Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence

• Published in: Investigative ophthalmology & visual science 2018-07, Vol.59 (8), p.3199-3208
• Main Authors: Schmidt-Erfurth, Ursula, Waldstein, Sebastian M, Klimscha, Sophie, Sadeghipour, Amir, Hu, Xiaofeng, Gerendas, Bianca S, Osborne, Aaron, Bogunovic, Hrvoje
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Artificial Intelligence; Choroidal Neovascularization - diagnosis; Disease Progression; Double-Blind Method; Female; Geographic Atrophy - diagnosis; Humans; Image Interpretation, Computer-Assisted - methods; Machine Learning; Male; Middle Aged; Prospective Studies; Retinal Drusen - diagnosis; Tomography, Optical Coherence - methods; Wet Macular Degeneration - diagnosis
• ISSN: 1552-5783; 1552-5783
• DOI: 10.1167/iovs.18-24106

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. 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. 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. 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.