence

Creating AI models with expert level understanding of surgery

Using machine learning, deep neural networks, and multi-modal large language models, our group trains AI models to understand surgical video at an expert level. Such models are used to investigate perception of surgical video information, supplement surgical education and assist with prospective surgical action decision making (Skinner et al., 2024).

Indocyanine Green Fluorescence

Investigating novel applications and quantification of ICG

Our group investigates the application of indocyanine green fluorescence for perfusion assessment and biliary structure identification (Skinner et al., 2024). We investigate objective quantification of indocyanine green which has shown higher correlation with important clinical outcomes like anastomotic leak compared to subjective interpretation.

Laser Speckle Contrast Imaging (LCSI)

Investigating clinically novel LSCI technology

Our group investigates the application of the clinically novel laser speckle contrast imaging. This technology helps surgeons assess tissue perfusion without the use of contrast dye, that is required with indocyanine green fluorescence. (Nwaiwu et al., 2023)

We have shown that subjective interpretation of LSCI is equivalent to ICG in left-sided colorectal resections.(Skinner et al., 2024), and that objective quantification of LSCI is equivalent to quantification of ICG in pre-clinical porcine model. (Skinner et al., 2024)

Surgical Video Digital Commons

Bringing blockchain technology to surgical video enhance privacy and security

Our group has recently received seed funding from the competitive, interdisciplinary UB AI and Health fund in order to develop software to more safely collect and store and surgical videos while simultaneously making these videos accessible and analyzable for researchers in the field.

  • 2024 - SUNY at Buffalo AI for Health Grant