One Profile to Rule Them All? A Neural Network Analysis of the Homogenizing Effect of Primary Rhinoplasty

Overview

Rhinoplasty aims to improve the nose while preserving individuality. The authors tested whether surgery makes patients look more alike, and whether each surgeon leaves a recognisable "signature." They collected standardised, unedited frontal, lateral and oblique photographs of 145 primary rhinoplasty patients (Surgeon A 50, B 50, C 45) and used Amazon Rekognition to compute similarity between all pre-op images and all post-op images.

Sections of note

• Overall similarity increased 17.58% after surgery (P < .001): lateral 19.17%, oblique 19.02%, frontal 14.38%.
• By surgeon: A +37.50%, B +24.60%, C +17.68%.
• Within-surgeon similarity increase (30.89%) was significantly greater than between-surgeon increase (14.15%).
• Level of Evidence 3.

What it means for a patient

• Rhinoplasty tends to move noses toward a common ideal, and each surgeon has a recognisable style.
• Looking at a surgeon's gallery tells you what your nose is likely to be pulled toward; choose a surgeon whose results you would want on your own face.
• The study measured similarity, not attractiveness or satisfaction.

Why this paper matters

It is the first quantification of the homogenising effect and the surgeon signature using neural networks. Limits: website photographs (selected by surgeons), one ethnic group and sex, similarity metric is a black box.

Terms

• Neural network: an AI system trained to recognise patterns, here faces.
• Amazon Rekognition: a commercial face-comparison service.
• Homogenisation: becoming more alike.
• Intracohort / intercohort: within one surgeon's patients versus across surgeons.