PEDIA: prioritization of exome data by image analysis

Journal article

Publication Details

Author(s): Hsieh TC, Mensah MA, Pantel JT, Aguilar D, Bar O, Bayat A, Becerra-Solano L, Bentzen HB, Biskup S, Borisov O, Braaten O, Ciaccio C, Coutelier M, Cremer K, Danyel M, Daschkey S, Eden HD, Devriendt K, Wilson S, Douzgou S, Đukić D, Ehmke N, Fauth C, Fischer-Zirnsak B, Fleischer N, Gabriel H, Graul-Neumann L, Gripp KW, Gurovich Y, Gusina A, Haddad N, Hajjir N, Hanani Y, Hertzberg J, Hoertnagel K, Howell J, Ivanovski I, Kaindl A, Kamphans T, Kamphausen S, Karimov C, Kathom H, Keryan A, Knaus A, Köhler S, Kornak U, Lavrov A, Leitheiser M, Lyon GJ, Mangold E, Reina PM, Carrascal AM, Mitter D, Herrador LM, Nadav G, Nöthen M, Orrico A, Ott CE, Park K, Peterlin B, Pölsler L, Raas-Rothschild A, Randolph L, Revencu N, Fagerberg CR, Robinson PN, Rosnev S, Rudnik S, Rudolf G, Schatz U, Schossig A, Schubach M, Shanoon O, Sheridan E, Smirin-Yosef P, Spielmann M, Suk EK, Sznajer Y, Thiel C, Thiel G, Verloes A, Vrecar I, Wahl D, Weber I, Winter K, Wiśniewska M, Wollnik B, Yeung MW, Zhao M, Zhu N, Zschocke J, Mundlos S, Horn D, Krawitz PM
Journal: Genetics in Medicine
Publication year: 2019
ISSN: 1098-3600


Purpose: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.

FAU Authors / FAU Editors

Thiel, Christian PD Dr.
Medizinische Fakultät

Additional Organisation
Humangenetisches Institut

External institutions with authors

Ariel University
Azienda ospedaliero-universitaria Senese
Azienda Unità Sanitaria Locale die Reggio Emilia
Berliner Institut für Gesundheitsforschung (BIH)
CeGaT GmbH
Center for Human Genetics and Laboratory Diagnostics (AHC)
Center for Prenatal Diagnosis and Human Genetics
Charité - Universitätsmedizin Berlin
Children's Hospital Los Angeles
Cliniques universitaires Saint-Luc (CHU St-Luc)
Cold Spring Harbor Laboratory
Eberhard Karls Universität Tübingen
Foundation of the Carlo Besta Neurological Institute (IRCCS)
GeneTalk GmbH
Gertner Institute
Health Innovation Manchester
Heinrich-Heine-Universität Düsseldorf
Hôpital universitaire Robert-Debré
Hospital de Requena
Hospital General Universitari de València
Hospital Universitario Miguel Servet
Jackson Laboratory for Genomic Medicine
Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven
Lineagen, Inc.
Ljubljana University Medical Centre (Ljubljana UMC) / Univerzitetni klinični center Ljubljana
Medical University Sofia / Медицински университет
Medizinische Universität Innsbruck
Nemours/Alfred I. duPont Hospital for Children
Odense Universitetshospital (OUH)
Poznan University of Medical Sciences / Uniwersytet Medyczny im. Karola Marcinkowskiego w Poznaniu
Rheinische Friedrich-Wilhelms-Universität Bonn
Russian Academy of Medical Sciences
State Republican Scientific and Practical Center "Mother and Child" / ДУ Рэспубліканскі навукова-практычны цэнтр "Маці і дзіця"
Tecnológico de Monterrey (ITESM)
UIM Unidad de Investigación Médica en Epidemiología Clínica
Universitätsklinikum Bonn
Universitätsklinikum Göttingen
Universitätsklinikum Hamburg-Eppendorf
Universitätsklinikum Leipzig
Universitätsklinikum Magdeburg A.ö.R.
Université Catholique de Louvain (UCL)
University of Colorado
University of Leeds
University of Oslo

How to cite

Hsieh, T.C., Mensah, M.A., Pantel, J.T., Aguilar, D., Bar, O., Bayat, A.,... Krawitz, P.M. (2019). PEDIA: prioritization of exome data by image analysis. Genetics in Medicine.

Hsieh, Tzung Chien, et al. "PEDIA: prioritization of exome data by image analysis." Genetics in Medicine (2019).


Last updated on 2019-26-06 at 11:08