An exploratory study of headache pain intensity using facial expressions and APEX frames.

In this cross-sectional observational study, we developed a computer vision-based machine-learning model to estimate headache pain intensity from facial action units (AUs) and evaluated its association with self-reported pain. Facial videos were obtained from 80 adults aged 19-80 years receiving headache care at a single institution (outpatient, inpatient, or emergency; IRB No. 2024-08-003). A multitask cascaded convolutional network was used to detect and align faces, and convolutional neural networks estimated facial landmark locations and AU intensities. Recordings were synchronized and denoised. We extracted an APEX frame representing the peak facial expression and computed a headache pain intensity index (HPII) by combining pain-relevant AUs. The HPII showed a moderate positive Pearson correlation coefficient (r) with visual analog scale scores (r = 0.413-0.522, where higher r values indicate a stronger linear association) among participants with moderate-to-severe pain. This AU-based apex-frame approach may serve as a practical nonverbal indicator for monitoring headache pain intensity.