# Diagnosis of Pressure Ulcer Stage Using On-Device AI
## 3 Results
### Pressure Ulcer Checker Mobile App  
Based on the trained YOLOv8m model, the 'Pressure Ulcer Checker' mobile app was developed. The main components of this mobile app are registration, home, detection results, and instruction pages (Figure 5).  
When a user runs the mobile app for the first time, the log-in page appears for user identification (Figure 5a). If the user has not registered yet, the user can sign up by pressing the 'Join the membership' button on the log-in page. After successfully logging in, the user encounters the home page (Figure 5b). There is a red and a blue button (Figure 5b). If the user presses the red button (camera), the rear camera of the mobile device is launched and the user can take a photo of the pressure ulcer directly applying this application. If the user presses the blue button (gallery), the mobile app allows the user to access the photo-gallery of the user's mobile device and to select the photo wanted for inspection. Furthermore, the user enables to check inspection history including the pressure ulcer photo, inspection date, and severity on the 'Latest Results' section in the home page (Figure 5b). After the user presses either the red or blue button, the inspection is conducted by the trained YOLOv8m model using the photo provided by the user. After finishing inspection, the diagnostic results of pressure ulcers are displayed on the screen (Figure 5c). The results include the bounding boxes and the classes with probability of each pressure ulcer. The detection results acquired from the mobile app (Figure 5c) are the same as those acquired from the desktop computer (Figure 4e). When the user chooses the 'next' button in Figure 5c, the mobile app offers the instructions on how to prevent pressure ulcers and information of the stage based on the inspection result (Figure 5d). The instructions and information about pressure ulcers were informed by previous studies [1; 2; 30].  
Figure 4: Representative pressure ulcer detection results by the trained YOLOv8m model. (**a–c**) Single stage detection in each image. (**d–f**) Multiple stage detections in each image.  
The 'Pressure Ulcer Checker' mobile app was tested using several Android phones such as the Samsung Galaxy S23 (Qualcomm Snapdragon 8 Gen 2, 8 GB RAM, 256 GB Storage), S22 (Qualcomm Snapdragon 8 Gen 1, 8 GB RAM, 256 GB Storage), and S21 (Exynos 2100, 8 GB RAM, 256 GB Storage). When one image was inspected, it took approximately 2.82 \(\pm\) 0.13 s, 3.16 \(\pm\) 0.21 s, and 3.18 \(\pm\) 0.17 s using the S23, S22, and S21, respectively. In addition, we derived the requirements for properly running the developed mobile app (Table 6). First, the operating system (OS) should be Android 10 or newer for smooth operations of various systems and third-party applications running together. Second, the minimum requirement of the application processor (AP) is selected for ARMv8a 64 bit, which is an AP that supports 64 bit, following the application development guidelines provided by Google Play. Third, 4 GB RAM is proposed as the minimum requirement. The RAM resource was monitored by using the Profiler tool in Android Studio. According to the observation, 4 GB RAM was necessary to run the OS, fundamental applications, trained YOLOv8m model, and deep learning framework, comfortably. Finally, storage of 1 GB or more is required for installing the mobile app (241 MB), storing enough photos of pressure ulcers, and saving diagnostic result log files.