Deep Learning Model Detecting Pressure Injury

Summary

In the health care system, pressure injuries, which are among the quality indicators, are a serious patient safety problem that affects the length of hospital stay and the cost of care. Pressure injuries are generally defined as localized injuries caused by pressure on bony prominences or by shear force combined with pressure. This health problem reduces the quality of life of the patient and their family, causes the individual to be socially isolated , requires more intensive and prolonged nursing care, and can cause mortality , morbidity and nosocomial infections if appropriate treatment and care are not provided .

systematic staging of pressure injuries positively directs the treatment process and the patient's prognosis . Correct staging of pressure injuries not only affects patient care outcomes but also increases the quality of nursing care provided by providing a common language among nurses.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. 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.

Conditions

• Knowledge
• Satisfaction

Interventions

OTHER

Control Group (Standard Procedure)

Application in Control Group: After the theoretical lesson, the nurses in the control group determined and classified the pressure injuries in their patients using the " Braden Risk Assessment Scale", which has been accepted as valid and reliable. The nurses in the control group, who determined the pressure injuries using the scale, were given training on the determination and classification of pressure injuries using written material, the content of which was prepared by the researchers. After the training, the "Satisfaction with the Training Method Survey" was applied to the nurses. One week after the training, the nurses were given the "Post-Test ( Modified "Pieper Pressure Sore Knowledge Test)" was applied. After the completion of the application, volunteer nurses from the control group were subjected to pressure injury detection and classification with the deep learning model and trained with the mobile application.

OTHER

Experimental Group

Application in the Experimental Group: After the theoretical course, the nurses in the experimental group detected and classified pressure injuries in their patients with the "Deep Learning Model". In the experimental group, a mobile application developed by the researchers was installed on the phones of the nurses who detected pressure injuries using the deep learning model and training was applied. Thus, the nurses were provided with the patient's care and treatment according to the developed mobile application according to the pressure injury stage detected by the deep learning model. After the training, the "Satisfaction Survey with the Training Method" was applied to the nurses. 1 week after the training, the nurses were given the "Post-Test ( Modified "Pieper Pressure Sore Knowledge Test)" was applied

Sponsors & Collaborators

• University of Beykent

  lead OTHER

Principal Investigators

• Hamiyet Kızıl, Phd RN · Istanbul Beykent University

Study Design

Allocation

RANDOMIZED

Purpose

PREVENTION

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Max Age

35 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2021-01-27

Primary Completion

2021-03-01

Completion

2022-06-01

Countries

• Turkey (Türkiye)

Study Locations