PEDIA: prioritization of exome data by image analysis

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

doi:10.1038/s41436-019-0566-2

PEDIA: prioritization of exome data by image analysis

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

53 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	2807-2814
Number of pages	8
Journal	Genetics in Medicine
Volume	21
Issue number	12
DOIs	https://doi.org/10.1038/s41436-019-0566-2
State	Published - 1 Dec 2019
Externally published	Yes

Funding

Funders	Funder number
Berlin Institute of Health
Charité–Universitätsmedizin Berlin
Deutsche Forschungsgemeinschaft	KR 3985/6-1, KR 3985/7-3
Deutsche Forschungsgemeinschaft

Keywords

computer vision
deep learning
dysmorphology
exome diagnostics
variant prioritization

Access to Document

10.1038/s41436-019-0566-2

Cite this

Hsieh, T. C., Mensah, M. A., Pantel, J. T., Aguilar, D., Bar, O., Bayat, A., Becerra-Solano, L., Bentzen, H. B., Biskup, S., Borisov, O., Braaten, O., Ciaccio, C., Coutelier, M., Cremer, K., Danyel, M., Daschkey, S., Eden, H. D., Devriendt, K., Wilson, S., ... Krawitz, P. M. (2019). PEDIA: prioritization of exome data by image analysis. Genetics in Medicine, 21(12), 2807-2814. https://doi.org/10.1038/s41436-019-0566-2

@article{8be2612e46004a3d8be5b4c664edb21e,

title = "PEDIA: prioritization of exome data by image analysis",

abstract = "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.",

keywords = "computer vision, deep learning, dysmorphology, exome diagnostics, variant prioritization",

author = "Hsieh, {Tzung Chien} and Mensah, {Martin A.} and Pantel, {Jean T.} and Dione Aguilar and Omri Bar and Allan Bayat and Luis Becerra-Solano and Bentzen, {Heidi B.} and Saskia Biskup and Oleg Borisov and Oivind Braaten and Claudia Ciaccio and Marie Coutelier and Kirsten Cremer and Magdalena Danyel and Svenja Daschkey and Eden, {Hilda David} and Koenraad Devriendt and Sandra Wilson and Sofia Douzgou and Dejan {\D}uki{\'c} and Nadja Ehmke and Christine Fauth and Bj{\"o}rn Fischer-Zirnsak and Nicole Fleischer and Heinz Gabriel and Luitgard Graul-Neumann and Gripp, {Karen W.} and Yaron Gurovich and Asya Gusina and Nechama Haddad and Nurulhuda Hajjir and Yair Hanani and Jakob Hertzberg and Konstanze Hoertnagel and Janelle Howell and Ivan Ivanovski and Angela Kaindl and Tom Kamphans and Susanne Kamphausen and Catherine Karimov and Hadil Kathom and Anna Keryan and Alexej Knaus and Sebastian K{\"o}hler and Uwe Kornak and Alexander Lavrov and Maximilian Leitheiser and Lyon, {Gholson J.} and Elisabeth Mangold and Reina, {Purificaci{\'o}n Mar{\'i}n} and Carrascal, {Antonio Martinez} and Diana Mitter and Herrador, {Laura Morlan} and Guy Nadav and Markus N{\"o}then and Alfredo Orrico and Ott, {Claus Eric} and Kristen Park and Borut Peterlin and Laura P{\"o}lsler and Annick Raas-Rothschild and Linda Randolph and Nicole Revencu and Fagerberg, {Christina Ringmann} and Robinson, {Peter Nick} and Stanislav Rosnev and Sabine Rudnik and Gorazd Rudolf and Ulrich Schatz and Anna Schossig and Max Schubach and Or Shanoon and Eamonn Sheridan and Pola Smirin-Yosef and Malte Spielmann and Suk, {Eun Kyung} and Yves Sznajer and Thiel, {Christian T.} and Gundula Thiel and Alain Verloes and Irena Vrecar and Dagmar Wahl and Ingrid Weber and Korina Winter and Marzena Wi{\'s}niewska and Bernd Wollnik and Yeung, {Ming W.} and Max Zhao and Na Zhu and Johannes Zschocke and Stefan Mundlos and Denise Horn and Krawitz, {Peter M.}",

note = "Publisher Copyright: {\textcopyright} 2019, The Author(s).",

year = "2019",

month = dec,

day = "1",

doi = "10.1038/s41436-019-0566-2",

language = "אנגלית",

volume = "21",

pages = "2807--2814",

journal = "Genetics in Medicine",

issn = "1098-3600",

publisher = "Lippincott Williams and Wilkins Ltd.",

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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, CT, 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 2019, 'PEDIA: prioritization of exome data by image analysis', Genetics in Medicine, vol. 21, no. 12, pp. 2807-2814. https://doi.org/10.1038/s41436-019-0566-2

TY - JOUR

T1 - PEDIA

T2 - prioritization of exome data by image analysis

AU - Hsieh, Tzung Chien

AU - Mensah, Martin A.

