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

Authors : Hamiyet Kızıl, Atınç Yılmaz, Melek Demiral, Umut Kaya, Rıdvan Çakır

Pages : 207-219

Doi:10.62111/ybhd.1749992

View : 77 | Download : 317

Publication Date : 2025-12-25

Article Type : Research Paper

Abstract :Objective: The study was conducted to determine the effect of the deep learning model on the knowledge and satisfaction levels of nurses in the detection and classification of pressure injuries. Method: The population of this randomized controlled trial consisted of nurses working in intensive care, internal medicine, and surgical clinics at a foundation university hospital between March and April 2022 who voluntarily participated in the study. The sample consisted of a total of 60 (30 experimental and 30 control) nurses who met the sample criteria. The research data were collected using the Structured Nurse Introduction Form, Modified Pieper Pressure Injury Knowledge Test and Nurse Satisfaction Scale.The research data were analyzed in the SPSS 25.0 program. Results: The mean age of the nurses in the experimental group was determined as 25.67±7.27, and the control group as 25.10±3.47. 50% of the nurses in the experimental and control groups graduated from health vocational high schools, and 40% of them worked in surgical services. When the nurses\\\' post-training knowledge exam (post-test) scores were compared; the mean score of the experimental group was determined as 39.36±1.88 and the control group as 33.30±1.68. The post-training knowledge level of the experimental group was found to be statistically significantly higher than the control group (P<.05). When the success of the pressure injury risk assessment and stage determination was examined, it was determined that the experimental group was able to assess the risk with 97% success with the deep learning model and determine the wound stage with 89% prediction verification. It was determined that the control group determined the patients\\\' risk levels with the Braden pressure injury risk assessment scale at a moderate level with 13.83±4.67 and were 50% successful in stage estimation. The evaluation and stage estimation levels were found to be statistically significantly higher than the control group (P<.05). When the satisfaction levels of the nurses participating in the study with the applied training were examined; the average score of the experimental group was determined as 24.60±0.96 and the control group as 20.93±0.63. The satisfaction level of the experimental group with the training was found to be statistically significantly higher than the control group (P<.05). Conclusion: It was determined that pressure injury detection and classification with artificial intelligence technology was more successful than the traditional method.
Keywords : Basınç yaralanması, derin öğrenme, yapay zeka

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