# Diagnosis of Pressure Ulcer Stage Using On-Device AI
### YOLOv8 Model Description  
YOLO was first introduced by [PERSON] et al. [38]. The model is the first one to use a one-step object detection approach that simultaneously detects bounding boxes and object classes. Compared to the conventional two-step object detectors including R-CNN, YOLO models have shown excellent performance with short object detection time [36]. YOLOv8 is the latest version of YOLO by Ultralyttics released in 2023 [39; 40]. Compared to its previous versions, it has the most enhanced object detection performance [36]. In particular, the model is an anchor-free approach to directly predict the center point and size of the bounding box containing the object. This method reduces the complexity of the model by eliminating the process to manually define the size and number of anchor boxes (Figure 1). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Image** & **Feature** \\ \hline Stage 1 & - & Local non-chronic erythema \\ Stage 2 & - & No skin damage \\ \hline Stage 2 & - & Thin, open, pink wound \\ Stage 3 & - & Not only the epidermis but also part of the dermis is damaged \\ Stage 4 & - & Occurrence of intact or damaged serous blisters \\ \hline Stage 3 & - & The epidermis, dermis, and subcutaneous tissue are damaged \\ Stage 4 & - & Can measure the depth of tissue damage \\ Stage 4 & - & Can involve tunneling or encroachment \\ \hline Stage 4 & - & Full-thickness skin damage with exposure of bones, ligaments, and muscles \\ Stage 4 & - & Partial growth of floating tissue or dry crust at the base of the wound \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline
**DTPI** & - & Partial skin discoloration to purple or reddish brown without skin damage \\ Stage 4 & - & Blood-filled blisters or deep bruises \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline
**Unstageable** & - & Full-thickness skin damage or wound bed covered with necrotic tissue \\ Stage 4 & - & The extent of tissue damage is unknown \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features of each pressure ulcer label with representative images. The YOLOv8 structure contains feature pyramid network (FPN) just as YOLOv5. FPN demonstrates a bottom-up structure with lateral connections, effectively detecting objects in multiple scales. However, there are a few differences between two models. The kernel size of the first convolution layer in the backbone and neck has been changed to 3 \(\times\) 3 (k = 3, Figure 1). The C3 module in YOLOv5 was replaced with the C2f module in YOLOv8 (Figure 1). The C2f module in YOLOv8 reduces the number of blocks in the initial stage of the backbone network and the number of output channels in the final stage to form a lighter model with high accuracy. In the head part of YOLOv8, the object-ness branch in YOLOv5 was removed and separate heads were used in YOLOv8 to individually process classification and regression tasks (Figure 1). These split heads allow each branch to focus on its own unique task, enhancing overall object detection accuracy. Overall, advanced backbone, neck, and head architectures result in enhancing feature extraction and object detection accuracy. There are five YOLOv8 models (e.g., 8n, 8s, 8m, 8l, and 8x) according to model size (depth (\(d\)), width (\(w\)), ratio (\(r\))) and the number of trainable parameters (Figure 1 and Table 3). YOLOv8 models were trained on a desktop computer (NVIDIA GeForce RTX 3090 Ti GPU, AMD Ryzen 5950X CPU, 128 GB RAM) with Python 3.10.9 and PyTorch version 1.12.1 + cu113. Hyperparameters used in the study are as follows: learning rate of 0.01, batch size  
Figure 1: YOLOv8 model architecture. \(d\), \(w\), and \(r\) indicate the depth multiple, width multiple, and ratio of each module, respectively. \(k\), \(s\), and \(p\) denote kernel size, stride, and padding number, respectively.