#  A Robot-Assisted Perfusion System to Improve Patient Safety in the Cardiac Operating Room 

 



### Overview

The vast majority of pediatric and adult cardiac surgery procedures worldwide involve the use of cardiopulmonary bypass with a dedicated and highly customized perfusion system. Cardiac surgery procedures are complex and entail safety-critical activities, requiring continuous coordination between four subteams: perfusion, surgical, nursing and anesthesia. This environment demands outstanding technical and non-technical skills (e.g. teamwork, communication and situational awareness). Over the past few decades, technological advancements have improved the safety and efficiency of the perfusion system, however, despite substantial progress, recent studies continue to report a high incidence of preventable intraoperative adverse events among cardiac surgery patients. The perfusion system, in particular, relies heavily on the expertise and skills of the perfusionist, and there is currently no computational intelligent system to support perfusionists' optimal decision-making during the critical phase of cardiopulmonary bypass. In this proposal, we seek to develop a data-driven approach to learn from expert perfusionists how to achieve optimal outcomes for cardiac surgery patients. Rather than attempt to engineer a solution, we propose to develop a computer-based apprentice that can learn from high-quality demonstrations of perfusionist actions to infer gold-standard patient care. Our goal is to develop and evaluate a Robot-Assisted Perfusion System (RAPS) that can be integrated into the cardiac surgery workflow as a non-human teammate. The RAPS will support the perfusion team in a way that perfusionists still will keep control of the perfusion system (i.e. human- in-the-loop), but cognitively supported and guided by the RAPS.

### Funding

NIH Award Number: [5R01HL157457-03](https://reporter.nih.gov/search/JIXJt3XL_EqOX8_YpV9S5Q/project-details/10907677), [1R01HL157457-01](https://reporter.nih.gov/search/JIXJt3XL_EqOX8_YpV9S5Q/project-details/10907677)

### Collaborators

- Prof. Roger D. Dias, PhD, MBA, MD
- Prof. Matthew Gombolay, PhD
- Dr. Julian M. Goldman, MD
- Geoffrey Rance, CCP, BS
- Rithy Srey, CCP, MS
- Paul O’Gara, CCP, LP

### Publications & Presentations

- Harari, R., Dias, R. D., Salas, E., Unhelkar, V., Chaspari, T., & **Zenati, M.** (2024). [Misalignment of Cognitive Processes within Cardiac Surgery Teams](https://pmc.ncbi.nlm.nih.gov/articles/PMC11287473/). *The Hamlyn Symposium on Medical Robotics*.
- Dias, R. D., Harari, R. E., **Zenati, M. A.**, Rance, G., Srey, R., Chen, L., & Gombolay, M. (2024). [A Clinician-Centered Explainable Artificial Intelligence Framework for Decision Support in the Operating Theatre](https://pmc.ncbi.nlm.nih.gov/articles/PMC11285016/). *The Hamlyn Symposium on Medical Robotics*.
- Harari, R. E., Dias, R. D., Kennedy-Metz, L. R., Varni, G., Gombolay, M., Yule, S., Salas, E., & **Zenati, M. A.** (2024). [Deep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skills](https://pmc.ncbi.nlm.nih.gov/articles/PMC11292454/). *JAMA Network Open*.
- Dias, R. D., Yule, S. J., Harari, R., & **Zenati, M. A.** (2024). [Exploring Intraoperative Cognitive Biases in Cardiac Surgery Teams](https://pmc.ncbi.nlm.nih.gov/articles/PMC11342855/). *Applied Human Factors and Ergonomics Conference*.
- Mishra, S., Dias, R. D., **Zenati, M. A.**, & Chaspari, T. (2024). [Acoustic Patterns of Interprofessional Communication and Quality of Teamwork in the Cardiac Operating Theatre](https://pmc.ncbi.nlm.nih.gov/articles/PMC11285017/). *The Hamlyn Symposium on Medical Robotics*.
- Dias, R. D., Kennedy-Metz, L. R., Srey, R., Rance, G., Ebnali, M., Arney, D., Gombolay, M., & **Zenati, M. A.** (2023). [Using Digital Biomarkers for Objective Assessment of Perfusionists' Workload and Acute Stress During Cardiac Surgery](https://pmc.ncbi.nlm.nih.gov/articles/PMC10371197/). *International Work-Conference on Bioinformatics and Biomedical Engineering*.
- Dias, R. D., **Zenati, M. A.**, Rance, G., Srey, R., Arney, D., Chen, L., Paleja, R., Kennedy-Metz, L. R., & Gombolay, M. (2022). [Using machine learning to predict perfusionists' critical decision-making during cardiac surgery](https://pmc.ncbi.nlm.nih.gov/articles/PMC9355042/). *Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization*.
- Ebnali, M., Kennedy-Metz, L. R., Conboy, H. M., Clarke, L. A., Osterweil, L. J., Avrunin, G., Miccile, C., Arshanskiy, M., Phillips, A., **Zenati, M. A.**, & Dias, R. D. (2022). [A Coding Framework for Usability Evaluation of Digital Health Technologies](https://pmc.ncbi.nlm.nih.gov/articles/PMC9413016/). *International Conference on Human-Computer Interaction*.
- Dias, R. D., Kennedy-Metz, L. R., Yule, S. J., Gombolay, M., & **Zenati, M. A.** (2022). [Assessing Team Situational Awareness in the Operating Room via Computer Vision](https://pmc.ncbi.nlm.nih.gov/articles/PMC9386571/). *IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIGMA)*.



 



 

 See also:- [ Research ](/page-categories/research)