Evidence map›Paper›PMID 35072073›Full record

ArticleAesthetic surgery journal. Open forum2022

BreastGAN: Artificial Intelligence-Enabled Breast Augmentation Simulation.

Christian Chartier, Ayden Watt, Owen Lin, Akash Chandawarkar, James Lee, Elizabeth Hall-Findlay

Open access · goldAbstract read
In one paragraph

Article in Aesthetic surgery journal. Open forum, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
4.2field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.

  1. Pooled it
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  4. Preoperative 3-dimensional Simulation in Aesthetic Surgery.Plastic and reconstructive surgery. Global open · 2026
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  9. AI-Based Facial Analysis vs Self-Report: A Pilot Study Using Insights From Pre-Procedural Psychological Tests.Medical science monitor : international medical journal of experimental and clinical research · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 2 countries.

Christian ChartierMcGill University Faculty of Medicine, Montreal, QC, Canada.ORCID https://orcid.org/0000-0003-2668-2874
Ayden WattDepartment of Experimental Surgery, McGill University Faculty of Medicine, Montreal, QC, Canada.ORCID https://orcid.org/0000-0001-5310-4362
Owen LinMcGill University, Montreal, QC, Canada.
Akash ChandawarkarManhattan Eye, Ear, and Throat Hospital, New York, NY, USA.
James LeeDivision of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, QC, Canada.
Elizabeth Hall-FindlayMcGill University Faculty of Medicine, Montreal, QC, Canada.
McGill University · CAManhattan Eye, Ear and Throat Hospital · USMcGill University Health Centre · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundManaging patient expectations is important to ensuring patient satisfaction in aesthetic medicine. To this end, computer technology developed to photograph, digitize, and manipulate three-dimensional (3D) objects has been applied to the female breast. However, the systems remain complex, physically cumbersome, and extremely expensive.

objectivesThe authors of the current study wish to introduce the plastic surgery community to BreastGAN, a portable, artificial intelligence (AI)-equipped tool trained on real clinical images to simulate breast augmentation outcomes.

methodsCharts of all patients who underwent bilateral breast augmentation performed by the senior author were retrieved and analyzed. Frontal before and after images were collected from each patient's chart, cropped in a standardized fashion, and used to train a neural network designed to manipulate before images to simulate a surgical result. AI-generated frontal after images were then compared with the real surgical results.

resultsStandardizing the evaluation of surgical results is a timeless challenge which persists in the context of AI-synthesized after images. In this study, AI-generated images were comparable to real surgical results.

conclusionsThis study features a portable, cost-effective neural network trained on real clinical images and designed to simulate surgical results following bilateral breast augmentation. Tools trained on a larger dataset of standardized surgical image pairs will be the subject of future studies.

Identifiers

PMID35072073
PMCPMC8781773
OpenAlexW4200336566

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.