Using deep-neural-network-driven facial recognition to identify distinct Kabuki syndrome 1 and 2 gestalt

Rouxel, Flavien; Yauy, Kévin; Boursier, Guilaine; Gatinois, Vincent; Barat‐Houari, Mouna; Sanchez, Elodie; Lacombe, Didier; Arpin, Stéphanie; Giuliano, Fabienne; Haye, Damien; Rio, Marlène; Toutain, Annick; Dieterich, Klaus; Brischoux‐Boucher, Elise; Julia, Sophie; Nizon, Mathilde; Afenjar, Alexandra; Keren, Boris; Jacquette, Aurélia; Moutton, Sébastien; Jacquemont, Marie‐Line; Capri, Yline; Amiel, Jeanne; Blanchet, P.; Lyonnet, Stanislas; Sanlaville, Damien; Geneviève, David

doi:10.1038/s41431-021-00994-8

Cited by 6 publications

(8 citation statements)

References 21 publications

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“…The typical KS face includes long palpebral fissures associated with eversion of the lateral third of the lower eyelid; long and heavy lashes giving the impression of made-up eyes; broad, arched and interrupted eyebrows; broad, depressed nasal tip; and prominent, cupped ears 1 , 2 , 4 . Extra-facial features include mild to moderate intellectual disability, visceral malformations, skeletal dysplasia and immunological manifestations 5 . KS has been described in all ethnic groups 6 , 7 .…”

Section: Introductionmentioning

confidence: 99%

“…The field of dysmorphology has been changed by these new methods, under the name of Next Generation Phenotyping (NGP) 18 . Publications comparing human performances to NGP are flourishing 19 – 22 , and some suggest that digital tools do it better than human experts in terms of diagnosis: Dudding-Byth et al 23 showed a better performance of NGP compared to clinicians in a group of ten genetic syndromes, not including KS; Rouxel et al 5 compared the performance of the DeepGestalt technology 18 using the Face2Gene online tool (FDNA Inc. Boston, MA, USA) to the performances of clinicians trained in the recognition of KS1 and KS2.…”

Section: Introductionmentioning

confidence: 99%

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Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Hennocq,

Willems,

Amiel

et al. 2024

Sci Rep

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The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9–99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729–0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Hennocq,

Willems,

Amiel

et al. 2024

Sci Rep

Self Cite

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“…Once it exists, the spatial location and extent of each instance are returned [5]. Many approaches have been proposed to detect faces, parts of faces, and features of faces [6][7][8][9][10][11][12]. A novel reverse engineering approach is used to detect which facial features are critical for users' identity and to use these features as eye prints [6].…”

Section: -Introductionmentioning

confidence: 99%

“…Specifically, these systems have given significant upgrades in object identification. Profound learning has changed a wide scope of the AI era, from picture order and video handling to discourse acknowledgment and common language understanding [9,10].…”

Section: -Introductionmentioning

confidence: 99%

Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

et al. 2022

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This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PDF

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“…Telemedicine to access clinical genomics advice is one route to overcome geographical barriers. Rouxel et al report a facial recognition method to aid the differential diagnosis of Kabuki syndrome [18]. Technological advances continue to improve genomic diagnostics.…”

mentioning

confidence: 99%

2022: the year that was in the European Journal of Human Genetics

McNeill

2023

Eur J Hum Genet

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Using deep-neural-network-driven facial recognition to identify distinct Kabuki syndrome 1 and 2 gestalt

Cited by 6 publications

References 21 publications

Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

2022: the year that was in the European Journal of Human Genetics

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