First Italian experience using the automated craniofacial gestalt analysis on a cohort of pediatric patients with multiple anomaly syndromes

Giulia Pascolini; Mauro Calvani; Paola Grammatico

doi:10.1186/s13052-022-01283-w

First Italian experience using the automated craniofacial gestalt analysis on a cohort of pediatric patients with multiple anomaly syndromes

Ital J Pediatr. 2022 Jun 13;48(1):91. doi: 10.1186/s13052-022-01283-w.

Authors

Giulia Pascolini¹, Mauro Calvani², Paola Grammatico³

Affiliations

¹ Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Circonvallazione Gianicolense 87, 00152, Rome, Italy. giupascolini@gmail.com.
² Pediatrics Division, Woman-Child Department, San Camillo-Forlanini Hospital, Rome, Italy.
³ Medical Genetics, Department of Molecular Medicine, Sapienza University, San Camillo-Forlanini Hospital, Circonvallazione Gianicolense 87, 00152, Rome, Italy.

Abstract

Background: In this study, we used the novel DeepGestalt technology powered by Face2Gene (FDNA Inc., MA, USA) in suggesting a correct diagnosis based on the facial gestalt of well-known multiple anomaly syndromes. Only molecularly characterized pediatric patients were considered in the present research.

Subjects and methods: A total of 19 two-dimensional (2D) images of patients affected by several molecularly confirmed craniofacial syndromes (14 monogenic disorders and 5 chromosome diseases) and evaluated at the main involved Institution were analyzed using the Face2Gene CLINIC application (vs.19.1.3). Patients were cataloged into two main analysis groups (A, B) according to the number of clinical evaluations. Specifically, group A contained the patients evaluated more than one time, while in group B were comprised the subjects with a single clinical assesment. The algorithm's reliability was measured based on its capacity to identify the correct diagnosis as top-1 match, within the top-10 match and top-30 matches, only based on the uploaded image and not any other clinical finding or HPO terms. Failure was represented by the top-0 match.

Results: The correct diagnosis was suggested respectively in 100% (8/8) and 81% (9/11) of cases of group A and B, globally failing in 16% (3/19).

Conclusion: The tested tool resulted to be useful in identifying the facial gestalt of a heterogeneous group of syndromic disorders. This study illustrates the first Italian experience with the next generation phenotyping technology, following previous works and providing additional observations.

Keywords: Craniofacial; DeepGestalt; Malformation; Syndromes.

MeSH terms

Abnormalities, Multiple* / diagnosis
Child
Facies
Humans
Image Processing, Computer-Assisted* / methods
Reproducibility of Results
Syndrome