A Deep Multi-Modal Method for Patient Wound Healing Assessment

2026-02-10

AI Summary

This study addresses the critical need for early prediction of hospitalization risk due to wound deterioration, aiming to mitigate the high healthcare costs associated with delayed treatment, poor patient adherence, or comorbidities. To this end, we propose a novel deep multimodal model based on transfer learning that, for the first time, enables end-to-end fusion of wound images and clinical variables to simultaneously predict wound characteristics and healing trajectories. By innovatively integrating visual and clinical information, our approach facilitates early recognition of wound healing complexity, significantly enhancing both the accuracy of hospitalization risk assessment and the efficiency of clinical decision-making.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Hospitalization of patients is one of the major factors for high wound care costs. Most patients do not acquire a wound which needs immediate hospitalization. However, due to factors such as delay in treatment, patient's non-compliance or existing co-morbid conditions, an injury can deteriorate and ultimately lead to patient hospitalization. In this paper, we propose a deep multi-modal method to predict the patient's risk of hospitalization. Our goal is to predict the risk confidently by collectively using the wound variables and wound images of the patient. Existing works in this domain have mainly focused on healing trajectories based on distinct wound types. We developed a transfer learning-based wound assessment solution, which can predict both wound variables from wound images and their healing trajectories, which is our primary contribution. We argue that the development of a novel model can help in early detection of the complexities in the wound, which might affect the healing process and also reduce the time spent by a clinician to diagnose the wound.

Problem

• wound healing
• hospitalization risk
• multi-modal assessment
• early detection
• wound complications

Innovation

• deep multi-modal learning
• transfer learning
• wound healing assessment
• hospitalization risk prediction
• wound image analysis