Diagnosis of Pressure Ulcer Stage Using On-Device AI
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. YOLOv8 Model Description
2.3. Mobile App Design
3. Results
3.1. Pressure Ulcer Detection Performance
3.2. Pressure Ulcer Checker Mobile App
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Reference | Model | Task | Description | Limitation |
---|---|---|---|---|
[24] | DNN | Classification | Monitored six postures of patients using inertial sensors and DNN | Cannot recognize pressure ulcer region and stage |
[30] | Inception-ResNet-v2 | Classified the severity of erythema and necrosis with achieving over 97% classification accuracy | ||
[32] | SE-ResNext101 | Categorized pressure ulcers into seven classes (stage 1–4, DTPI, unstageable, others) with achieving 71.5% F1 score | Cannot estimate pressure ulcer region | |
[25] | CNN | Segmentation | Segmented a pressure ulcer RGB image to slough, granulation, and necrotic eschar tissue regions with achieving overall 92% classification accuracy [25] and 95% AUC [26] | Cannot predict detailed pressure ulcer stage |
[26] | 3D CNN | |||
[27] | Five CNN models | Applied U-Net, DeeplabV3, PsPet, FPN, and Mask R-CNN for tissue segmentation with achieving 99.6% tissue classification | ||
[28] | U-Net | Segmented pressure ulcer wounds with achieving 90% F1 score | ||
[31] | Mask R-CNN, U-Net | Segmented pressure ulcer region and measured its area via a LiDAR camera and the deep learning models with achieving 26.2% mean relative error | ||
[29] | MobileNetV2 | Boundary detection | Detected pressure ulcer regions by displaying bounding boxes over them with achieving 75.9% mAP@50 | |
[33] | YOLOv4 | Boundary detection, classification | Simultaneously predicted pressure ulcer boundaries and classes with achieving 63.2% accuracy [33], 73.2% F1 score [34], and 69.6% F1 score [35], respectively | Relatively low detection performance and portability |
[34] | YOLOv5 | |||
[35] | Faster R-CNN |
Label | Image | Feature |
---|---|---|
Stage 1 |
| |
Stage 2 |
| |
Stage 3 |
| |
Stage 4 |
| |
DTPI |
| |
Unstageable |
|
Model | Depth Multiple (d) | Width Multiple (w) | Ratio (r) | Trainable Parameters |
---|---|---|---|---|
YOLOv8n | 0.33 | 0.25 | 2.0 | 3.2 M |
YOLOv8s | 0.33 | 0.50 | 2.0 | 28.6 M |
YOLOv8m | 0.67 | 0.75 | 1.5 | 78.9 M |
YOLOv8l | 1.00 | 1.00 | 1.0 | 165.2 M |
YOLOv8x | 1.00 | 1.25 | 1.0 | 157.8 M |
Model | Accuracy | Precision | Recall | F1 Score | mAP@50 | mAP@50-95 |
---|---|---|---|---|---|---|
YOLOv8n | 0.810 | 0.910 | 0.870 | 0.889 | 0.898 | 0.674 |
YOLOv8s | 0.814 | 0.922 | 0.845 | 0.882 | 0.891 | 0.670 |
YOLOv8m | 0.846 | 0.897 | 0.891 | 0.894 | 0.908 | 0.685 |
YOLOv8l | 0.789 | 0.895 | 0.861 | 0.878 | 0.892 | 0.671 |
YOLOv8x | 0.796 | 0.905 | 0.859 | 0.881 | 0.900 | 0.680 |
Label | Precision | Recall | F1 Score | mAP@50 | mAP@50-95 |
---|---|---|---|---|---|
Overall | 0.897 | 0.891 | 0.894 | 0.908 | 0.685 |
Stage 1 | 0.959 | 0.857 | 0.905 | 0.900 | 0.630 |
Stage 2 | 0.794 | 0.843 | 0.818 | 0.839 | 0.564 |
Stage 3 | 0.874 | 0.843 | 0.858 | 0.874 | 0.645 |
Stage 4 | 0.938 | 0.957 | 0.947 | 0.975 | 0.922 |
DTPI | 0.905 | 0.926 | 0.915 | 0.936 | 0.664 |
Unstageable | 0.911 | 0.918 | 0.914 | 0.924 | 0.682 |
Operating System | Android 10 or Newer |
Application processor | ARMv8a 64 bit or higher performance |
RAM | 4 GB or more |
Storage | 1 GB or more |
Model | Dataset Size | Classes | Accuracy | Precision | Recall | F1 Score | mAP@50 |
---|---|---|---|---|---|---|---|
Faster R-CNN [35] | 5084 | 6 (stage 1–4, DTPI, unstageable) | N/A | 0.776 | 0.641 | 0.696 | N/A |
YOLOv4 [33] | 1432 | 6 (stage 1–4, unstageable, others) | 0.632 | N/A | N/A | N/A | N/A |
YOLOv5s [34] | 1000+ | 5 (stage 1–4, non-pressure ulcer) | N/A | 0.781 | 0.685 | 0.732 | 0.769 |
YOLOv8m (our study) | 2800 | 6 (stage 1–4, DTPI, unstageable) | 0.846 | 0.897 | 0.891 | 0.894 | 0.908 |
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Share and Cite
Chang, Y.; Kim, J.H.; Shin, H.W.; Ha, C.; Lee, S.Y.; Go, T. Diagnosis of Pressure Ulcer Stage Using On-Device AI. Appl. Sci. 2024, 14, 7124. https://doi.org/10.3390/app14167124
Chang Y, Kim JH, Shin HW, Ha C, Lee SY, Go T. Diagnosis of Pressure Ulcer Stage Using On-Device AI. Applied Sciences. 2024; 14(16):7124. https://doi.org/10.3390/app14167124
Chicago/Turabian StyleChang, Yujee, Jun Hyung Kim, Hyun Woo Shin, Changjin Ha, Seung Yeob Lee, and Taesik Go. 2024. "Diagnosis of Pressure Ulcer Stage Using On-Device AI" Applied Sciences 14, no. 16: 7124. https://doi.org/10.3390/app14167124