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Making the Subjective Objective: Machine Learning and Rhinoplasty.

Robert Dorfman¹, Irene Chang¹, Sean Saadat¹

¹Division of Plastic and Reconstructive Surgery, UCLA David Geffen School of Medicine, Los Angeles, CA.

Aesthetic Surgery Journal

|December 1, 2019

Summary

This summary is machine-generated.

A novel machine learning algorithm accurately estimated patient age before and after cosmetic rhinoplasty. Open rhinoplasty was found to reverse signs of facial aging, making patients appear younger.

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Area of Science:

Plastic Surgery
Artificial Intelligence
Dermatology

Background:

Machine learning, specifically Convolutional Neural Networks (CNNs), offers innovative approaches to surgical analysis.
Aging patterns can be subtle and difficult to detect with the human eye, necessitating advanced analytical tools.

Purpose of the Study:

To evaluate the impact of aesthetic open rhinoplasty on perceived facial aging.
To utilize a novel machine learning algorithm for objective age estimation before and after rhinoplasty.

Main Methods:

A retrospective review of 100 female patients who underwent open rhinoplasty between 2014-2018.
Facial age was estimated using Microsoft Azure Face API, which employs a CNN-based prediction model.
Postoperative photos were analyzed at a minimum of 12 weeks follow-up.

Main Results:

The ranking CNN algorithm demonstrated high accuracy in age prediction (r = 0.91).
Preoperatively, the algorithm estimated patients to be, on average, 0.03 years older than their actual age.
Post-open rhinoplasty, patients appeared significantly younger, with an average estimated age reduction of 3.10 years (P < 0.0001).

Conclusions:

The ranking CNN algorithm provides accurate and precise age estimations in the context of cosmetic rhinoplasty.
Open rhinoplasty can effectively reverse visible signs of facial aging, a finding supported by objective AI-driven analysis.