L. Basel-Vanagaite 1,2,3 S. Lyonnet4, V. Cormier-Daire4, M. Rio4, J. Amiel4, L. Wolf2,3

1 Schneider Children's Medical Center and Beilinson Hospital, Rabin Medical Center, Felsenstein Medical Research Center, Petah Tikva, Israel;2Tel Aviv University, Tel Aviv, Israel; 3FDNA, Boston, MA, USA; 4Université Paris Descartes-Sorbonne, Imagine Institute, Hôpital Necker

Enfants Malades, Paris, France

Performance of Facial Dysmorphology Novel Analysis: comparison to molecular testing-based diagnostics

INTRODUCTION

Facial Dysmorphology Novel Analysis (FDNA) technology automatically identifies facial patterns associated with genetic syndromes by analyzingtwo dimensional facial photos. We evaluated the technology’s ability to retrieve a gestalt-based list of syndromes that matches molecularlyconfirmed diagnoses of individuals.

RESULTS

The molecularly confirmed diagnosis was listed in the first 20 matches for 89% of the cases, 10for 75% of the cases, 5 for 69% of the cases, 2 for 54% of the cases and as the first result for 40%of the cases (Figure 2). Statistically, the chances of obtaining such results are virtually zero(p<1e-100). However, since some syndromes are more common than others, we ranpermutation tests. Mixing the images and the diagnosis randomly, the rank 1 retrieval is lessthan 5%. The best results were observed for SHORT Syndrome, Kaufman OculocerebrofacialSyndrome and Auriculo-condylar Syndrome 1 (100% correct 1st match). For microdeletionsyndromes, the best performance was observed for Angelman Syndrome (listed in the first 5matches in 93% of the cases) as compared to Williams Syndrome and 22q11.2 Syndrome (86%and 67%, respectively.) (Fig. 3)

METHODS

331 frontal images of Caucasian children with 43 different molecularly confirmed syndromeswere analyzed. The images were submitted by clinical geneticists using the Face2Gene mobileapplication. Each image received a list of syndromes ranked by similarity. The image analysispipeline consists of five steps as depicted in Fig. 1. First, the frame of the face is located usingHaar based cascaded face detection algorithm. Then within the detection frame, 130 fiducialfacial points are located using similar local image detectors trained for each one of the pointsindividually. From the anatomical points located, various local properties such as ratios ofdistances and local image descriptors of the face are computed. These measurements are used incombination with statistical models called Bayesian networks in order to indicate the presence ofdysmorphic features and to evaluate the extent of similarity to the gestalt associated with each ofa multitude of genetic syndromes for which the system is trained to identify. In addition, localimage information is integrated in order to provide the appearance or "gestalt" description of theface. Specifically, vectors of local binary patterns are used to capture the appearance of the entireface. Lastly, a mask depicting the characteristic appearance of each syndrome is created. (Fig. 3)

CONCLUSIONS

We conclude that a system supported with FDNA technology can be useful for medical professionals researching and investigatinggenetic syndromes.Future applications include its usage to:1) guide targeted molecular testing;2) complement next generation sequencing-based molecular testing by inferring causative genetic variants from sequencing data.

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Figure 1. FDNA image analysis pipeline

Figure 2. Percentage of cases with correct diagnosis displayed in top 20, 10, 5,

2 and 1 syndrome matches

Figure 3. Comparison of percentage of microdeletion syndrome with correct diagnosis displayed in top 5 syndrome matches. Mask corresponding to each of the syndromes is presented.

Angelman Syndrome Williams-Beuren Syndrome 22q11.2 Syndrome

In 93% of the cases listed within top 5 matches

In 86% of the cases listed within top 5 matches

In 67% of the cases listed within top 5 matches

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