[Paper Review] Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers

Paul Fergus, Carl Chalmers+1 more|arXiv (Cornell University)|Aug 17, 2018

Pressure Ulcer Prevention and Management18 references3 citations

TL;DR

This paper presents an automated web-based platform using convolutional neural networks and transfer learning to detect, assess, and support management of pressure ulcers. It enables nursing professionals, patients, and caregivers to analyze skin damage through image-based assessment, with future potential for decision support across clinical and personal care boundaries.

ABSTRACT

This paper describes the Pressure Ulcers Online Website, which is a first step solution towards a new and innovative platform for helping people to detect, understand and manage pressure ulcers. It outlines the reasons why the project has been developed and provides a central point of contact for pressure ulcer analysis and ongoing research. Using state-of-the-art technologies in convolutional neural networks and transfer learning along with end-to-end web technologies, this platform allows pressure ulcers to be analysed and findings to be reported. As the system evolves through collaborative partnerships, future versions will provide decision support functions to describe the complex characteristics of pressure ulcers along with information on wound care across multiple user boundaries. This project is therefore intended to raise awareness and support for people suffering with or providing care for pressure ulcers.

Motivation & Objective

• To develop a collaborative digital platform for early detection and management of pressure ulcers across diverse user groups.
• To address the clinical challenge of delayed pressure ulcer diagnosis through accessible, real-time skin assessment technology.
• To integrate state-of-the-art machine learning with end-to-end web technologies for scalable clinical use.
• To create a centralized research and clinical resource for pressure ulcer analysis and ongoing innovation.
• To support shared decision-making by enabling patients and caregivers to participate in wound monitoring and care planning.

Proposed method

• Leverages convolutional neural networks (CNNs) and transfer learning to analyze skin images for pressure ulcer detection.
• Employs end-to-end web technologies to deliver a scalable, accessible platform for diverse users including patients and carers.
• Uses image-based input from users to automatically assess skin damage and ulcer characteristics.
• Designs the system with extensibility in mind to support future integration of clinical decision support features.
• Establishes a centralized website as a hub for research collaboration and data sharing on pressure ulcer management.
• Applies transfer learning techniques to adapt pre-trained models to the specific task of pressure ulcer classification and analysis.

Experimental results

Research questions

• RQ1 How can an automated, web-based system improve early detection of pressure ulcers across diverse user groups?
• RQ2 What role can transfer learning and CNNs play in enabling accurate, low-resource skin damage assessment?
• RQ3 How can digital platforms foster collaboration between nursing professionals, patients, and caregivers in pressure ulcer management?
• RQ4 What are the technical and clinical requirements for building a scalable, multi-user pressure ulcer assessment tool?
• RQ5 How can such a system support ongoing research and clinical decision-making in wound care?

Key findings

• The platform successfully demonstrates the feasibility of using deep learning for automated pressure ulcer image analysis in a real-world, collaborative context.
• The integration of transfer learning enables effective model performance with limited labeled training data for pressure ulcer detection.
• The system provides a centralized, accessible interface that supports both clinical professionals and non-clinical users in wound assessment.
• The platform is designed to evolve with future enhancements, including decision support and multi-user collaboration features.
• The project establishes a foundation for ongoing research and data sharing in pressure ulcer prevention and management.
• The use of end-to-end web technologies ensures broad accessibility and scalability across diverse care environments.