Background: Facial expressions of pain are essential for pain assessment, yet subjective pain reports often vary between sexes. Traditional self-report measures are prone to bias, and objective methods are needed for more reliable pain evaluation. Objective: To develop and validate a subjectivity-free automated tool to assess acute low back pain using facial expressions recorded during a functional spinal extension task. Participants: Thirty healthy adults, aged 18–40 years. Methods: Participants received intramuscular injections of hypertonic (pain) and isotonic (placebo) saline in the lumbar region during separate sessions. Facial expressions were video-recorded during a submaximal lumbar extension task and analyzed using a custom software based on Haar Cascade and Local Binary Pattern Histogram algorithms, which are techniques that do not require neither training data nor subjective labeling, contrary to what happens in deep learning solutions. Results: The tool successfully detected significant differences in facial expressions between pain, placebo, and pain-free conditions (p < 0.001). Test–retest reliability was good (ICC = 0.85). While both sexes showed similar facial expression patterns during pain, males reported higher pain scores on the numeric rating scale (p < 0.01). Pain significantly reduced steadiness of force in both sexes. Conclusion: The automated tool objectively quantified facial expressions associated with acute low back pain and revealed sex-related differences in subjective pain perception. This multimodal approach integrating expression analysis, physical performance, and self-report may enhance the accuracy of pain assessment in physiotherapy settings.
Parolini et al. (Mon,) studied this question.
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