Deciphering age-related macular degeneration by deep phenotyping and machine learning


  • Prof Andrew Lotery

    University of Southampton

  • Prof Toby Prevost

    Imperial College London

  • Prof Daniel Rueckert

    Imperial College London

  • Prof Hendrik Scholl

    University of Basel

  • Prof Sobha Sivaprasad

    University College London

  • Dr Lars Fritsche

    University of Michigan

  • Prof Ursula Schmidt-Erfurth

    Medical University of Vienna

  • Dr Sebastian Waldstein

    Medical University of Vienna

Project summary

Age-related macular degeneration (AMD) is a very common cause of blindness. It is difficult to predict who will progress to the stage of the disease where eyesight is threatened as some patients progress slowly or not at all and others progress quickly.

We can teach computers to analyse high resolution images of the inside of the eye. We have access to hundreds of thousands of such images from patients with AMD as well as those who don’t. These images will allow us to train computers to identify what eye changes appear in patients with AMD. Once the computers have learnt this, we expect they will identify new changes.

This approach will enable us to better predict the patients who are most at risk of sight loss. This should help us develop better treatments and enter the most appropriate patients into clinical trials. It should also allow us to better understand why AMD develops.