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相关实验视频

Updated: Jun 14, 2026

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
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人工智能模型使用面部表情预测术后疼痛:试点研究

Insun Park1, Jae Hyon Park2, Jongjin Yoon3

  • 1Department of Anaesthesiology and Pain Medicine, Seoul National University Bundang Hospital, 82, Gumi 173, Bundang, Seongnam, 13620, Gyeonggi, Republic of Korea.

Journal of clinical monitoring and computing
|December 27, 2023
PubMed
概括

分析面部表情的人工智能模型可以准确地预测明显的手术后疼痛. 这项技术显示出查需要立即缓解疼痛的患者的潜力.

关键词:
人工智能的人工智能是人工智能.面部识别功能 面部识别功能机器学习 机器学习数字评级级别的数值评级表.手术后的疼痛 术后的疼痛

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科学领域:

  • 医学技术 医学技术 医学技术
  • 人工智能的人工智能是人工智能.
  • 疼痛管理 疼痛管理

背景情况:

  • 评估术后疼痛对于患者的康复至关重要.
  • 术后患者的客观疼痛测量仍然具有挑战性.
  • 面部表情提供了一个潜在的非侵入性疼痛指标.

研究的目的:

  • 评估人工智能 (AI) 模型在预测显著的术后疼痛方面的准确性.
  • 确定面部表情是否可以作为手术后疼痛强度的可靠指标.

主要方法:

  • 分析了155个来自胃癌手术患者的面部表情.
  • 机器学习模型是使用面部动作单元 (AU),目光和地标开发的.
  • 模型预测了显著的疼痛 (NRS ≥ 7) 与不那么显著的疼痛 (NRS < 7).

主要成果:

  • 特定的AU (AU17,AU20) 与疼痛有一定的关联,但效果不如一般的面部特征.
  • 使用头部位置和面部地标的模型实现了更高的预测准确性 (AUROC 0.85-0.96).
  • 一个合并的人工智能模型,结合了目光,眼睛,头部和面部地标,显示出最佳性能 (AUROC 0.90).

结论:

  • 分析面部表情的AI模型可以准确地预测明显的手术后疼痛.
  • 这些模型有可能用于查需要紧急止痛的患者.
  • 面部地标和头部位置比特定的AU更能预测术后疼痛.