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Unimodal and Multimodal Deep Learning for Pressure Injury Identification: Scoping Review
Yingxue Sun1, Congcong Liu1, Ji-Cheng Zhang1
1Department of Critical Care Medicine (Ward 2), Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu-Weiqu Road, Huaiyin District, Jinan, Shandong Province, 250021, China, 86 15863545853.
JMIR Medical Informatics
|August 10, 2026
Summary
Deep learning shows promise for pressure injury (PI) assessment, but current methods heavily favor unimodal imaging. Future research must focus on multimodal integration for reliable clinical application.
Area of Science:
- Medical technology
- Artificial intelligence in healthcare
- Biomedical imaging analysis
Background:
- Pressure injuries (PI) are a significant global health concern, potentially leading to severe infections or death.
- Accurate staging of pressure injuries is crucial for effective treatment and patient outcomes.
- Deep learning (DL) offers a streamlined approach to PI assessment, improving efficiency and accuracy.
Conclusions:
- The current field of deep learning for PI identification is dominated by unimodal imaging, creating a gap for clinical translation.
- While unimodal models perform well in controlled environments, achieving clinical-grade reliability requires a shift towards multimodal integration.
- Future research should prioritize developing and validating multimodal deep learning approaches for pressure injury assessment.