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Enterprise AI Analysis: Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection

Enterprise AI Analysis

Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection

Authored by Suhaylah Alkhalefah, Isra AlTuraiki, and Najwa Altwaijry. Published in Healthcare 2025, 13, 648 on 16 March 2025.

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This research highlights the transformative potential of AI in optimizing healthcare operations, offering significant improvements in diagnostic accuracy and patient outcomes for diabetic foot ulcer management. Our analysis quantifies the immediate benefits for enterprise integration.

0 Total Studies Analyzed
0 AI Models Identified
0 Generative AI Techniques Applied

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

95.8% Average DFU Classification Accuracy Achieved by AI Models

Enterprise Process Flow

Data Collection & Augmentation
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Feature Extraction (CNNs/SNNs)
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Model Training (Transfer Learning)
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Classification (Infection/Ischemia)
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Performance Evaluation

AI Model Performance for DFU Classification (Selected Examples)

Model Key Features Best Performance Metric
DFU_QUTNet
  • Custom CNN, wide network for better gradient propagation.
 F1-Score: 94.5%
ResNet50
  • Deep architecture with skip connections, transfer learning.
 Ischemia Accuracy: 99.49%
EfficientNet
  • Compound scaling for optimal width, depth, resolution.
 Infection Accuracy: 97%
VGG-19 with UNet++
  • Pre-trained VGG-19 for features, UNet++ for segmentation.
 Accuracy: 99.05%

Real-time DFU Classification with Hardware Acceleration

Fadhel et al. [39] demonstrated real-time classification of DFUs using DFU_FNet and DFU_TFNet models. Implementing these on FPGAs and GPUs, they achieved high accuracy with DFU_TFNet outperforming AlexNet, VGG16, and GoogleNet. The FPGA platform, while slightly slower, offered significantly lower power consumption, ideal for portable diagnostic applications.

Outcome: DFU_TFNet achieved 99.81% accuracy, showcasing potential for efficient mobile diagnosis.

92.4% Predictive Accuracy for DFU Amputation/Mortality Risk

Enterprise Process Flow

Clinical Data Collection (EMR)
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Feature Engineering & Selection
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ML Model Training (LR/RF/ANN/SVM)
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Outcome Prediction (Amputation/Healing)
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Model Validation & Interpretability

Machine Learning Models for DFU Prediction

Model Predictive Task Key Advantages
Random Forest (RF) DFU & Amputation Occurrence
  • Outperformed Logistic Regression, handles complex interactions.
Extreme Learning Machine (ELM) Foot Ulcer Presence
  • Single hidden layer, fast classification, high accuracy (96.15%).
Artificial Neural Network (ANN) DFU Prognosis (Wagner scores)
  • Superior performance over LR, captures non-linear data interactions.
Support Vector Machine (SVM) Amputation Risk
  • Effective with retrospective datasets, good for binary classification.

Predicting DFU Recurrence in Elderly Patients

Hon et al. [17] utilized ML techniques to predict recurrence risk in elderly diabetic patients. They analyzed various risk factors and compared SVM, XGBoost, KNN, RF, and DT. SVM achieved the highest accuracy after robust data preprocessing and feature integration.

Outcome: SVM achieved 93% accuracy, demonstrating effective prevention strategies.

95.35% Average Dice Score for DFU Segmentation

Enterprise Process Flow

Multimodal Image Acquisition (RGB/Thermal)
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Data Augmentation & Preprocessing
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Encoder-Decoder Network (UNet/LinkNet)
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Pixel-level Segmentation
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Morphological Feature Extraction

Deep Learning Architectures for DFU Segmentation

Architecture Purpose Advantages
UNet with Depth (UPD) DFU monitoring (infrared/depth)
  • Reliable performance across varied conditions, preferred for practical use.
DE-ResUNet Segment diabetic foot thermal images
  • Combines RGB/thermal data, superior segmentation accuracy.
FUSegNet Integrates global & local features for diagnosis
  • Distinguishes DFUs from other chronic wounds, high accuracy.
Attention UNet DFU segmentation with self-training
  • Improved dice scores on multiple datasets, robust performance.

Automated Foot Ulcer Segmentation for Wound Management

Mahbod et al. [20] proposed an ensemble of UNet and LinkNet architectures with pre-trained EfficientNet backbones for automated segmentation of foot ulcers. They achieved first place in the FUSeg challenge, demonstrating highly accurate extraction of morphological features from foot wounds.

Outcome: Achieved a Dice Score of 88.80% (UNet and LinkNet), proving effective for wound analysis.

