Scholarlink

Objectives <p>This work describes the design and methodological framework of the I-SCREEN project, which aims to develop an artificial intelligence (AI)-based infrastructure utilising optical coherence tomography (OCT) for early detection of AMD and assessment of progression risk.</p> Methods <p>The pan-European project is conducted across clinics and optometry/optician practices in six European countries. I-SCREEN encompasses seven work packages covering community-based AMD identification, clinical follow-up, AI development and project dissemination. Three interconnected clinical studies are carried out by optometry/optician practices (PYRENEES) and ophthalmology clinics (SUDETES and APENNINES).</p> Results <p>The PYRENEES study is a prospective, cross-sectional study evaluating the feasibility of detecting subclinical AMD in optometry/optician practices under ophthalmologist supervision via telemedicine. A robust screening network comprising 28 community-based optometry/optician practices and 7 ophthalmology clinics has been established. Patients with suspected non-neovascular AMD are referred to partnered&#xa0;clinics. In the hospital setting, patients with early or intermediate AMD are followed in the longitudinal SUDETES study, while patients with non-foveal geographic atrophy are invited to take part in the APENNINES study. Data obtained inform AI development for community-based AMD detection and monitoring. Predictive modelling will further enable personalised risk assessments.</p> Conclusions <p>I-SCREEN brings together multidisciplinary experts across Europe to establish an AI-driven shared care model for AMD detection and monitoring. By combining high-quality OCT imaging from community practices with longitudinal clinical studies, the initiative provides novel insights into early AMD progression and &#xa0;establishes a foundation for innovative AI-based detection and prediction throughout the real-world population.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

I-SCREEN: Development of an AI-based infrastructure for community-wide screening and prediction of progression in age-related macular degeneration providing accessible shared care

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

查看全文
  • Marie Louise Enzendorfer,
  • Gregor S. Reiter,
  • Hrvoje Bogunović,
  • Sophie Riedl,
  • Julia Mai,
  • Johannes Schrittwieser,
  • Christoph Stapf,
  • Virginia Mares,
  • Matjaž Mihelčič,
  • Julie-Anne Little,
  • Polona Jaki-Mekjavić,
  • Daniel Barthelmes,
  • Katja Hatz,
  • Javier Zarranz-Ventura,
  • Catherine Creuzot-Garcher,
  • Ruth Hogg,
  • Amir Sadeghipour,
  • Ursula Schmidt-Erfurth,
  • Matjaž Turk,
  • Klaudia Birner,
  • Anna Eidenberger,
  • Stefan Sacu,
  • Thomas Pinetz,
  • Veit Hucke,
  • Dmitrii Lachinov,
  • Taha Emre,
  • Julie-Ann Little,
  • Fabienne Eckert,
  • Peter Gumpelmayer,
  • Polona Jaki-Mekjavić,
  • Polona Zaletel Benda,
  • Fran Drnovšek,
  • Vlasta Hadalin,
  • Barbara Klemenc,
  • Darko Perovšek,
  • Katrin Rudolph,
  • Luca Timoceanu,
  • Katja Hatz,
  • Judith Buck,
  • Charlotte Monnet,
  • Christina Plasencia,
  • Katharina Heck,
  • Javier Zarranz-Ventura,
  • Andrea Mendez-Mourelle,
  • Rafael Castro-Domínguez,
  • Irene Vila,
  • Alicia Pereira,
  • Carolina Bernal-Morales,
  • Lena Giralt,
  • Josep Grau,
  • Valentina Bilbao,
  • Sonia Marías-Perez,
  • Pau Marjalizo,
  • Pierre Henry Gabrielle,
  • Laure-Anne Steinberg,
  • Tunde Peto,
  • Lucia Dalton,
  • Raymond Beirne,
  • Emma McConnell,
  • Deirdre Brannigan-Hogarth,
  • Jill McKeown,
  • Lucy Rundle,
  • Jill Telford,
  • Stephen Vandevyver,
  • Faith Donaldson,
  • Judith Long,
  • Graeme Black,
  • Vedran Hrbacek,
  • Ariadne Whitby,
  • Doruk Okcuoglu,
  • Patricia Ambichler,
  • Miriam Lenhart,
  • Sanja Šale,
  • Gabrijela Radić,
  • Lucija Rogina,
  • Slaven Rašković,
  • Nikolina Lednicki

摘要

Objectives

This work describes the design and methodological framework of the I-SCREEN project, which aims to develop an artificial intelligence (AI)-based infrastructure utilising optical coherence tomography (OCT) for early detection of AMD and assessment of progression risk.

Methods

The pan-European project is conducted across clinics and optometry/optician practices in six European countries. I-SCREEN encompasses seven work packages covering community-based AMD identification, clinical follow-up, AI development and project dissemination. Three interconnected clinical studies are carried out by optometry/optician practices (PYRENEES) and ophthalmology clinics (SUDETES and APENNINES).

Results

The PYRENEES study is a prospective, cross-sectional study evaluating the feasibility of detecting subclinical AMD in optometry/optician practices under ophthalmologist supervision via telemedicine. A robust screening network comprising 28 community-based optometry/optician practices and 7 ophthalmology clinics has been established. Patients with suspected non-neovascular AMD are referred to partnered clinics. In the hospital setting, patients with early or intermediate AMD are followed in the longitudinal SUDETES study, while patients with non-foveal geographic atrophy are invited to take part in the APENNINES study. Data obtained inform AI development for community-based AMD detection and monitoring. Predictive modelling will further enable personalised risk assessments.

Conclusions

I-SCREEN brings together multidisciplinary experts across Europe to establish an AI-driven shared care model for AMD detection and monitoring. By combining high-quality OCT imaging from community practices with longitudinal clinical studies, the initiative provides novel insights into early AMD progression and  establishes a foundation for innovative AI-based detection and prediction throughout the real-world population.