Developing Interdisciplinary Models for Surgical Education (2026-2027)

Background

As surgical technology rapidly expands with innovations such as robotic platforms, advanced imaging and minimally invasive techniques, surgical trainees face mounting pressure to master technical skills in limited time. High-quality, objective feedback is essential for skill development, yet most current assessment tools rely on subjective evaluations with variable reliability.

There is a critical need for high-fidelity, validated models and tools that assess technical performance objectively and help tailor individualized training. This interdisciplinary project brings together surgery, engineering and education to develop and validate next-generation assessment models.

Project Description

This project will expand efforts begun by a previous team to design, build, validate and implement technical skills assessments grounded in engineering, medical education science and clinical expertise. Work will focus on four major domains, with additional expansion into cholecystectomy and robotic thoracic surgery education.

Open suturing and knot tying
A porcine tissue model with integrated force sensors will measure real-time tissue tension as trainees perform various types of sutures. Technique will be assessed using these sensors and an expert-developed checklist.

Laparoscopic spatial awareness and dexterity
The team will develop 3D-printed laparoscopic training maps in collaboration with medical illustrators. Assessments will integrate motion sensor data, video-based machine learning analysis and validated qualitative ratings.

Hand strength and dexterity
Using Purdue Pegboard testing and Jamar Hydraulic Hand and Pinch Dynamometers, surgical faculty will establish normative data for hand strength and dexterity. A Duke occupational therapist will design improvement plans for residents as needed.

Each novel assessment will undergo validation processes. The team will also work to develop improvement plans for each skill tested.

Anticipated Outputs

Validated, high-fidelity training and assessment models
Novel quantitative and machine learning–based tools for surgical skill evaluation
Conference presentations and peer-reviewed publications
Data to support future grant applications connecting technical skill assessment to clinical outcomes
Expanded simulation models for laparoscopic cholecystectomy and robotic thoracic surgery

Student Opportunities

Ideally, this team will include 2–3 graduate or professional students and 6–8 undergraduates from education, engineering, medicine and related fields. The project will include two subteams: an engineering model team focused on the technical elements of model development and a surgical education team focused on the pedagogy components of the project.

Depending on specialization, students will have opportunities to:

Participate in prototyping, 3D printing, hydrogel fabrication and sensor integration
Collect performance data and conduct validation analyses
Contribute to curriculum design for technical skills improvement
Join interdisciplinary working groups across engineering and surgical education
Contribute to scholarly publications

Selected advanced students — including engineering Ph.D. students and surgical residents — will serve as subteam leaders and mentors. Some students may have the opportunity to present work at regional and national conferences.

Timing

Summer 2026 – Summer 2027

Summer 2026 (optional):

Literature review
Support model development already underway

Fall 2026:

Training on foundational concepts in surgical education and model building
Establishment of subteams

Spring 2027:

Model development and validation studies
Data analysis and preparation of manuscripts and posters

Summer 2027 (optional):

Continued model refinement
Presentation of findings at conferences

Crediting

Academic credit available for fall and spring semesters