AU - Pantel, Jean T.

AU - Aguilar, Dione

AU - Bar, Omri

AU - Bayat, Allan

AU - Becerra-Solano, Luis

AU - Bentzen, Heidi B.

AU - Biskup, Saskia

AU - Borisov, Oleg

AU - Braaten, Oivind

AU - Ciaccio, Claudia

AU - Coutelier, Marie

AU - Cremer, Kirsten

AU - Danyel, Magdalena

AU - Daschkey, Svenja

AU - Eden, Hilda David

AU - Devriendt, Koenraad

AU - Wilson, Sandra

AU - Douzgou, Sofia

AU - Đukić, Dejan

AU - Ehmke, Nadja

AU - Fauth, Christine

AU - Fischer-Zirnsak, Björn

AU - Fleischer, Nicole

AU - Gabriel, Heinz

AU - Graul-Neumann, Luitgard

AU - Gripp, Karen W.

AU - Gurovich, Yaron

AU - Gusina, Asya

AU - Haddad, Nechama

AU - Hajjir, Nurulhuda

AU - Hanani, Yair

AU - Hertzberg, Jakob

AU - Hoertnagel, Konstanze

AU - Howell, Janelle

AU - Ivanovski, Ivan

AU - Kaindl, Angela

AU - Kamphans, Tom

AU - Kamphausen, Susanne

AU - Karimov, Catherine

AU - Kathom, Hadil

AU - Keryan, Anna

AU - Knaus, Alexej

AU - Köhler, Sebastian

AU - Kornak, Uwe

AU - Lavrov, Alexander

AU - Leitheiser, Maximilian

AU - Lyon, Gholson J.

AU - Mangold, Elisabeth

AU - Reina, Purificación Marín

AU - Carrascal, Antonio Martinez

AU - Mitter, Diana

AU - Herrador, Laura Morlan

AU - Nadav, Guy

AU - Nöthen, Markus

AU - Orrico, Alfredo

AU - Ott, Claus Eric

AU - Park, Kristen

AU - Peterlin, Borut

AU - Pölsler, Laura

AU - Raas-Rothschild, Annick

AU - Randolph, Linda

AU - Revencu, Nicole

AU - Fagerberg, Christina Ringmann

AU - Robinson, Peter Nick

AU - Rosnev, Stanislav

AU - Rudnik, Sabine

AU - Rudolf, Gorazd

AU - Schatz, Ulrich

AU - Schossig, Anna

AU - Schubach, Max

AU - Shanoon, Or

AU - Sheridan, Eamonn

AU - Smirin-Yosef, Pola

AU - Spielmann, Malte

AU - Suk, Eun Kyung

AU - Sznajer, Yves

AU - Thiel, Christian T.

AU - Thiel, Gundula

AU - Verloes, Alain

AU - Vrecar, Irena

AU - Wahl, Dagmar

AU - Weber, Ingrid

AU - Winter, Korina

AU - Wiśniewska, Marzena

AU - Wollnik, Bernd

AU - Yeung, Ming W.

AU - Zhao, Max

AU - Zhu, Na

AU - Zschocke, Johannes

AU - Mundlos, Stefan

AU - Horn, Denise

AU - Krawitz, Peter M.

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

AB - 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.

KW - computer vision

KW - deep learning

KW - dysmorphology

KW - exome diagnostics

KW - variant prioritization

UR - http://www.scopus.com/inward/record.url?scp=85066983034&partnerID=8YFLogxK

U2 - 10.1038/s41436-019-0566-2

DO - 10.1038/s41436-019-0566-2

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

C2 - 31164752

AN - SCOPUS:85066983034

SN - 1098-3600

VL - 21

SP - 2807

EP - 2814

JO - Genetics in Medicine

JF - Genetics in Medicine

IS - 12

ER -

PEDIA: prioritization of exome data by image analysis

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