PEDIA: prioritization of exome data by image analysis

Hsieh, Tzung-Chien; Mensah, Martin A.; Pantel, Jean Tori; Aguilar, Dione; Bar, Omri; Bayat, Allan; Becerra-Solano, Luis Eduardo; Bentzen, Heidi Beate; Biskup, Saskia; Borisov, Oleg; Braaten, Øivind; Ciaccio, Claudia; Coutelier, Marie; Cremer, Kirsten; Danyel, Magdalena; Daschkey, Svenja; Eden, Hilda David; Devriendt, Koenraad; Wilson, Sandra; Douzgou, Sofia; Đukić, Dejan; Ehmke, Nadja; Fauth, Christine; Fischer-Zirnsak, Björn; Fleischer, Nicole; Gabriel, Heinz; Graul‐Neumann, Luitgard; Gripp, Karen W.; Gurovich, Yaron; Gusina, A. A.; Haddad, Nechama; Hajjir, Nurulhuda; Hanani, Yair; Hertzberg, Jakob; Hoertnagel, Konstanze; Howell, Janelle; Ivanovski, Ivan; Kaindl, Angela M.; Kamphans, Tom; Kamphausen, Susanne; Karimov, Catherine; Kathom, Hadil; Keryan, Anna; Knaus, Alexej; Köhler, Sebastian; Kornak, Uwe; Лавров, А. В.; Leitheiser, Maximilian; Lyon, Gholson J.; Mangold, Elisabeth; Reina, Purificación Marín; Carrascal, Antonio Martinez; Mitter, Diana; Herrador, Laura Morlan; Nadav, Guy; Nöthen, Markus M.; Orrico, Alfredo; Ott, Claus‐Eric; Park, Kristen; Peterlin, Borut; Pölsler, Laura; Raas‐Rothschild, Annick; Randolph, Linda M.; Revençu, Nicole; Fagerberg, Christina; Robinson, Peter N.; Rosnev, Stanislav; Rudnik, Sabine; Rudolf, G. de M.; Schatz, Ulrich; Schossig, Anna; Schubach, Max; Shanoon, Or; Sheridan, Eamonn; Smirin‐Yosef, Pola; Spielmann, Malte; Suk, Eun Kyung; Sznajer, Yves; Thiel, Christian T.; Thiel, Gundula; Verloès, Alain; Vrečar, Irena; Wahl, Dagmar; Weber, Ingrid; Winter, Korina; Wiśniewska, Marzena; Wollnik, Bernd; Yeung, Ming Wai; Zhao, Max; Zhu, Na; Zschocke, Johannes; Mundlos, Stefan; Horn, Denise; Krawitz, Peter

doi:10.1038/s41436-019-0566-2

Cited by 70 publications

(47 citation statements)

References 22 publications

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“…Studies of more recent versions of DeepGestalt suggested that ethnicity had no major influence on its sensitivity [ 26 , 29 ]. In our set of syndromic images, DeepGestalt’s sensitivity is remarkably high, which is in line with the previous studies highlighting DeepGestalt’s good general sensitivity [ 11 , 36 , 42 ]. This high sensitivity of DeepGestalt was confirmed for both groups of images, those of White persons and those of persons of other ethnicities.…”

Section: Discussionsupporting

confidence: 92%

See 1 more Smart Citation

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Pantel¹,

Hajjir²,

Danyel³

et al. 2020

J Med Internet Res

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Background Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.

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Section: Discussionsupporting

confidence: 92%

“…No other phenotypic information but 1 portrait photo per case was entered into the system. DeepGestalt's training set was tested not to contain duplicates of images used in this study, as described previously [ 42 ].…”

Section: Methodsmentioning

confidence: 99%

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Pantel¹,

Hajjir²,

Danyel³

et al. 2020

J Med Internet Res

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“…This ingenious approach (called prioritization of exome data by image analysis-PEDIA) was carried out in a large cohort of patients with monogenetic RDs. The authors show that the addition of phenotypic data significantly improved correct disease-causing gene prediction, particularly when deep learning face recognition (with Face2Gene) was encompassed [52].…”

Section: Imaging-based Ddssmentioning

confidence: 99%

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Brasil

Pascoal

Francisco

et al. 2019

Genes

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The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

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“…Studies have shown that facial analysis technologies measured up to the capabilities of expert clinicians in recognizing various developmental and genetic disorders [1][2][3][4]. Clinical dysmorphologists have developed their skill of recognizing a genetic syndrome based on the gestalt, gradually over the years with experience.…”

Section: Next Generation Clinical Practice -It's Man Versusmentioning

confidence: 99%