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人工智能辅助疼痛评估工具在婴儿中的临床适用性:可行性和可用性评估研究。

The Clinical Suitability of an Artificial Intelligence-Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study.

机构信息

Curtin Medical School, Curtin University, Perth, Australia.

Institute for Health Research, The University of Notre Dame Australia, Fremantle, Australia.

出版信息

J Med Internet Res. 2023 Feb 13;25:e41992. doi: 10.2196/41992.

Abstract

BACKGROUND

Infants are unable to self-report their pain, which, therefore, often goes underrecognized and undertreated. Adequate assessment of pain, including procedural pain, which has short- and long-term consequences, is critical for its management. The introduction of mobile health-based (mHealth) pain assessment tools could address current challenges and is an area requiring further research.

OBJECTIVE

The purpose of this study is to evaluate the accuracy and feasibility aspects of PainChek Infant and, therefore, assess its applicability in the intended setting.

METHODS

By observing infants just before, during, and after immunization, we evaluated the accuracy and precision at different cutoff scores of PainChek Infant, which is a point-of-care mHealth-based solution that uses artificial intelligence to detect pain and intensity based solely on facial expression. We used receiver operator characteristic analysis to assess interpretability and establish a cutoff score. Clinician comprehensibility was evaluated using a standardized questionnaire. Other feasibility aspects were evaluated based on comparison with currently available observational pain assessment tools for use in infants with procedural pain.

RESULTS

Both PainChek Infant Standard and Adaptive modes demonstrated high accuracy (area under the curve 0.964 and 0.966, respectively). At a cutoff score of ≥2, accuracy and precision were 0.908 and 0.912 for Standard and 0.912 and 0.897 for Adaptive modes, respectively. Currently available data allowed evaluation of 16 of the 17 feasibility aspects, with only the cost of the outcome measurement instrument unable to be evaluated since it is yet to be determined. PainChek Infant performed well across feasibility aspects, including interpretability (cutoff score defined), ease of administration, completion time (3 seconds), and clinician comprehensibility.

CONCLUSIONS

This work provides information on the feasibility of using PainChek Infant in clinical practice for procedural pain assessment and monitoring, and demonstrates the accuracy and precision of the tool at the defined cutoff score.

摘要

背景

婴儿无法自我报告疼痛,因此往往未被充分识别和治疗。充分评估疼痛,包括具有短期和长期后果的程序性疼痛,对其治疗至关重要。引入基于移动健康(mHealth)的疼痛评估工具可以解决当前的挑战,这是一个需要进一步研究的领域。

目的

本研究旨在评估 PainChek Infant 的准确性和可行性方面,从而评估其在预期环境中的适用性。

方法

通过观察婴儿在免疫接种前后的情况,我们评估了 PainChek Infant 在不同截断分数下的准确性和精密度,该工具是一种即时护理的基于 mHealth 的解决方案,仅根据面部表情使用人工智能来检测疼痛和强度。我们使用接收者操作特征分析来评估可解释性并确定截断分数。临床医生的理解能力通过使用标准化问卷进行评估。其他可行性方面是基于与目前用于评估有程序性疼痛的婴儿的观察性疼痛评估工具进行比较来评估的。

结果

PainChek Infant 标准模式和自适应模式都表现出了很高的准确性(曲线下面积分别为 0.964 和 0.966)。在截断分数≥2 的情况下,标准模式的准确性和精密度分别为 0.908 和 0.912,自适应模式分别为 0.912 和 0.897。目前可用的数据允许评估 17 个可行性方面中的 16 个,只有结果测量仪器的成本无法评估,因为它尚未确定。PainChek Infant 在各个可行性方面表现良好,包括可解释性(定义了截断分数)、易于管理、完成时间(3 秒)和临床医生的理解能力。

结论

这项工作提供了关于在临床实践中使用 PainChek Infant 进行程序性疼痛评估和监测的可行性信息,并展示了该工具在定义的截断分数下的准确性和精密度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/899c/9972204/06f02e8aab37/jmir_v25i1e41992_fig1.jpg

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