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Artificial intelligence model predicting postoperative pain using facial expressions: a pilot study

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Abstract

Purpose

This study aimed to assess whether an artificial intelligence model based on facial expressions can accurately predict significant postoperative pain.

Methods

A total of 155 facial expressions from patients who underwent gastric cancer surgery were analyzed to extract facial action units (AUs), gaze, landmarks, and positions. These features were used to construct various machine learning (ML) models, designed to predict significant postoperative pain intensity (NRS ≥ 7) from less significant pain (NRS < 7). Significant AUs predictive of NRS ≥ 7 were determined and compared to AUs known to be associated with pain in awake patients. The area under the receiver operating characteristic curves (AUROCs) of the ML models was calculated and compared using DeLong’s test.

Results

AU17 (chin raising) and AU20 (lip stretching) were found to be associated with NRS ≥ 7 (both P ≤ 0.004). AUs known to be associated with pain in awake patients did not show an association with pain in postoperative patients. An ML model based on AU17 and AU20 demonstrated an AUROC of 0.62 for NRS ≥ 7, which was inferior to a model based on all AUs (AUROC = 0.81, P = 0.006). Among facial features, head position and facial landmarks proved to be better predictors of NRS ≥ 7 (AUROC, 0.85–0.96) than AUs. A merged ML model that utilized gaze and eye landmarks, as well as head position and facial landmarks, exhibited the best performance (AUROC, 0.90) in predicting significant postoperative pain.

Conclusion

ML models using facial expressions can accurately predict the presence of significant postoperative pain and have the potential to screen patients in need of rescue analgesia.

Trial registration number

This study was registered at ClinicalTrials.gov (NCT05477303; date: June 17, 2022).

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Acknowledgements

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Author notes

  1. Insun Park and Jae Hyon Park contributed equally to this work.

Authors and Affiliations

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

    Insun Park, In-Ae Song, Hyo-Seok Na, Jung-Hee Ryu & Ah-Young Oh

  2. Department of Radiology, Armed Forces Daejeon Hospital, Daejeon, Republic of Korea

    Jae Hyon Park

  3. Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea

    Jongjin Yoon

  4. Department of Anaesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea

    Jung-Hee Ryu & Ah-Young Oh

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  1. Insun Park
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Insun Park, Jae Hyon Park, and Jongjin Yoon. The first draft of the manuscript was written by Insun Park, and Jae Hyon Park, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ah-Young Oh.

Ethics declarations

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the institutional review board (IRB) of the Seoul National University Bundang Hospital (Chairperson Hak Chul Jang, B-2205-757-304) on 9 May 2022.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent to publish

The authors affirm that the participant (co-1st author: Jae Hyon Park) provided informed consent for publication of the image in Fig. 1.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

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Park, I., Park, J.H., Yoon, J. et al. Artificial intelligence model predicting postoperative pain using facial expressions: a pilot study. J Clin Monit Comput 38, 261–270 (2024). https://doi.org/10.1007/s10877-023-01100-7

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  • DOI: https://doi.org/10.1007/s10877-023-01100-7

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