Purpose: While millions of individuals show early age-related macular degeneration (AMD) signs, yet have excellent vision, the risk of progression to advanced AMD with legal blindness is highly variable. We suggest means of artificial intelligence to individually predict AMD progression. Methods: In eyes with intermediate AMD, progression to the neovascular type with choroidal neovascularization (CNV) or the dry type with geographic atrophy (GA) was diagnosed based on standardized monthly optical coherence tomography (OCT) images by independent graders. We obtained automated volumetric segmentation of outer neurosensory layers and retinal pigment epithelium, drusen, and hyperreflective foci by spectral domain-OCT image analysis. Using imaging, demographic, and genetic input features, we developed and validated a machine learning-based predictive model assessing the risk of conversion to advanced AMD. Results:Of a total of 495 eyes, 159 eyes (32%) had converted to advanced AMD within 2 years, 114 eyes progressed to CNV, and 45 to GA. Our predictive model differentiated converting versus nonconverting eyes with a performance of 0.68 and 0.80 for CNV and GA, respectively. The most critical quantitative features for progression were outer retinal thickness, hyperreflective foci, and drusen area. The features for conversion showed pathognomonic patterns that were distinctly different for the neovascular and the atrophic pathways. Predictive hallmarks for CNV were mostly drusen-centric, while GA markers were associated with neurosensory retina and age. Conclusions: Artificial intelligence with automated analysis of imaging biomarkers allows personalized prediction of AMD progression. Moreover, pathways of progression may be specific in respect to the neovascular/atrophic type.
RCT Entities:
Purpose: While millions of individuals show early age-related macular degeneration (AMD) signs, yet have excellent vision, the risk of progression to advanced AMD with legal blindness is highly variable. We suggest means of artificial intelligence to individually predict AMD progression. Methods: In eyes with intermediate AMD, progression to the neovascular type with choroidal neovascularization (CNV) or the dry type with geographic atrophy (GA) was diagnosed based on standardized monthly optical coherence tomography (OCT) images by independent graders. We obtained automated volumetric segmentation of outer neurosensory layers and retinal pigment epithelium, drusen, and hyperreflective foci by spectral domain-OCT image analysis. Using imaging, demographic, and genetic input features, we developed and validated a machine learning-based predictive model assessing the risk of conversion to advanced AMD. Results: Of a total of 495 eyes, 159 eyes (32%) had converted to advanced AMD within 2 years, 114 eyes progressed to CNV, and 45 to GA. Our predictive model differentiated converting versus nonconverting eyes with a performance of 0.68 and 0.80 for CNV and GA, respectively. The most critical quantitative features for progression were outer retinal thickness, hyperreflective foci, and drusen area. The features for conversion showed pathognomonic patterns that were distinctly different for the neovascular and the atrophic pathways. Predictive hallmarks for CNV were mostly drusen-centric, while GA markers were associated with neurosensory retina and age. Conclusions: Artificial intelligence with automated analysis of imaging biomarkers allows personalized prediction of AMD progression. Moreover, pathways of progression may be specific in respect to the neovascular/atrophic type.
Authors: Eduardo M Normando; Tim E Yap; John Maddison; Serge Miodragovic; Paolo Bonetti; Melanie Almonte; Nada G Mohammad; Sally Ameen; Laura Crawley; Faisal Ahmed; Philip A Bloom; Maria Francesca Cordeiro Journal: Expert Rev Mol Diagn Date: 2020-05-03 Impact factor: 5.225
Authors: Kathleen Romond; Minhaj Alam; Sasha Kravets; Luis de Sisternes; Theodore Leng; Jennifer I Lim; Daniel Rubin; Joelle A Hallak Journal: Exp Biol Med (Maywood) Date: 2021-08-18
Authors: Patricia T A Bui; Gregor S Reiter; Maria Fabianska; Sebastian M Waldstein; Christoph Grechenig; Hrvoje Bogunovic; Mustafa Arikan; Ursula Schmidt-Erfurth Journal: Eye (Lond) Date: 2021-08-16 Impact factor: 4.456
Authors: Jason Yim; Reena Chopra; Terry Spitz; Jim Winkens; Annette Obika; Christopher Kelly; Harry Askham; Marko Lukic; Josef Huemer; Katrin Fasler; Gabriella Moraes; Clemens Meyer; Marc Wilson; Jonathan Dixon; Cian Hughes; Geraint Rees; Peng T Khaw; Alan Karthikesalingam; Dominic King; Demis Hassabis; Mustafa Suleyman; Trevor Back; Joseph R Ledsam; Pearse A Keane; Jeffrey De Fauw Journal: Nat Med Date: 2020-05-18 Impact factor: 53.440
Authors: Matthias Gutfleisch; Oliver Ester; Sökmen Aydin; Martin Quassowski; Georg Spital; Albrecht Lommatzsch; Kai Rothaus; Adam Michael Dubis; Daniel Pauleikhoff Journal: Graefes Arch Clin Exp Ophthalmol Date: 2022-01-22 Impact factor: 3.117
Authors: Nazlee Zebardast; Sayuri Sekimitsu; Jiali Wang; Tobias Elze; Puya Gharahkhani; Brian S Cole; Michael M Lin; Ayellet V Segrè; Janey L Wiggs Journal: Ophthalmology Date: 2021-03-10 Impact factor: 14.277