Author Affiliations: Department of Child Health and Diseases Nursing, Faculty of Health Sciences, Kütahya University of Health Sciences, Kutahya, Turkey (Dr Yigit); and Department of Child Health and Diseases Nursing, Faculty of Health Sciences, Eskisehir Osmangazi University, Eskisehir, Turkey (Dr Acikgoz).
J Perinat Neonatal Nurs. 2024;38(3):E38-E45. doi: 10.1097/JPN.0000000000000768. Epub 2024 Jul 29.
One of the scales most frequently used in the evaluation of newborn comfort levels is the Neonatal Comfort Behavior Scale (NCBS). It is important therefore that an increased use of the NCBS is encouraged through a more practical method of assessment.
This study was carried out for the purpose of designing a means of assessing neonatal comfort levels by employing the techniques of artificial intelligence (AI).
The AI-based study was conducted with 362 newborns under treatment in the neonatal intensive care unit of a hospital. A data collection form, the NCBS, and a camera system were used as data collection tools. The data were analyzed with the SPSS Statistics 21.0 program. Descriptive statistics and Cohen κ test were employed in the analysis.
The 2 researchers named in the study first labeled the audiovisual recordings of the 362 newborns in the study. These labeled audiovisual recordings were used in training (80%) as well as testing (20%) the AI model. The AI model displayed a rate of success of 99.82%.
It was ultimately seen that the AI model that had been developed was a successful tool that could be used to determine the comfort behavior levels of newborns in the neonatal intensive care unit.
在评估新生儿舒适度水平时，最常使用的量表之一是新生儿舒适行为量表（NCBS）。因此，通过更实用的评估方法鼓励更多地使用 NCBS 非常重要。
本研究旨在通过使用人工智能（AI）技术设计一种评估新生儿舒适度水平的方法。
这项基于人工智能的研究在一家医院的新生儿重症监护病房对 362 名接受治疗的新生儿进行。使用数据采集表、NCBS 和摄像系统作为数据采集工具。使用 SPSS Statistics 21.0 程序对数据进行分析。采用描述性统计和 Cohen κ 检验进行分析。
研究中指定的 2 位研究人员首先对研究中 362 名新生儿的视听记录进行了标记。这些标记的视听记录用于训练（80%）和测试（20%）人工智能模型。人工智能模型的成功率为 99.82%。
最终发现，开发的人工智能模型是一种成功的工具，可用于确定新生儿重症监护病房中新生儿的舒适行为水平。
