# Diagnosis of Pressure Ulcer Stage Using On-Device AI
## 5 Conclusions  
We proposed the on-device AI mobile app to automatically detect and classify pressure ulcers. To establish the object detection model, pressure ulcer images were gathered from diverse open sources, augmented, and annotated by hand. A total of 2800 pressure ulcer images were used to train, validate, and test five YOLOv8 models (8n, 8s, 8m, 8l, and 8x). To compare their pressure ulcer detection performance, five metrics such as accuracy, precision, recall, F1 score, and mAP were assessed. As a result, the YOLOv8m model showed the highest detection performance on pressure ulcers with 0.846, 0.897, 0.891, 0.894, and 0.908 of accuracy, precision, recall, F1 score, and mAP@50, respectively. The trained YOLOv8m model also outperformed past studies. In addition, this YOLOv8m model was implemented into the Android app. This app was developed utilizing Android Studio and the Kotlin programming language. The mobile app was tested under several Android phones (Samsung Galaxy S21, S22, and S23). Consequently, the on-device AI mobile app is able to precisely determine the pressure ulcer areas and stages with taking about 3 s. The proposed system would be useful for early diagnosis of and recovery from pressure ulcers. Y.C. : investigation, methodology, software, writing--original draft, writing--review and editing; J.H.K. : data curation, investigation, methodology, visualization, writing--original draft, writing--review and editing; H.W.S. : data curation, investigation, methodology, validation, writing--original draft, writing--review and editing; C.H. : software, validation, visualization; S.Y.L. : funding acquisition, methodology, project administration, validation, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Dataset Size** & **Classes** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **mAP@50** \\ \hline Faster & 5084 & 6 (stage 1–4, DTPI, ustageable) & N/A & 0.776 & 0.641 & 0.696 & N/A \\ YOLOv4 [33] & 1432 & 6 (stage 1–4, ustageable, 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 & 2800 & 6 (stage 1–4, DTPI, ustageable) & 0.846 & 0.897 & 0.891 & 0.894 & 0.908 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the detection performance between our study and recent studies predicting both the area and type of pressure ulcers at the same time. writing--review and editing; T.G. : conceptualization, funding acquisition, resources, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research was supported by the Bio&Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. RS-2023-00236157). Ethical review and approval were waived for this study due to the usage of a public open dataset. * _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Artificial intelligence in wound care: Diagnosis, assessment and treatment of hard-to-head wounds: A narrative review. _J. Wound Care_**2024**, _33_, 229-242. [CrossRef] [PubMed]
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