## 4 Discussion  
To the best of our knowledge, this study is the first to report on the development of a deep learning-based classification system that can detect change in the deep tissue in pressure injuries and distinguish between the types of US findings. Although real-time burn classification using ultrasound images in ex vivo porcine skin tissue has been presented previously [28], a real-time approach for the classification of ultrasound images on actual human wound sites has not been developed to date. Moreover, to date, the classification of US images of pressure injury depends on the operator's skill in image interpretation, and US for pressure injury is a procedure that can only be performed by a limited number of highly trained medical professionals. However, in the future, this technology could allow non-specialist medical professionals to easily determine deep tissue conditions. Our results show an overall high detection performance and overall high values of the IoU and DICE score, indicating that the classification of US images of pressure injuries by deep learning may be applicable in clinical practice. Conversely, the values of the IoU of the cobblestone- and cloud-like patterns were slightly lower compared to those in previous studies [29, 30]. One of the reasons for this is that these two patterns are similar, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**US Findings** & **Detection** & **Mean Value** & **Mean Value of** & **Number of Cases** & **Number of Images in** \\  & **Performance** & **of IoU** & **DICE Score** & **in Test Data** & **Test Data** \\ \hline Unclear layer structure & 100.0\% & 0.80 & 0.89 & 2 & 37 \\ Cobblestone-like pattern & 85.7\% & 0.56 & 0.71 & 1 & 7 \\ Cloud-like pattern & 71.4\% & 0.38 & 0.51 & 1 & 14 \\ Anechoic pattern & 93.3\% & 0.62 & 0.76 & 1 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the test for each ultrasonographic finding. Figure 4: Examples of failed results of automatic classification based on deep learning. (**a**) Cobblestone-like pattern. (**b**) Cloud-like pattern. (**c**) Anechoic pattern. The “ground-truth” images show the regions that were manually annotated by the expert. The “deep-learning” images show the regions extracted as a result of automatic classification. White indicates an unclear layer structure, yellow indicates a cobblestone-like pattern image, red indicates a cloud-like pattern image, and purple indicates an an anechoic pattern image. The values of the intersection over union (IoU) are, from left to right, 0.27, 0.42, and 0.27.  
and as shown in Figure 4, they are often misjudged. Moreover, hyperechoic findings of the bone were sometimes incorrectly detected as a cloud-like pattern, as shown in Figure 4. Clinically, high detection performance is a higher priority than the IoU in determining findings of DTPI, meaning low values of the IoU may be ignored. However, care should be taken because misjudgment of the cobblestone- and cloud-like patterns may lead to the selection of completely different treatment methods [17]. To improve the accuracy, more data should be collected and validated in the future. This technology has the potential to enable point-of-care US (POCUS) for the treatment of pressure injuries at the bedside. Although the application is currently available on a desktop computer, it is expected that a handheld US device with AI-assisted functions will be developed in the near future. Since present and previous studies have used relatively high-quality equipment [17], image quality will be the key to making it possible in a handheld US device. This study has several limitations. The first is the small number of subjects. It should be validated in more subjects in the future, especially with additional US images of the cobblestone- and cloud-like patterns. Conversely, further validation should be conducted with handheld US devices for POCUS purposes in the future. Second, this is limited to data obtained from thin older adult patients in Japan.