Evidence map›Paper›PMID 38777929›Full record

ArticleAesthetic plastic surgery2024

Turn Your Vision into Reality-AI-Powered Pre-operative Outcome Simulation in Rhinoplasty Surgery.

Samuel Knoedler, Michael Alfertshofer, Siddharth Simon, Adriana C Panayi, Rakan Saadoun, Alen Palackic, Florian Falkner, Gabriel Hundeshagen, Martin Kauke-Navarro, Felix H Vollbach and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Aesthetic plastic surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  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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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Samuel KnoedlerDivision of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Michael AlfertshoferDepartment of Plastic and Hand Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.ORCID 0000-0002-4892-2376
Siddharth SimonDepartment of Oromaxillofacial Surgery, Ludwig-Maximilians University Munich, Munich, Germany.
Adriana C PanayiDepartment of Hand-, Plastic and Reconstructive Surgery, Microsurgery, Burn Center, BG Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany.
Rakan SaadounDepartment of Plastic Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
Alen PalackicDepartment of Hand-, Plastic and Reconstructive Surgery, Microsurgery, Burn Center, BG Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany.
Florian FalknerDepartment of Hand-, Plastic and Reconstructive Surgery, Microsurgery, Burn Center, BG Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany.
Gabriel HundeshagenDepartment of Hand-, Plastic and Reconstructive Surgery, Microsurgery, Burn Center, BG Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany.
Martin Kauke-NavarroDepartment of Surgery, Division of Plastic Surgery, Yale School of Medicine, New Haven, CT, USA.
Felix H VollbachDepartment of Hand-, Plastic and Reconstructive Surgery, Microsurgery, Burn Center, BG Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany.
Amir K BigdeliDepartment of Hand-, Plastic and Reconstructive Surgery, Microsurgery, Burn Center, BG Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany.
Leonard KnoedlerDepartment of Surgery, Division of Plastic Surgery, Yale School of Medicine, New Haven, CT, USA. leonardknoedler@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe increasing demand and changing trends in rhinoplasty surgery emphasize the need for effective doctor-patient communication, for which Artificial Intelligence (AI) could be a valuable tool in managing patient expectations during pre-operative consultations.

objectiveTo develop an AI-based model to simulate realistic postoperative rhinoplasty outcomes.

methodsWe trained a Generative Adversarial Network (GAN) using 3,030 rhinoplasty patients' pre- and postoperative images. One-hundred-one study participants were presented with 30 pre-rhinoplasty patient photographs followed by an image set consisting of the real postoperative versus the GAN-generated image and asked to identify the GAN-generated image.

resultsThe study sample (48 males, 53 females, mean age of 31.6 ± 9.0 years) correctly identified the GAN-generated images with an accuracy of 52.5 ± 14.3%. Male study participants were more likely to identify the AI-generated images compared with female study participants (55.4% versus 49.6%; p = 0.042).

conclusionWe presented a GAN-based simulator for rhinoplasty outcomes which used pre-operative patient images to predict accurate representations that were not perceived as different from real postoperative outcomes. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Indexed as

Artificial IntelligencePreoperative CareRhinoplastyAdultFemaleHumansMalePhotographyPhysician-Patient RelationsPreoperative PeriodTreatment OutcomeYoung AdultArtificial intelligenceComputer simulationGenerative adversarial networksNose reshapingPre-operative simulationRhinoplasty

Identifiers

PMID38777929
PMCPMC11739225

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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.