Using Facial Recognition Software to Quantify Perceived Age Reduction in Patients Undergoing Blepharoplasty

Convolutional neural networks quantified reductions in perceived age following blepharoplasty, with results aligning with human evaluations. CNN-perceived age reduction was greatest in patients who appeared older than their true age, particularly for those with larger discrepancies. These findings support the potential utility of CNNs as objective tools for assessing aesthetic outcomes and may help preoperatively guide patient expectations for postoperative age reduction.