用多模态深度学习预测伤口恶化风险，助力早期干预。
A Deep Multi-Modal Method for Patient Wound Healing Assessment
- 融合伤口图像与临床变量，通过迁移学习联合建模
- 可同时预测伤口特征与愈合轨迹，提升风险预判能力
- 适合医疗AI研究者与临床护理决策支持系统开发者
患者住院是导致伤口护理成本高昂的主要因素。多数患者并不需要立即住院，但由于治疗延迟、患者依从性差或共病等因素，伤口可能恶化并最终导致住院。本文提出一种深度多模态方法，通过综合患者伤口变量与伤口图像，预测其住院风险。现有工作主要聚焦于特定伤口类型的愈合轨迹分析。我们开发了一种基于迁移学习的伤口评估方案，能够从伤口图像中预测伤口变量及其愈合轨迹，这是本文的核心贡献。我们认为，该新型模型有助于早期发现影响愈合过程的复杂情况，并减少临床医生诊断所需时间。
原文摘要 · Abstract (English)
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.
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