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Published on: January 10, 2025
Machine Learning for Wound Management: A Structured Narrative Review of Applications, Challenges, and Prospects.
Itishree Jogamaya Das1, Jitendra Debata2, Kalpita Bhatta3
1Faculty of Medical Science & Research, Sai Nath University, Ranchi, India.
Summary
Machine learning (ML) offers automated wound assessment for better objectivity and efficiency. However, challenges like data limitations and validation need addressing for clinical integration of ML in wound care.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Wound Management Technologies
Background:
- Chronic wounds pose significant clinical and economic challenges.
- Current wound evaluation methods are subjective and time-consuming.
- Automated approaches are needed for improved wound assessment and management.
Purpose of the Study:
- To review machine learning (ML) and deep learning (DL) applications in wound care.
- To identify key ML/DL methods, data modalities, and evaluation strategies.
- To discuss barriers and opportunities for ML adoption in clinical wound management.
Main Methods:
- Structured narrative review of ML and DL in wound care.
- Analysis of commonly used algorithms, data types, and assessment metrics.
- Critical discussion of clinical adoption challenges and future directions.
Main Results:
- ML/DL enables wound detection, segmentation, characterization, infection assessment, and healing prediction.
- Commonly used methods include convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
- Barriers include limited data, annotation issues, poor generalizability, bias, and integration challenges.
Conclusions:
- ML/DL holds significant potential to enhance objectivity, efficiency, and personalization in wound care.
- Overcoming barriers requires standardized datasets, transparent reporting, and rigorous validation.
- Emerging opportunities include multimodal learning, federated learning, XAI, and wearable technologies.
