Evidence map›Paper›PMID 41261254›Full record

ArticleAesthetic plastic surgery2026

AI-Driven Insights into Glabellar Wrinkle Patterns: Reassessing the Standardised Botulinum Toxin: An Injection Protocol to Address Anatomical Variability.

Eqram Rahman, Patricia E Garcia, Karim Sayed, Parinitha Rao, Sotirios Ioannidis, Nanze Yu, Leila Sayed, Aoife Flanagan, Wolfgang G Philipp-Dormston, Alexander D Nassif and 4 more

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Article in Aesthetic plastic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

14 authors.

Eqram RahmanResearch and Innovation Hub, Innovation Aesthetics, London, WC2H 9JQ, UK. Eqram.rahman@gmail.com.ORCID http://orcid.org/0000-0002-8443-8338
Patricia E GarciaPrivate practice in dermatology, Puerto Vallarta, Mexico.
Karim SayedNomi Oslo, Oslo, Norway.
Parinitha RaoThe Skin Address, Aesthetic Dermatology Practice, Bangalore, India.
Sotirios IoannidisPlastic Surgery Clinic, 546 21, Thessaloniki, Greece.
Nanze YuDepartment of Plastic Surgery, Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, PekingBeijing, China.
Leila SayedNomi Oslo, Oslo, Norway.
Aoife FlanaganBeechwood Dental and Implant Centre, Dublin, Republic of Ireland.
Wolfgang G Philipp-DormstonHautzentrum Koeln, Cologne, Germany.
Alexander D NassifDepartamento de Pesquisa, Núcleo Nassif-Ensino Médico e Pesquisa, Belo Horizonte, 30411-148, Brazil.
William Richard WebbResearch and Innovation Hub, Innovation Aesthetics, London, WC2H 9JQ, UK.
Jean D A CarruthersDepartment of Ophthalmology, University of British Columbia, Vancouver, BC, Canada.
Greg J GoodmanMonash University Australia, Clayton, VIC, Australia.
Woffles T L WuWoffles Wu Aesthetic Surgery and Laser Centre, Camden Medical Centre Singapore, Orchard Boulevard Suite 09-02, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionGlabellar wrinkle patterns, a visible manifestation of facial dynamics and ageing, result from complex interactions between anatomical and functional variables such as muscle interdigitation density, skin elasticity, and subcutaneous tissue characteristics. The standard injection pattern for botulinum toxin type A (BoNTA) is widely used but assumes uniformity in these factors, potentially overlooking individual anatomical and demographic variability. This study evaluates whether the standard injection pattern adequately addresses these differences.

methodsAdvanced biomechanical modelling and machine learning were employed to analyse the interactions between glabellar muscles, skin, and subcutaneous tissue. AI frameworks, including convolutional neural networks and long short-term memory networks, were trained on 3D facial scan data capturing dynamic expressions. Bayesian modelling quantified demography-specific predictors, while finite element analysis simulated wrinkle patterns under varying biomechanical conditions.

resultsThe hybrid cohort included 600 participants (300 males, 300 females), evenly distributed across Caucasian (n = 150), African American (n = 150), Asian (n = 100), Hispanic (n = 100), and Middle Eastern (n = 100) ethnicities, aged 18-65 years. Significant predictors of wrinkle patterns were age (β = -0.64, p < 0.001), elasticity (β = 0.48, p < 0.01), and BMI (β = -0.52, p < 0.01). Within our cohort, higher interdigitation density was observed in Asian and African American participants, producing narrower wrinkle configurations ("11", "V"), while lower densities in Caucasian and Hispanic groups allowed broader patterns ("omega", "converging arrows"). AI models achieved >90% accuracy, demonstrating significant variability in wrinkle morphology and the need to reassess the universal applicability of the standard injection pattern.

conclusionThis study underscores the limitations of the standard injection pattern, advocating for anatomically precise, personalised treatment protocols to better accommodate demographic and anatomical diversity in clinical practice. 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 IntelligenceBotulinum Toxins, Type ANeuromuscular AgentsSkin AgingAdolescentAdultAgedCohort StudiesFacial MusclesFemaleHumansInjections, SubcutaneousMachine LearningMaleMiddle AgedYoung AdultBotulinum Toxins, Type ANeuromuscular Agents3-point injection pattern5-point injection patternAnatomical variabilityArtificial intelligenceBotulinum toxin AGlabellar wrinklesMuscle interdigitationPersonalised treatmentPredictive modelling

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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.