Facial features of lysosomal storage disorders

ABSTRACT

Introduction

The use of facial recognition technology has diversified the diagnostic toolbelt for clinicians and researchers for the accurate diagnoses of patients with rare and challenging disorders. Specific identifiers in patient images can be grouped using artificial intelligence to allow the recognition of diseases and syndromes with similar features. Lysosomal storage disorders are rare, and some have prominent and unique features that may be used to train the accuracy of facial recognition software algorithms. Noteworthy features of lysosomal storage disorders (LSDs) include facial features such as prominent brows, wide noses, thickened lips, mouth, and chin, resulting in coarse and rounded facial features.

Areas covered

We evaluated and report the prevalence of facial phenotypes in patients with different LSDs, noting two current examples when artificial intelligence strategies have been utilized to identify distinctive facies.

Expert opinion

Specific LSDs, including Gaucher disease, Mucolipidosis IV and Fabry disease have recently been distinguished using facial recognition software. Additional lysosomal disorders LSDs lysosomal storage disorders with unique and distinguishable facial features also merit evaluation using this technology. These tools may ultimately aid in the identification of specific LSDs and shorten the diagnostic odyssey for patients with these rare and under-recognized disorders.

KEYWORDS:

• Coarse facial features
• diagnostic odyssey
• facial recognition software
• Fabry disease
• Gaucher disease
• lipidoses
• mucopolysaccharidoses
• lysosomal storage disorders

Article highlights

• Facial recognition software has successfully identified specific facial phenotypes in lysosomal storage disorders such as Gaucher disease, Mucolipidosis IV and Fabry disease.

• Additional lysosomal storage disorders with characteristic facial features and dysmorphism may also potentially benefit from the use of facial recognition technology.

• Increased use of such software in clinical settings may assist clinicians in narrowing the scope of potential diagnoses for patients based on facial features. This allows for a targeted approach to genetic testing and would shorten the diagnostic odyssey.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

A reviewer on this manuscript belongs to the Scientific Advisory Board of the Face2Gene [FDNA]. The remaining reviewers have no other relevant financial relationships or otherwise to disclose.

Additional information

Funding

This paper received support from The Intramural Research Programs of the NIH and NHGRI.