# Image Based Artificial Intelligence in Wound Assessment: A Systematic Review

arXiv: https://arxiv.org/abs/2009.07141 · HTML: https://mlsanity.com/paper/2009.07141/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videos](https://mlsanity.com/paper/2407.19652/) (2024)
- [Development of a deep learning-based tool to assist wound classification](https://mlsanity.com/paper/2303.16522/) (2023)
- [Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation using A Novel Dataset](https://mlsanity.com/paper/2502.10652/) (2025)
- [Wound Severity Classification using Deep Neural Network](https://mlsanity.com/paper/2204.07942/) (2022)
- [Advancing Wound Filling Extraction on 3D Faces: Auto-Segmentation and Wound Face Regeneration Approach](https://mlsanity.com/paper/2307.01844/) (2023)
- [Non-Invasive 3D Wound Measurement with RGB-D Imaging](https://mlsanity.com/paper/2601.19014/) (2026)
- [Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification](https://mlsanity.com/paper/2603.27325/) (2026)
- [WoundFormer: Multi-Scale Spatial Feature Fusion for Multi-Class Wound Tissue Segmentation](https://mlsanity.com/paper/2605.19868/) (2026)
- [Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoring](https://mlsanity.com/paper/2607.05625/) (2026)
- [In-Context Learning for Wound Classification with Small Multimodal Language Models](https://mlsanity.com/paper/2607.18819/) (2026)
- [WILLIE: A Unified Framework and Benchmark for Wound Classification, Segmentation, and Localization](https://mlsanity.com/paper/2610.05341/) (2026)

## Abstract (arXiv)

Efficient and effective assessment of acute and chronic wounds can help wound care teams in clinical practice to greatly improve wound diagnosis, optimize treatment plans, ease the workload and achieve health related quality of life to the patient population. While artificial intelligence (AI) has found wide applications in health-related sciences and technology, AI-based systems remain to be developed clinically and computationally for high-quality wound care. To this end, we have carried out a systematic review of intelligent image-based data analysis and system developments for wound assessment. Specifically, we provide an extensive review of research methods on wound measurement (segmentation) and wound diagnosis (classification). We also reviewed recent work on wound assessment systems (including hardware, software, and mobile apps). More than 250 articles were retrieved from various publication databases and online resources, and 115 of them were carefully selected to cover the breadth and depth of most recent and relevant work to convey the current review to its fulfillment.