• 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.

Training Surgical Skills

To enhance surgical training, new virtual skills trainers are being developed for tasks ranging from fundamentals of laparoscopic surgery to camera navigation and advanced suturing. We have been collaborating on the development and validation of these devices. In particular, in collaboration with Kitware, Inc, we are investigating the use of simulators to support surgeons in the practice of responding to adverse and rare events to improve skill proficiency and resilience.

Surgical Skill Assessment

Systematic and objective evaluation and feedback are crucial for effective training of psychomotor skills involved in point-of-injury care and hospital-based medicine (HBM). Our team has been conducting two large-scale projects, funded by the U.S. Army and in collaboration with RPI and FSU-FAMU, on the use of brain-based and video-based metrics for evaluating skill on laparoscopic suturing and on prolonged field care tasks (endotracheal intubation and cricothyrotomy). Our current studies will be able to show the benefits and limitations of these metrics relative to other measurement modalities for the classification of experts versus novices and how brain activation as individuals train and gain skill on the task. These outcomes will inform training programs, and surgeon and Army medic credentialing standards.

Mental Imagery

Non-invasive functional neuroimaging during surgical tasks has been explored as an objective assessment tool. Our group was the first to monitor brain activity during Fundamentals of Laparoscopic Surgery (FLS) tasks and demonstrated that neuroimaging data can reliably discriminate between novice and expert performance. However, brain activity during cognitive surgical tasks has not yet been examined. Our overall goal is to develop a neuroimaging methodology for the objective evaluation of cognitive surgical tasks.  

• UB Department of Computer Science and Engineering 
• Roswell Park Comprehensive Cancer Center
• Activ Surgical
  

• 2024 - SUNY at Buffalo AI for Health Grant
  

FACULTY

Peter C. Kim, MD PhD
Vice Chair - Research & Innovation Department of Surgery
Professor of Surgery

Dr. Kim also has developed, validated, and de-risked complex technologies and FDA-cleared advanced visualization devices, including surgical robots. Dr. Kim’s team was the first to show autonomous robotic soft tissue surgical tasks.

Steven D. Schwaitzberg, MD, FACS
SUNY Distinguished Service
Professor and Chairman - Department of Surgery

Dr. Schwaitzberg has developed, validated, and de-risked several complex technologies, and chaired the FDA human factors committee for medical devices. Dr. Schwaitzberg will co-lead preclinical/clinical validation and data accrual, segmentation, and validation.

Jinjun Xiong, PhD
SUNY Empire Innovation Professor
Professor
Department of Computer Science and Engineering
School of Engineering and Applied Sciences

Dr. Xiong is an expert in the application of cognitive computing from industrial solutions and is the director for the SUNY at Buffalo Institute for Artificial Intelligence and Data Science. Dr. Xiong serves as a technical supervisor for the UB DARTS multiple AI and computer vision projects.

Gene Yang, MD, MS
Clinical Assistant Professor
Department of Surgery

Dr. Yang is a MIS surgeon and former Medtronics Innovation fellow with deep interests in data accrual, transparency and traceability.  He has been exploring advanced local hashing techniques, providing a framework that respects the confidentiality and integrity of patient information while facilitating educational and research opportunities in the surgical field.

Junsong Yuan PhD
Professor and Director of Visual Computing Lab
Department of Computer Science and Engineering
School of Engineering and Applied Sciences

Dr. Yuan is an expert in computer vision, pattern recognition, video analytics, gesture and action analysis, and large-scale visual search. Dr. Yuan serves as a technical supervisor for UB DARTS for computer vision projects currently focused on surgical instrument counting and classification.

P. Ben Ham III, MD, MS
Assistant Professor
Department of Surgery

Dr. Ham is a pediatric surgeon and Director of IT for Kaleida Health affiliated with the University at Buffalo. As a clinician champion, he leads and manages IT infrastructure, data security and usability of the Cerner EHR at the Oshei Children’s Hospital.

TRAINEES

Brian Quaranto, MD
Surgical Innovation Fellow

Dr. Quaranto is a recent graduate from the UB General Surgery Residency program and will be working to support all functions of the UB DARTS team. Dr. Quaranto has developed hardware and software solutions to capture and analyze the surgeon’s unique visual perspective in the operating room and is working on developing pathways to commercialization for surgical computer vision tools.

Garrett Skinner, MD
3rd Year General Surgery Resident

Dr. Skinner was Surgical Innovator-in-Residence at Activ Surgical from 2022-2024 where he helped investigate clinically novel near-infrared imaging quantification and surgical artificial intelligence . Now returning to complete his residency in General Surgery, he will continue breaking new ground integrating multi-modal large language models with surgical computer vision models.

Jiajie Li
3rd Year Computer Vision PhD Student
Department of Computer Science and Engineering
School of Engineering and Applied Sciences

Emily Hannah
4th Year Medical Student
Jacobs School of Medicine and Biomedical Sciences

Ascharya Balaji
4th Year Medical Student
Jacobs School of Medicine and Biomedical Sciences

Joshua Marek
2nd Year Medical Student
Jacobs School of Medicine and Biomedical Sciences

Gabriela Miletsky
2nd Year Medical Student
Jacobs School of Medicine and Biomedical Sciences     

2024

Real-time near infrared artificial intelligence using scalable non-expert crowdsourcing in colorectal surgery
G. Skinner, T. Chen, G. Jentis, and Y. Liu, C. McCulloh, A. Harzman, E. Huang, M. Kalady, P. Kim
npj Digital Medicine, 2024

Clinical Utility of Laser Speckle Contrast Imaging and Real-Time Quantification of Bowel Perfusion in Minimally Invasive Left-Sided Colorectal Resections
G. Skinner, Y. Liu, A. Harzman, and S. Husain, A. Gasior, L. Cunningham, A. Traugott, C. McCulloh, M. Kalady, P. Kim, E. Huang
Diseases of the Colon & Rectum, 2024

Dye-less Quantification of Tissue Perfusion by Laser Speckle Contrast Imaging is Equivalent to Quantified Indocyanine Green in a Porcine Model
G. Skinner, M. Marois, J. Oberlin, and C. McCulloh, S. Schwaitzberg, P. Kim
Surgical Endoscopy (Accepted), 2024

2023

Real-time First-In-Human Comparison of Laser Speckle Contrast Imaging and ICG in Minimally Invasive Colorectal & Bariatric Surgery
Chibueze A. Nwaiwu, Christopher J. McCulloh, Garrett Skinner, and Shinil K. Shah, Peter C.W. Kim, Steven D. Schwaitzberg, Erik B. Wilson
Journal of Gastrointestinal Surgery, 2023