Contributed Papers 1

Moderator: Carl Nelson, University of Nebraska-Lincoln

Presentations in this session were chosen from the peer-reviewed contributed papers. The papers will be published in the 2026 Proceedings of the Design of Medical Devices Conference in the ASME Digital Collection.

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Presentation Details

Strepapp: Instant Strep Throat Diagnosis using Deep Learning on Smartphone Oropharyngeal Images

Pediatric Forensic Medical Exam Training Device

Child sexual abuse remains a significant public health concern, and pediatric forensic medical exams require highly specialized, technically precise skills such as labial traction. However, no prepubescent training models for this purpose currently exist, which contributes to low clinician confidence and limited access to trained providers. This work presents a pediatric forensic medical exam training device designed to provide realistic, hands-on instruction. The device integrates a poseable prepubescent patient model, anatomically accurate silicone inserts of varying skin tones, and a preliminary embedded sensor system to characterize labial traction force and evaluate the feasibility of real-time feedback. Initial validation testing with certified Pediatric Sexual Assault Nurse Examiners (SANE-Ps) supported both anatomical realism and tactile accuracy, demonstrating the potential for biomimetic simulators to improve training quality, expand access to pediatric forensic exam education, and ultimately improve patient outcomes.

Anna Ryder

Anna Ryder
Virginia Tech

Bio: Anna Ryder recently earned a B.S. in Biomedical Engineering from Virginia Tech. Her current work focuses on continuing the development of this project to establish a foundation for future expansion and refinement.

Multimodal Sensing for Robot-Assisted Sub-Tissue Feature Detection in Physiotherapy Palpation

Development and Preliminary Benchtop Evaluation of a Novel Athermal Septostomy Catheter

Force and Stress Analysis of a Redesigned Minimally Invasive Stapler- Cutter Instrument

Colorectal cancer was the third leading cause of cancer-related deaths in the United States in 2025. Minimally invasive (laparoscopic) colorectal resection is a common treatment in which the cancerous segment of the colon or rectum is removed using a surgical stapler that simultaneously cuts and seals the tissue with rows of staples. Perpendicular placement of the stapler across the intestine (90°) is ideal, as it minimizes cut length, increases the likelihood of complete transection with a single firing, and simplifies the subsequent anastomosis. However, existing surgical staplers have limited articulation ranges, which makes this orientation difficult to achieve. 

To address this limitation, a cable-pulley articulation system was developed to enable full 90° articulation. The handle was also redesigned to improve trigger ergonomics and reduce the risk of operator error. This talk presents the force and stress analysis of the stapler jaw and redesigned handle to evaluate structural integrity, demonstrate material feasibility, and prepare for fabrication and experimental testing.

Peyton Kullmann

Peyton Kullmann, PhD Student
University of Nebraska-Lincoln

Peyton Kullmann is a Ph.D. student in Mechanical Engineering & Applied Mechanics at the University of Nebraska-Lincoln. She is an NSF Graduate Research Fellow and works in Dr. Carl Nelson’s Applied Mechanisms and Design Lab. She received her B.S. in Mechanical Engineering from Nebraska, where she worked on the redesign of a surgical stapler used for minimally invasive colorectal resection. While continuing work on surgical device innovation, her current research also explores passive mechanisms and soft robotic systems for pediatric rehabilitation, aimed at restoring mobility and independence in children with motor impairments. 

Development And Validation of a Temporary Gastroretentive Device

Development of a Standardized Mechanical Design and Testing Procedure for 3D-Printed, Patient-Specific Prosthetic Sockets

A Rapid Prototyping Tool for Air-Bladder Wheelchair Cushions Using Image-Driven Modeling