What is this project?
Stryker is a global leader in medical technologies, including medical and surgical equipment for the operating theater. Students on this team will develop a vision system that identifies instruments used in surgery, helping to ensure they are all accounted for at the end of the surgery.
What am I going to do?
MDP projects push you to integrate interdisciplinary engineering knowledge and develop strategic problem-solving skills. On this project, students will develop a vision system that identifies instruments used in surgical procedures, helping to ensure they are all accounted for at the end of the surgery.
- Functional prototype of a surgical instrument machine vision system that can identify and track various surgical instruments, including:
- a model that can be validated in a controlled, simulated environment
- vision hardware selection
- proposal for mounting hardware in the operating room
- Strategic roadmap, incorporating points for refinement, and a strategy to produce a minimum viable product for use in a real operating room
- Tech Stack: PyTorch
Stretch Goal Opportunities Include:
- Implement in a clinical simulation center
- Collect user feedback from surgeons and operators, and refine the system
Why does it matter?
Surgeries require a significant number of instruments and consumables, some of which are very small, like sutures. The current process of accounting for these devices involves humans physically inventorying the instruments before and after the surgery. Students on this team will design and develop a vision/machine learning system to identify and account for the devices before and after surgery, improving the chances that no surgical instruments are left behind in the patient.
This project addresses a critical challenge in surgical safety, with direct implications for hospitals, healthcare systems, and patients worldwide. According to the Pennsylvania Patient Safety Authority, analysts estimate that 1 to 2 retained surgical instruments (RSIs) occur per 100,000 patient procedures. By developing an automated system to detect and account for surgical tools and consumables, students are contributing to a technology that can dramatically reduce the occurrence of retained surgical items. This not only protects patients from serious harm—such as infection, injury, or death—but also reduces the burden of legal and financial consequences for healthcare providers. The system has the potential to set a new standard in surgical quality control, improving trust, efficiency, and outcomes on a global scale.
Below are the skills needed for this project. Students with the following relevant skills and interests, regardless of major, are encouraged to apply! This is a team-based multidisciplinary project. Students on the team are not expected to have experience in all areas, but should be willing to learn and will be asked to perform a breadth of tasks throughout the two-semester project.
Computer Vision Algorithm (3 students)
Specific Skills: Implementation and tuning of computer vision / machine learning detection algorithms (e.g. CNN, YOLO, Detectron2, etc.)
EECS 281 (or equivalent) is required
EECS 442 or EECS 504 desired
Likely Majors: CS, CSE, ECE, ROB, DATA
Machine Learning (2 Students)
Specific Skills: Machine learning, ideally with experience deploying models on limited resource hardware
Likely Majors: CS, CSE, ECE, ROB, DATA
General Programming (2 Students)
Specific Skills: Creation of integrated computer vision tool (image acquisition, database structure, model implementation, and basic UI)
EECS 281 (or equivalent) is required
All team members are expected to develop computer vision / machine learning knowledge and skills
Likely Majors: CS, DATA
Hardware Experience Computer Vision (1 Student)
Specific Skills: Practical experience in hardware camera calibration (e.g. resolution tradeoffs), lighting
EECS 216 or equivalent is required
Likely Majors: EE, CE, ECE, ROB
Additional Desired Skills/Knowledge/Experience
Strong candidates will have familiarity or experience with some of the following items, and a positive attitude to learn what is necessary, as the project gets underway. If you have any of these characteristics, highlight them on your Experience and Interest Form and talk about them in your (optional) one way video interview.
- Stryker culture is high-energy and quality driven. We value those who are good team members, demonstrating a hands-on, proactive approach to their work. We particularly appreciate teammates able to work across disciplinary categories and contribute widely
- Passion for the field of health care
- Successful team-based experience in any context
- Practical experience developing computer vision, decision systems, and/or ML systems
- Experience deploying ML models on limited-resource hardware using TensorRT, ONNX, etc.
Recommended Coursework
Include completed relevant courses (term, institution, course number/title, and grade). If you’ve completed any of the following courses, we recommend mentioning them in your application materials:
- EECS 442: Computer Vision
- EECS 504: Foundations of Computer Vision
- EECS 505: Computational Data Science and Machine Learning
- EECS 542: Advanced Topics in Computer Vision
- EECS 545: Machine Learning (CSE)
Sponsor Mentor
Patrick Lafleche
Patrick Lafleche is a Chief Engineer/Portfolio Lead Engineer for Stryker’s Surgical Technologies business, and he has been with Stryker for over 23 years. Patrick has a degree in Mechanical Engineering, and specializes in managing a portfolio of products from a technical point of view. Over his career, Patrick has been part of over 25 product launches, sat on various standards committees, is the inventor of multiple patents, and is also trained in design thinking methodology and product management.
Martin Griffin
Martin Griffin is a Senior R&D Manager – Software at Stryker, with over 10 years of experience as a technical leader and full-stack engineer specializing in cloud, web, cybersecurity, and mobile technologies. He has spent nearly a decade at Stryker, where he has led cybersecurity efforts for FDA 510k approvals, managed multiple agile software teams, and successfully launched digital medical device products, including AI-powered SaMD applications. His expertise spans the full technology stack, from designing and architecting Android/iOS applications, to developing backend web services and platforms, with a strong focus on medical device software and regulatory compliance. He combines deep technical knowledge and business acumen with a focus on digital product security and innovation.
Slaven Sutalo
Slaven Sutalo is the Senior Manager of the marketing team that’s focused on bringing to market products that help promote Patient Safety for the Surgical Technologies business. He has over 10 years of marketing experience that range from new product development, through commercialization, lifecycle management, and development of brand-new markets. We work with customers and end-users to identify pain points, and figure out how to turn that into successful products and businesses.
Faculty Mentor
Sindhu Kutty
Electrical Engineering and Computer Science
Dr. Kutty is a faculty member and the Director of the Teaching Lab in the Division of Computer Science and Engineering at the University of Michigan. She is also the Chair of Undergraduate Research Initiatives in which role, she oversees the experience of undergraduate researchers in the division. She has introduced a new course focused on introducing undergraduates to research in Machine Learning. Her research work with undergraduate students and other collaborators has been recognized by awards at various national and international conferences and competitions. Her work as an educator has been recognized by the Jon R. and Beverly S. Holt Award for Excellence in Teaching and by the EECS Outstanding Achievement Award. She has previously held faculty positions at the University of Detroit Mercy and at Swarthmore College.
Project Meetings
During the winter 2026 semester, the Stryker Vision team will meet on North Campus on TBD.
Work Location
Most of the work will be taking place on campus in Ann Arbor. Students will have access to the engineering services department at Stryker Instruments Kalamazoo for testing or access to specialized equipment, should the need arise.
Course Substitutions: ChE Elective, BME CD or PiP, CS Capstone/MDE, DATA Capstone, DATA Graduate Capstone, CoE Honors, MECHENG 590, ROB Flex Tech , ROB 590
Citizenship Requirements: This project is open to all students. Note: International students on an F-1 visa will be required to declare part time CPT during Winter 2026 and Fall 2026 terms.
IP/NDA: Students will sign an IP and NDA agreement that is unique to Stryker.
Summer Project Activities: No summer activity is required. Qualified students will be guaranteed an opportunity to interview for summer 2026 internships. Students must have the right to work in the U.S.A. indefinitely, without sponsorship, to participate in a summer internship.