Performance of Facial Dysmorphology Novel AnalysisFigure 1. FDNA image analysis pipeline Figure 2....
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L. Basel-Vanagaite 1,2,3 S. Lyonnet 4 , V. Cormier-Daire 4 , M. Rio 4 , J. Amiel 4 , L. Wolf 2,3 1 Schneider Children's Medical Center and Beilinson Hospital, Rabin Medical Center, Felsenstein Medical Research Center, Petah Tikva, Israel; 2 Tel Aviv University, Tel Aviv, Israel; 3 FDNA, Boston, MA, USA; 4 Université 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 analyzing two dimensional facial photos. We evaluated the technology’s ability to retrieve a gestalt-based list of syndromes that matches molecularly confirmed diagnoses of individuals. RESULTS The molecularly confirmed diagnosis was listed in the first 20 matches for 89% of the cases, 10 for 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 ran permutation tests. Mixing the images and the diagnosis randomly, the rank 1 retrieval is less than 5%. The best results were observed for SHORT Syndrome, Kaufman Oculocerebrofacial Syndrome and Auriculo-condylar Syndrome 1 (100% correct 1st match). For microdeletion syndromes, the best performance was observed for Angelman Syndrome (listed in the first 5 matches 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 syndromes were analyzed. The images were submitted by clinical geneticists using the Face2Gene mobile application. Each image received a list of syndromes ranked by similarity. The image analysis pipeline consists of five steps as depicted in Fig. 1. First, the frame of the face is located using Haar based cascaded face detection algorithm. Then within the detection frame, 130 fiducial facial points are located using similar local image detectors trained for each one of the points individually. From the anatomical points located, various local properties such as ratios of distances and local image descriptors of the face are computed. These measurements are used in combination with statistical models called Bayesian networks in order to indicate the presence of dysmorphic features and to evaluate the extent of similarity to the gestalt associated with each of a multitude of genetic syndromes for which the system is trained to identify. In addition, local image information is integrated in order to provide the appearance or "gestalt" description of the face. Specifically, vectors of local binary patterns are used to capture the appearance of the entire face. 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 investigating genetic 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. 2486T 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 Submit image Detect face Localize facial feature points Estimate local properties 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Top 2 matches Top match Top 5 matches Top 10 matches Top 20 matches
Transcript of Performance of Facial Dysmorphology Novel AnalysisFigure 1. FDNA image analysis pipeline Figure 2....
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.
2486T
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
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