SBIR Phase I: Surgical training platform with customizable training scenarios enabled by 3D printing and artificial intelligence

Award ID: 2304526

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a customizable surgical training platform supported by artificial intelligence. Existing surgical simulators allow trainees to practice in safe environments prior to operating on patients. These simulators are limited in recreating the challenges of the operating room and providing feedback to assess trainees performance. The technology developed in this project focuses on forming a better understanding of methods and techniques to recreate synthetic patient anatomy and how to provide higher quality assessments of surgical training procedures. The project will provide a platform for improved medical training of medical students, residents and surgeons that results in better skilled medical practitioners that deliver higher quality patient outcomes. This Small Business Innovation Research (SBIR) Phase I project focuses on raising the quality of surgical training platforms by improving the realism of recreated anatomy and enabling scenario customization during training. The project uses techniques of 3D printing, mechanical testing, and machine learning to characterize the properties of synthetic anatomy and objectively assess trainees surgical performance for each scenario. The artificial intelligence will be trained by mechanical measurements of synthetic anatomy before/after training operations from users. Comparisons of surgical performance will be conducted between experienced, practicing surgeons and inexperienced/less experienced medical students. The anticipated results are the development of a customizable surgical platform that provides objective feedback on a personalized basis to improve the standards of surgical practice among trainees. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Alastair Monk