Deep Learning Model Detecting Pressure Injury

NCT06641258 | Completed DMC

• 1 other identifier: 2104
• Study Type: interventional
• Target: 60
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

pressure injury,; deep learning model,; artificial intelligence,; nursing

Outcome Measures

Primary Outcomes (1)

• Knowledge levels

  Nurses were informed about the knowledge exam. Modified Pieper Pressure Sore Knowledge Test: As a result of the research, the Modified Scale was developed by Pieper and Mott in 1995, modified by Lawrence , and its validity and reliability were determined by Asiye Gül and her colleagues in 2017. Pieper Pressure Wound Knowledge Test was used. This test consists of 49 items. The scale is divided into three sub-dimensions. The general knowledge score can be up to 49 points, the prevention knowledge score can be up to 33 points, the staging knowledge score can be up to 9 points, and the wound identification score can be up to 7 points. Modified Permission was requested from Prof. Dr. Asiye Gül for the Pieper Pressure Sore Knowledge Test. A reliability analysis was performed to determine the reliability level of the scale used in the study and the Chronbach alpha coefficient of the experimental group was obtained as 0.838 and that of the control group as 0.812.

  12 month

Secondary Outcomes (1)

• Nurse Satisfaction levels

  12 month

Study Arms (2)

Control Group (Standard Procedure)

OTHER

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: Control Group (Standard Procedure)

Experimental Group

EXPERIMENTAL

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

Other: Experimental Group

Interventions

Control Group (Standard Procedure) OTHER

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.

Control Group (Standard Procedure)

Experimental Group OTHER

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

Experimental Group

Eligibility Criteria

• Age: 18 Years - 35 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• The nurse must;
• Be over 18 years of age
• Work as an intensive care or clinical nurse
• Agree to participate in the research verbally and in writing.

You may not qualify if:

• The nurse;
• Being under the age of 18
• Working in a place other than intensive care and clinic (e.g. blood collection unit, laboratory, etc.)
• Not accepting to participate in the research verbally or in writing.

Sponsors & Collaborators

• University of Beykent: lead

Study Sites (1)

Beykent University

Istanbul, 34500, Turkey (Türkiye)

Location

Related Publications (2)

• Raju D, Su X, Patrician PA, Loan LA, McCarthy MS. Exploring factors associated with pressure ulcers: a data mining approach. Int J Nurs Stud. 2015 Jan;52(1):102-11. doi: 10.1016/j.ijnurstu.2014.08.002. Epub 2014 Aug 18.

  PMID: 25192963 RESULT

• Alderden J, Pepper GA, Wilson A, Whitney JD, Richardson S, Butcher R, Jo Y, Cummins MR. Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model. Am J Crit Care. 2018 Nov;27(6):461-468. doi: 10.4037/ajcc2018525.

  PMID: 30385537 RESULT

MeSH Terms

Conditions

Personal Satisfaction; Pressure Ulcer

Interventions

Control Groups

Condition Hierarchy (Ancestors)

Behavior; Skin Ulcer; Skin Diseases; Skin and Connective Tissue Diseases

Intervention Hierarchy (Ancestors)

Epidemiologic Research Design; Epidemiologic Methods; Investigative Techniques; Research Design; Methods

Study Officials

• Hamiyet Kızıl, Phd RN

  Istanbul Beykent University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PhD RN Assistant Professor Hamiyet KIZIL

Study Record Dates

• First Submitted: October 11, 2024
• First Posted: October 15, 2024
• Study Start: January 27, 2021
• Primary Completion: March 1, 2021
• Study Completion: June 1, 2022
• Last Updated: October 15, 2024

Record last verified: 2024-10

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)