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.
Background:
Machine learning represents a new frontier in surgical innovation. The ranking Convolutional Neural Network (CNN) is a novel machine learning algorithm that helps elucidate patterns and features of aging that are not always appreciable with the human eye.
Objectives:
The authors sought to determine the impact of aesthetic rhinoplasty on facial aging employing a multidimensional facial recognition and comparison software.
Methods:
A retrospective chart review and subsequent analysis was carried out on all female patients who underwent open rhinoplasty with the senior author from 2014 through 2018 and had postoperative photos at 12 or more weeks follow-up. All photos were analyzed with Microsoft Azure Face API (Redmond, WA), which estimates patients' age by cropping the face from a photograph and then extracting a CNN-based prediction through multiple deep neural networks.
Results:
A total of 100 patients ultimately met full inclusion criteria. The average post-surgical follow up for this cohort was 29 weeks (median, 14 weeks; range, 12-64 weeks). Patients ranged from 16 to 72 years old (mean, 32.75 years; median, 28.00 years; standard deviation, 12.79 years). The ranking CNN algorithm on average estimated patients preoperatively to be 0.03 years older than their actual age. The correlation coefficient between actual age and predicted preoperative age was r = 0.91. On average, patients were found to look younger post-open rhinoplasty (-3.10 vs 0.03 years, P < 0.0001).
Conclusions:
The ranking CNN algorithm is both accurate and precise in estimating human age before and after cosmetic rhinoplasty. Given the resulting data, the effects of open rhinoplasty on reversing signs of facial aging should be revisited.
