Enterprise AI Analysis
Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review
Over recent years, venous ulcer wound care has experienced significant advancements through the application of machine learning (ML) models. The aim of the present study is a systematic, comprehensive analysis of prior research studies in this field covering the period between 2001 and August 2025. By searching multiple academic databases, including the Web of Science, Scopus, and PubMed, using relevant keywords and different queries, and screening reference lists of previously published manuscripts and review papers with a focus on the application of artificial intelligence in dermatology and medicine, an initial set of potential studies for review was obtained. To ensure the scope and relevance of the review, several inclusion and exclusion criteria were used to derive the final set of relevant research studies upon which a database for research data management was created. As a result, a total of 79 relevant research studies were comprehensively analysed, upon which detailed meta-analysis and analysis of application areas of ML models within venous ulcer wound care were conducted. Afterwards, a summary of benefits for medical systems and patients was given along with a general discussion regarding ML model limitations, trends, and opportunities, as well as research studies' limitations and possible future research directions. The presented analyses may be valuable for researchers interested in applying ML models not only to venous ulcer wound care but also to other types of chronic wound care.
Executive Impact & ROI Overview
Machine Learning is transforming wound care. This analysis highlights key metrics demonstrating the current state and potential for AI-driven advancements in Venous Ulcer assessment and treatment.
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Research Studies Analyzed
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Key Application Areas
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Years Covered (2001-2025)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Research Growth & Methodology
The field of Machine Learning in venous ulcer wound care has seen significant growth, particularly in the last decade (2015-2025), averaging six publications annually. This growth reflects the increasing awareness of ML's potential to address complex challenges in wound care. Systematic review methodologies ensure comprehensive analysis, including rigorous criteria for study selection and data management.
Data-Based ML Model Analysis
Data-based ML models utilize numerical and categorical data for tasks like wound healing prediction and risk assessment. Common models include Logistic Regression, Classification Trees, Random Forests, and Gradient-Boosted Decision Trees. While less prevalent than image-based models recently, they remain crucial for specific analytical tasks, often leveraging diverse data sources such as electronic medical records.
Image-Based ML Model Analysis
Image-based ML models, predominantly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), are increasingly applied for wound localization, segmentation, tissue detection, and classification. These models process wound images to automate assessment, reduce variability, and support personalized treatment planning. Data augmentation and hyperparameter tuning are critical for their development.
Key Application Areas
Six main application areas for ML models in VU wound care have been identified: wound localization, measurement, assessment, and documentation; wound tissue detection, characterization, and classification; wound type classification; wound healing prediction, risk assessment, and decision-making; content-based image retrieval (CBIR); and versatile applications combining multiple tasks into unified frameworks.
Benefits for Patients & Healthcare Systems
ML applications offer substantial benefits, including improved assessment accuracy, time/cost efficiency, enhanced decision support for medical staff, and remote care capabilities. For patients, this translates to better access to care, earlier detection, personalized treatment, and reduced healthcare costs. ML contributes to standardized documentation and monitoring, reducing human subjectivity.
Limitations & Future Directions
Current limitations include small, imbalanced datasets, lack of standardization in image acquisition, and challenges in ML model interpretability and integration with existing healthcare systems. Future research should focus on larger, more diverse datasets, multimodal approaches, clinical integration, rigorous validation, and the development of explainable AI models for broader acceptance.
Enterprise Process Flow: Literature Review Methodology
Keywords, Logical operators, Queries, Academic databases, Reference list screening
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Duplicate removal, Inclusion and exclusion criteria match
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Final set of research studies, Excel database for research data management
The review followed a systematic methodology, starting with keyword-based searches across multiple databases, followed by duplicate removal, application of inclusion/exclusion criteria, and finally compilation into an Excel database for analysis.
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Average Annual Publications (2015-2025)
Over the last decade (2015-2025), an average of six research studies focusing on ML models for VU wound care were published annually, showing a strong growth trend in the field.
| Feature | Data-Based ML Models | Image-Based ML Models |
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| Primary Application Scope |
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| Recent Trend |
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While data-based ML models are still used for specific applications like healing prediction, image-based ML models, leveraging deep learning architectures, have seen a significant increase in application over the last decade, accounting for 87% of reviewed studies.
Leading Data-Based ML Models for VU Wound Care
The meta-analysis of data-based ML models reveals that Logistic Regression (LR), Classification Tree (CT), Random Forest (RF), and Gradient-Boosted Decision Tree (GBDT) are the most frequently applied models. These supervised learning techniques are primarily used for tasks such as wound healing prediction and risk assessment due to their interpretability and efficiency with structured data.
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Reduction in Inter-Observer Variability
ML models significantly reduce inter-observer variability in wound assessment, leading to more consistent and objective diagnoses and treatment plans.
Enterprise Process Flow: Future Research Directions
Collect more comprehensive & diverse wound datasets
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Focus on clinical applications & integration with workflow
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Conduct more comprehensive clinical validation
Future research should prioritize collecting larger, more diverse datasets, integrating ML models into clinical workflows, and conducting rigorous clinical validation to enhance robustness and applicability.
Calculate Your Potential AI-Driven ROI
Estimate the significant operational efficiencies and cost savings your organization could achieve by implementing AI solutions in wound care.
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Your AI Implementation Roadmap
A strategic approach is crucial for successful AI integration. Here’s a typical phased roadmap designed for enterprise adoption in wound care.
Phase 1: Data Acquisition & Curation
Secure diverse datasets, establish robust data pipelines, and ensure data quality with clinical annotations for optimal model training.
Phase 2: Model Development & Customization
Train and fine-tune ML models using advanced architectures, focusing on interpretability and explainability to meet specific clinical needs.
Phase 3: Integration & Validation
Seamlessly integrate AI solutions into existing healthcare workflows and conduct rigorous prospective clinical trials to validate performance in real-world settings.
Phase 4: Deployment & Monitoring
Roll out the validated system, establish continuous monitoring protocols, and implement feedback loops for ongoing optimization and sustained impact.
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