Evidence map›Paper›PMID 41530558›Full record

ArticleAesthetic plastic surgery2026

AI-Based Quantitative and Objective Analysis of Aesthetic Results in Genioplasty.

Jakob Fenske, Samuel Knoedler, Tobias Niederegger, Simon Bigus, Jan O Voss, Rainer Pooth, Carsten Rendenbach, Max Heiland, Alexandre G Lellouch, Leonard Knoedler

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Aesthetic plastic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Article
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

10 authors.

Jakob FenskeDepartment of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Samuel KnoedlerDivision of Plastic Surgery, Department of Surgery, Yale School of Medicine, New Haven, CT, USA.
Tobias NiedereggerDepartment of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Simon BigusDepartment of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Jan O VossDepartment of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Rainer PoothClinical Research and Development, ICA Aesthetic Navigation, Frankfurt am Main, Germany.
Carsten RendenbachDepartment of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Max HeilandDepartment of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Alexandre G Lellouch *Division of Plastic and Reconstructive Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Leonard Knoedler *Department of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany. leonard.knoedler@charite.de.ORCID 0000-0002-8949-3168

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenioplasty and chin-augmentation are well-established procedures aimed at enhancing lower facial aesthetics. Traditionally, aesthetic outcomes have been assessed subjectively through expert opinions and patient-reported measures. The integration of artificial intelligence (AI) offers an objective approach to evaluating surgical results. This study utilizes the ICA Aesthetic Navigation AI Research Metrics Model (ICAAN® ARMM) to analyze postoperative changes in facial attractiveness, youthfulness, and skin quality following genioplasty.

methodsPre- and postoperative full-frontal images of 50 patients undergoing osseous genioplasty were analyzed using the ICAAN® ARMM. Therefore, an array of three aesthetic scores, the Facial Aesthetic Index (FAI), Facial Youthfulness Index (FYI), and Skin Quality Index (SQI), were measured before and after surgery, with subgroup analyses by age, sex, and ethnicity. Minimally clinically important differences (MCIDs) were estimated.

resultsAll three aesthetic scores demonstrated improvement postoperatively, with FAI showing the greatest increase (82 (73-89) to 85 (75-92); p = 0.296), without showing statistical significance. Older patients (≥ 35 years) exhibited greater improvements in FAI scores compared to younger individuals (4 (1-10) vs. 1 (- 3-5); p = 0.028). Sex-related trends were observed, while lacking statistical significance. Ethnic subgroup analysis revealed no differences in score changes, suggesting cross-cultural applicability. Observed improvements did not exceed estimated MCIDs.

conclusionAI-assisted aesthetic analysis offers a novel, contemporary, and objective method for assessing genioplasty outcomes. While our study suggests general aesthetic improvements following surgery, further research incorporating larger data collections and subjective patient-reported measures is necessary. AI tools hold promise as a complementary tool in aesthetic medicine, supporting both clinicians and patients in surgical decision-making. 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 IntelligenceEstheticsGenioplastyAdolescentAdultCohort StudiesFemaleHumansMaleMiddle AgedPatient SatisfactionRetrospective StudiesTreatment OutcomeYoung AdultAesthetic outcomesAesthetic surgeryArtificial intelligenceGenioplastyMachine learning

Identifiers

PMID41530558
PMCPMC13009015

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.