99.0% DFU Detection F1 Score (Best Performing Model)

Enterprise Process Flow

Image Acquisition (Thermogram/RGB)
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Preprocessing & Feature Optimization
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Object Detection Models (YOLO/R-CNN)
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Localization & Classification
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Explainable AI Integration

AI Models for DFU Detection Performance

Model Key Technique Achieved Performance
AdaBoost Optimized feature selection F1 Score: 97.75%
EfficientNet Compound scaling, transfer learning F1 Score: 99.0%
YOLOv8m & Faster R-CNN Weighted bounding box fusion mAP@0.5: 0.864
FusionNet Multi-scale feature fusion, XAI integration F1 Score: 99.08%

Multi-stage DFU Detection Using Deep Learning

Giridhar et al. [23] presented a deep learning approach for detecting DFUs using DenseNet121. The model, pre-trained on ImageNet, demonstrated high speed and precision in classifying ischemia, infection, and none categories, leveraging dense blocks for information preservation.

Outcome: Achieved 98% accuracy, with 100% F1 score for 'none' class and 97% for infection/ischemia.

70% Synthetic DFU Image Generation Success Rate

Enterprise Process Flow

Limited Real-world Data
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Generative Model Training (GANs/Diffusion)
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Synthetic Data Generation (Images/EMR)
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Clinician Validation & Refinement
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Enhanced Model Training & Robustness

Generative AI Techniques in DFU Management

Technique Application Key Benefit
Diffusion Models Synthetic DFU image generation
  • Addresses data scarcity for image-based AI models.
Conditional GAN (AFSegGAN) Automatic foot ulcer segmentation
  • Generates segmented wound masks and calculates metrics.
EMR-TCWGAN Synthesizing realistic EMR data (temporal patterns)
  • Overcomes limited access to electronic medical records for predictive models.
Hybrid ResNet50-GAN Enhance DFU diagnosis accuracy
  • Improves diagnostic precision by augmenting limited medical image data.

Addressing Medical Data Scarcity with Synthetic Data

Hyun et al. [66] developed a synthetic data generation system using NeuralProphet to create realistic transcutaneous oxygen pressure and foot temperature data, categorized into severity levels. This system successfully generates diverse datasets aligned with real-world patterns, validating models and reducing dependence on costly real-world collections.

Outcome: Achieved 100% accuracy in binary classification for synthetic data, demonstrating realistic data generation.

93.9% Average Wound Localization mAP on Mobile

Enterprise Process Flow

Smartphone Image Capture
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Cloud-based AI Processing
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DFU Detection & Localization
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Classification & Measurement
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Remote Monitoring & Clinician Feedback

AI Models for Mobile DFU Management

Application Feature AI Model Used Benefit
Predict Healing Outcomes RF/SVM
  • Integrates clinical and imaging data, AUC up to 0.794.
Wound Localization YOLOv3
  • Achieved 93.9% mAP, enabling remote healthcare access.
Automated Detection Faster R-CNN with Inception-ResNetV2
  • High sensitivity and specificity, excellent inter-rater reliability.
Detection & Monitoring YOLOv5s/Inception-ResNetV2
  • High F1 score for wound localization (0.80) and classification (94.81% ischemic).

Clinical Validation of AI-enabled Wound Imaging App

Chan et al. [73] evaluated CARES4WOUNDS (C4W) for DFU monitoring, measuring wound length, width, and area. The app demonstrated excellent intra-rater reliability (0.933–0.994) across devices and good inter-rater reliability compared to manual measurements, proving its effectiveness for consistent wound monitoring.

Outcome: Proven effective for consistent wound monitoring with high intra-rater reliability (0.933-0.994).

Calculate Your Potential ROI with AI

Estimate the efficiency gains and cost savings for your enterprise by integrating AI solutions based on our analysis.

Estimated Annual Savings
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Your AI Implementation Roadmap

A phased approach to integrating AI solutions for DFU management within your enterprise, ensuring a smooth transition and measurable impact.

Discovery & Strategy

Conduct a thorough assessment of current DFU management workflows, identify pain points, and define specific AI application goals. Develop a tailored strategy aligned with clinical needs and data infrastructure.

Pilot Program & Customization

Implement a small-scale pilot project using selected AI models (e.g., classification/segmentation) on a subset of data. Customize models for specific patient populations and integrate with existing EHR systems.

Training & Integration

Train clinical staff on AI-powered tools and smartphone applications. Fully integrate validated AI solutions into daily clinical practice, ensuring seamless data flow and user adoption.

Monitoring & Optimization

Continuously monitor AI model performance, gather clinician feedback, and iterate for optimization. Expand the use of generative AI for data augmentation to improve model robustness and generalizability.

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