使用基于机器学习的面部表情分析实时估计数字评分量表（NRS）分数：概念验证研究
Real-Time Estimation of Numerical Rating Scale (NRS) Scores Using Machine Learning-Based Facial Expression Analysis: A Proof-of-Concept Study.
摘要(中文译文)
背景 准确的疼痛评估是有效癌症疼痛管理的基础。然而，数字评定量表（NRS）等主观量表在言语沟通受损的儿童和老年患者中存在局限性。本研究旨在开发一种基于面部表情的实时NRS估计流程，并评估其作为概念验证（PoC）的技术可行性。方法 采用Python（版本3.11；Python Software Foundation，美国弗吉尼亚州弗雷德里克斯堡）实现实时分析流程，整合MediaPipe进行面部检测、DeepFace进行情绪估计。从30 fps视频流中提取的7个情绪概率评分作为预测变量，使用包括随机森林（RF）在内的多种回归模型估计NRS值。为技术验证生成的合成数据集采用留一交叉验证（LOOCV）进行评估。性能采用Spearman秩相关系数（ρ）和平均绝对误差（MAE）评估。结果 在儿童数据集中，RF模型达到ρ = 0.7383（p < 0.001）和MAE = 1.5195，与基线模型（ρ = 0.2765）相比性能有所提高。在老年数据集中，RF模型显示ρ = 0.7566（p < 0.001）和MAE = 1.5760。特征重要性分析表明，“恐惧”在两个数据集中均贡献突出，而“中性”在老年数据集中也显示出相对较高的重要性。结论 本研究证明了使用基于人工智能（AI）的面部表情分析进行实时NRS估计流程的技术可行性。研究结果表明，其有望作为言语沟通受限患者疼痛评估的补充方法，尚待未来临床验证。
Abstract
Background Accurate pain assessment is fundamental to effective cancer pain management. However, subjective scales such as the Numerical Rating Scale (NRS) have limitations in pediatric and elderly patients with impaired verbal communication. This study aimed to develop a real-time facial expression-based pipeline for NRS estimation and to evaluate its technical feasibility as a proof of concept (PoC). Methods A real-time analysis pipeline was implemented in Python (version 3.11; Python Software Foundation, Fredericksburg, VA, USA), integrating MediaPipe for facial detection and DeepFace for emotion estimation. Seven emotion probability scores extracted from 30 fps video streams were used as predictor variables to estimate NRS values using several regression models, including Random Forest (RF). Synthetic datasets generated for technical validation were evaluated using leave-one-out cross-validation (LOOCV). Performance was assessed using Spearman's rank correlation coefficient (ρ) and mean absolute error (MAE). Results In the pediatric dataset, the RF model achieved ρ = 0.7383 (p < 0.001) and MAE = 1.5195, demonstrating improved performance compared with the baseline model (ρ = 0.2765). In the elderly dataset, the RF model showed ρ = 0.7566 (p < 0.001) and MAE = 1.5760. Feature importance analysis indicated that "Fear" contributed prominently in both datasets, whereas "Neutral" also showed relatively higher importance in the elderly dataset. Conclusions This study demonstrated the technical feasibility of a real-time NRS estimation pipeline using artificial intelligence (AI)-based facial expression analysis. The findings suggest potential applicability as a complementary pain assessment approach for patients with limited verbal communication, pending future clinical validation.
如何引用
AMA
Kentaro Uejima, Tsutomu Takahashi, Miki Matsui, Sakae Fukushima, Yoshifumi Nishi. Real-Time Estimation of Numerical Rating Scale (NRS) Scores Using Machine Learning-Based Facial Expression Analysis: A Proof-of-Concept Study.. Cureus. 2026; doi:10.7759/cureus.107883.
APA
Kentaro Uejima, Tsutomu Takahashi, Miki Matsui, Sakae Fukushima, Yoshifumi Nishi (2026). Real-Time Estimation of Numerical Rating Scale (NRS) Scores Using Machine Learning-Based Facial Expression Analysis: A Proof-of-Concept Study.. Cureus. https://doi.org/10.7759/cureus.107883
GB/T 7714
Kentaro Uejima, Tsutomu Takahashi, Miki Matsui, Sakae Fukushima, Yoshifumi Nishi. Real-Time Estimation of Numerical Rating Scale (NRS) Scores Using Machine Learning-Based Facial Expression Analysis: A Proof-of-Concept Study.[J]. Cureus, 2026 doi:10.7759/cureus.107883.
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