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Automated assessment of aesthetic outcomes following combined facial aesthetic surgery – a pilot study

Khalil Sherwani L. Knoedler Thomas Schaschinger Tobias Niederegger Jakob Fenske Kevin Sadati M. Heiland Rainer Pooth Theodore Brown C. Cetrulo Raffaele Aguglia A. Lellouch

Sep 2026 · BMC Plastic and Reconstructive Surgery · Vol 2 · 0 citations · 25 references

Abstract

Outcomes after combined facial aesthetic surgery (CFAS), defined as the simultaneous performance of two or more facial rejuvenation procedures in a single operative setting, are commonly evaluated using subjective tools with high interrater variability. This pilot feasibility study aimed to explore a quantitative, automated pipeline for CFAS outcome assessment by integrating the CAARISMA® ARMM algorithm with Vectra® imaging. Ten female patients who underwent elective CFAS were retrospectively analyzed using standardized pre- and postoperative (3-month) frontal Vectra® H2 images processed through the CAARISMA® ARMM system. The artificial intelligence (AI) algorithm automatically generated Facial Youthfulness Index (FYI), Facial Aesthetic Index (FAI), and Skin Quality Index (SQI) values, supplemented by detailed analyses of skin texture and wrinkle parameters. Postoperative scores increased significantly across all three indices: FYI (Δ relative (rel) 2.1 ± 1.8%; p = 0.016), FAI (Δ rel 12.3 ± 13.3%; p = 0.008), and SQI (Δ rel 14.3 ± 8.9%; p = 0.001). SQI demonstrated the most consistent relative improvements (coefficient of variation 62.3%), with the largest gains in fine relief (Δ rel 56.2%), roughness (Δ rel 15.6%), and rough relief (Δ rel 13.0%). Wrinkle scores improved most in the crow’s feet (lateral canthal rhytids) (Δ rel 10.4%) and infraorbital (Δ rel 6.6%) regions. To our knowledge, this is among the first studies to demonstrate clinical integration of an automated AI algorithm with a standard clinical camera system for CFAS outcome assessment. This automated workflow minimizes manual input and observer bias, but as a pilot feasibility study it has not yet been validated against surgeon- or patient-reported outcomes, and the numerical gains reported here should not be interpreted as evidence of clinically perceptible improvement. Larger, validation-focused studies are needed before broader clinical adoption.

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