TY - JOUR
T1 - Shared facial phenotype of patients with mucolipidosis type IV
T2 - A clinical observation reaffirmed by next generation phenotyping
AU - Pode-Shakked, Ben
AU - Finezilber, Yael
AU - Levi, Yonit
AU - Liber, Shiri
AU - Fleischer, Nicole
AU - Greenbaum, Lior
AU - Raas-Rothschild, Annick
N1 - Publisher Copyright:
© 2020
PY - 2020/7
Y1 - 2020/7
N2 - Background: Mucolipidosis type IV (ML-IV) is a rare autosomal-recessive lysosomal storage disease, caused by mutations in MCOLN1. ML-IV manifests with developmental delay, esotropia and corneal clouding. While the clinical phenotype is well-described, the diagnosis of ML-IV is often challenging and elusive. Objective: Our experience with ML-IV patients brought to the clinical observation that they share common and identifiable facial features, not yet described in the literature to date. Here, we utilized a computerized facial analysis tool to establish this association. Methods: Using the DeepGestalt algorithm, 50 two-dimensional facial images of ten ML-IV patients were analyzed, and compared to unaffected controls (n = 98) and to individuals affected with other genetic disorders (n = 99). Results were expressed in terms of the area-under-the-curve (AUC) of the receiver-operating-characteristic curve (ROC). Results: When compared to unaffected cases and to cases diagnosed with syndromes other than ML-IV, the ML-IV cohort showed an AUC of 0.822 (p value < 0.01) and an AUC of 0.885 (p value < 0.001), respectively. Conclusions: We describe recognizable facial features typical in patients with ML-IV. Reaffirmed by the DeepGestalt technology, the described common facial phenotype adds to the tools currently available for clinicians and may thus assist in reaching an earlier diagnosis of this rare and underdiagnosed disorder.
AB - Background: Mucolipidosis type IV (ML-IV) is a rare autosomal-recessive lysosomal storage disease, caused by mutations in MCOLN1. ML-IV manifests with developmental delay, esotropia and corneal clouding. While the clinical phenotype is well-described, the diagnosis of ML-IV is often challenging and elusive. Objective: Our experience with ML-IV patients brought to the clinical observation that they share common and identifiable facial features, not yet described in the literature to date. Here, we utilized a computerized facial analysis tool to establish this association. Methods: Using the DeepGestalt algorithm, 50 two-dimensional facial images of ten ML-IV patients were analyzed, and compared to unaffected controls (n = 98) and to individuals affected with other genetic disorders (n = 99). Results were expressed in terms of the area-under-the-curve (AUC) of the receiver-operating-characteristic curve (ROC). Results: When compared to unaffected cases and to cases diagnosed with syndromes other than ML-IV, the ML-IV cohort showed an AUC of 0.822 (p value < 0.01) and an AUC of 0.885 (p value < 0.001), respectively. Conclusions: We describe recognizable facial features typical in patients with ML-IV. Reaffirmed by the DeepGestalt technology, the described common facial phenotype adds to the tools currently available for clinicians and may thus assist in reaching an earlier diagnosis of this rare and underdiagnosed disorder.
KW - DeepGestalt
KW - Dysmorphism
KW - Face2Gene
KW - Facial recognition technology
KW - MLIV
KW - Mucolipidosis IV
UR - http://www.scopus.com/inward/record.url?scp=85084394964&partnerID=8YFLogxK
U2 - 10.1016/j.ejmg.2020.103927
DO - 10.1016/j.ejmg.2020.103927
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C2 - 32298796
AN - SCOPUS:85084394964
SN - 1769-7212
VL - 63
JO - European Journal of Medical Genetics
JF - European Journal of Medical Genetics
IS - 7
M1 - 103927
ER -