ArticleJIMD reports2013
A Dysmorphometric Analysis to Investigate Facial Phenotypic Signatures as a Foundation for Non-invasive Monitoring of Lysosomal Storage Disorders.
Article in JIMD reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Advances in computer-assisted syndrome recognition by the example of inborn errors of metabolism.Journal of inherited metabolic disease · 2018Pooled it
- Artificial Intelligence, Connected Care, and Enabling Digital Health Technologies in Rare Diseases With a Focus on Lysosomal Storage Disorders: Scoping Review.Journal of medical Internet research · 2026Article
- Comparison Study of Extraction Accuracy of 3D Facial Anatomical Landmarks Based on Non-Rigid Registration of Face Template.Diagnostics (Basel, Switzerland) · 2023Article
- 3D facial analysis for rare disease diagnosis and treatment monitoring: Proof-Of-Concept plan for hereditary angioedema.PLOS digital health · 2023Article
- Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism.Scientific reports · 2021Article
- The case for open science: rare diseases.JAMIA open · 2020Review
- Monitoring of Therapy for Mucopolysaccharidosis Type I Using Dysmorphometric Facial Phenotypic Signatures.JIMD reports · 2015Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSome lysosomal storage disorders (LSDs), including Muccopolysaccharidosis type 1 (MPSI), are associated with characteristic facies. METHODS such as three-dimensional (3D) facial scanning and geometric morphometric techniques can potentially generate detailed objective descriptions of these facial phenotypes. This approach can facilitate discriminating the inherent overlap in facial phenotypes within these disease spectra, and the non-invasive monitoring of disease progression and treatment.
methods3D facial images of three MPS I-affected individuals and 400 reference subjects (aged 5-25 years) were obtained using a 3dMD camera (Atlanta, Georgia). Images were fitted with an anthropometric mask, comprising a set of spatially dense quasi-landmarks. A statistical face-space was constructed from the reference image set and the MPS I-affected individuals were compared to this face-space utilising an emerging methodology known as dysmorphometrics. This facilitated simultaneous identification of harmonic and discordant facial regions. A relative significant discordance (RSD) score quantified proportional facial discordance for a given individual, whilst a root-mean-squared-error (RMSE) score measured the degree of facial discordance providing a severity measure.
resultsA consistent facial pattern, with differential severities, primarily affecting the frontal, nasal, infraorbital and cheek regions, was detected in all three individuals. As expected, there was greater discordance (RMSE, RSD) with clinically severe MPS I when compared to attenuated disease.
conclusionsObjective detection and localisation of MPS I facial characteristics was achieved, and severity scores were attributed. This spatially dense dysmorphometric facial phenotyping technique has the potential to be used for non-invasive treatment monitoring and as a discriminatory tool.
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Registered trials
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