Evidence map›Paper›PMID 34917697›Full record

ArticleBone reports2021

The 3D skull 0-4 years: A validated, generative, statistical shape model.

Eimear O' Sullivan, Lara S van de Lande, Anne-Jet C Oosting, Athanasios Papaioannou, N Owase Jeelani, Maarten J Koudstaal, Roman H Khonsari, David J Dunaway, Stefanos Zafeiriou, Silvia Schievano

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In one paragraph

Article in Bone reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Is a standardised severity index needed in unicoronal craniosynostosis? Challenges in developing an objective metric.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 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

10 authors.

Eimear O' SullivanGreat Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
Lara S van de LandeGreat Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
Anne-Jet C OostingGreat Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
Athanasios PapaioannouGreat Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
N Owase JeelaniGreat Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
Maarten J KoudstaalDepartment of Oral and Maxillofacial Surgery, Erasmus Medical Centre, Rotterdam, the Netherlands.
Roman H KhonsariOral and Maxillofacial Surgery Department, Hospital Necker, Enfants Malades, Paris, France.
David J DunawayGreat Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
Stefanos ZafeiriouDepartment of Computing, Imperial College London, London, UK.
Silvia SchievanoGreat Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aims to capture the 3D shape of the human skull in a healthy paediatric population (0-4 years old) and construct a generative statistical shape model.

methodsThe skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (CT) images. 29 anatomical landmarks were placed on the 3D skull reconstructions. Rotation, translation and size were removed, and all skull meshes were placed in dense correspondence using a dimensionless skull mesh template and a non-rigid iterative closest point algorithm. A 3D morphable model (3DMM) was created using principal component analysis, and intrinsically and geometrically validated with anthropometric measurements. Synthetic skull instances were generated exploiting the 3DMM and validated by comparison of the anthropometric measurements with the selected input population.

resultsThe 3DMM of the paediatric skull 0-4 years was successfully constructed. The model was reasonably compact - 90% of the model shape variance was captured within the first 10 principal components. The generalisation error, quantifying the ability of the 3DMM to represent shape instances not encountered during training, was 0.47 mm when all model components were used. The specificity value was <0.7 mm demonstrating that novel skull instances generated by the model are realistic. The 3DMM mean shape was representative of the selected population (differences <2%). Overall, good agreement was observed in the anthropometric measures extracted from the selected population, and compared to normative literature data (max difference in the intertemporal distance) and to the synthetic generated cases.

conclusionThis study presents a reliable statistical shape model of the paediatric skull 0-4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, thus presenting a solution to limited availability of normative data in this field.

Indexed as

3D morphable modelMorphometricsPaediatric skullStatistical shape modelSynthetic shapes

Identifiers

PMID34917697
PMCPMC8645852

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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.