Evaluation of the Effectiveness of Deep Learning Model in Detection and Classification of Pressure Injury

Today, with the increasing use of technology, it is seen that larger data is needed to solve complex problems. In order to meet this need, Convolutional Neural Networks have emerged, which are used in many areas such as object recognition, speech recognition, and natural language processing, and can automatically learn from the symbols of data belonging to images, videos, audio, and texts, instead of learning with coded rules, unlike traditional machine learning methods, based on Artificial Neural Networks. Convolutional Neural Networks are one of the Deep Learning methods, which is a sub-branch of machine learning methods and has the ability to learn from examples. Convolutional Neural Networks are methods that can also learn from raw image or text data and whose prediction accuracy increases according to the size of the data. It has been proven in the literature that artificial intelligence and deep learning models are effective in the risk analysis of pressure injuries. However , no study has been found on the classification of pressure injuries. Raju , Su, Patrician et al. (2015), provided more accurate and faster prediction of Braden risk scale scores with the deep learning model they developed as a result of a four-year follow-up in a military hospital . Alderden , Pepper , Wilson et al. (2018) developed a deep learning model that reveals the risk analysis of pressure injuries in intensive care patients, and provided more accurate and meaningful pressure injury risk analysis with more sensitive measurements for intensive care patients who are considered high risk according to risk assessment tools. Demircan, Yücedağ , Toz et al. (2016) developed a mathematical model that analyzes the risk factors in the formation process of pressure injuries, and ensured that pressure injuries were detected at an early stage. The use of these innovative applications, which are included and used in the world literature , is limited in our country. In an environment where technology is rapidly developing and consumed, not remaining indifferent to innovative initiatives and integrating technology into nursing practices will increase the visibility of our profession by training innovative nurses. In light of this information, the study was conducted to develop a deep learning model in the detection and classification of pressure injuries and to determine the effect of the model on the knowledge and satisfaction levels of nurses.

Aim A randomized controlled experimental study was conducted to develop a deep learning model for the detection and classification of pressure injuries and to determine the effect of the model on the knowledge and satisfaction levels of nurses .

Hypotheses of the Research; H1: Deep Learning Model Provides Detection of Pressure Sores.

H2: Deep Learning Model Provides Classification of Pressure Sores.

H3: Mobile Application Developed with Deep Learning Model Plays an Active Role in Pressure Sore Treatment and Care.