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<p>Rare eye diseases such as inherited retinal diseases (IRDs) are challenging to diagnose genetically. IRDs are typically monogenic disorders and represent a leading cause of blindness in children and working-age adults worldwide. A growing number are now being targeted in clinical trials, with approved treatments increasingly available. However, access requires a genetic diagnosis to be established sufficiently early. Critically, the timely identification of a genetic cause remains challenging. We demonstrate that a deep learning algorithm, Eye2Gene, trained on a large multimodal imaging dataset of individuals with IRDs (<i>n</i> = 2,451) and externally validated on data provided by five different clinical centres, provides better-than-expert-level top-five accuracy of 83.9% for supporting genetic diagnosis for the 63 most common genetic causes. We demonstrate that Eye2Gene’s next-generation phenotyping can increase diagnostic yield by improving screening for IRDs, phenotype-driven variant prioritization and automatic similarity matching in phenotypic space to identify new genes. Eye2Gene is accessible online (<a href="http://app.eye2gene.com">app.eye2gene.com</a>) for research purposes.</p>

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Next-generation phenotyping of inherited retinal diseases from multimodal imaging with Eye2Gene

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月18日

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  • Nikolas Pontikos,
  • William A. Woof,
  • Siying Lin,
  • Biraja Ghoshal,
  • Bernardo S. Mendes,
  • Advaith Veturi,
  • Quang Nguyen,
  • Behnam Javanmardi,
  • Michalis Georgiou,
  • Alexander Hustinx,
  • Miguel A. Ibarra-Arellano,
  • Ismail Moghul,
  • Yichen Liu,
  • Kristina Pfau,
  • Maximilian Pfau,
  • Mital Shah,
  • Jing Yu,
  • Saoud Al-Khuzaei,
  • Siegfried K. Wagner,
  • Malena Daich Varela,
  • Thales Antonio Cabral de Guimarães,
  • Sagnik Sen,
  • Gunjan Naik,
  • Dayyanah Sumodhee,
  • Dun Jack Fu,
  • Nathaniel Kabiri,
  • Jennifer Furman,
  • Bart Liefers,
  • Aaron Y. Lee,
  • Samantha R. De Silva,
  • Caio Marques,
  • Fabiana Motta,
  • Yu Fujinami-Yokokawa,
  • Alison J. Hardcastle,
  • Gavin Arno,
  • Birgit Lorenz,
  • Philipp Herrmann,
  • Kaoru Fujinami,
  • Juliana Sallum,
  • Savita Madhusudhan,
  • Susan M. Downes,
  • Frank G. Holz,
  • Konstantinos Balaskas,
  • Andrew R. Webster,
  • Omar A. Mahroo,
  • Peter M. Krawitz,
  • Michel Michaelides

摘要

Rare eye diseases such as inherited retinal diseases (IRDs) are challenging to diagnose genetically. IRDs are typically monogenic disorders and represent a leading cause of blindness in children and working-age adults worldwide. A growing number are now being targeted in clinical trials, with approved treatments increasingly available. However, access requires a genetic diagnosis to be established sufficiently early. Critically, the timely identification of a genetic cause remains challenging. We demonstrate that a deep learning algorithm, Eye2Gene, trained on a large multimodal imaging dataset of individuals with IRDs (n = 2,451) and externally validated on data provided by five different clinical centres, provides better-than-expert-level top-five accuracy of 83.9% for supporting genetic diagnosis for the 63 most common genetic causes. We demonstrate that Eye2Gene’s next-generation phenotyping can increase diagnostic yield by improving screening for IRDs, phenotype-driven variant prioritization and automatic similarity matching in phenotypic space to identify new genes. Eye2Gene is accessible online (app.eye2gene.com) for research purposes.