Künstliche Intelligenz in der Augenheilkunde

Association of Reading Performance in Geographic Atrophy Secondary to Age-Related Macular Degeneration With Visual Function and Structural Biomarkers

“…32 Nested patient-based cross-validation was applied to estimate prediction accuracy (outer leave-1-out crossvalidation), while simultaneously optimizing the tuning parameter lambda of the LASSO regression (nested inner leave-1-out cross-validation). 33 For comparison, we present the results of a conventional (least-squares) cross-sectional multivariable regression analysis (eFigure 2 in the Supplement). Variables were selected using stepwise backward selection based on the value of the F statistic.…”

Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology

“…In total, 650 articles met the search criteria, of which 17 were reported specific guideline recommendations. 6,8,12,[17][18][19][20][21][22][23][24][25][26][27][28][29][30] An additional 34 articles were suggested by the expert panel as specific to factors that influence AI diagnosis, which informed development of the criteria.…”

AI-based structure-function correlation in age-related macular degeneration

“…In the context of AMD, inference of BCVA from OCT images has been proposed predominantly for macular neovascularisation (Table 1 ) [ 23 , 27 – 31 ]. During anti-VEGF treatment, retinal imaging plays a pivotal role in disease management, with patients being regularly monitored by OCT.…”

“… Author/Ref. Title Disease Technique Prediction Outcome measure Outcome Rohm et al [ 27 ] Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration Neovascular age-related macular degeneration • Five different machine-learning algorithms • Best performance by Lasso regression • logMAR visual acuity after 3- and 12 months • Mean Absolute Error (MAE) • Root Mean Sqaured Error (RMSE) • 3 Months = MAE: 0.11–014/RMSE: 0.18–0.2 • 12 Months = MAE: 0.16–0.2/RMSE: 0.2–0.22 Schmidt-Erfurth et al [ 28 ] Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration Neovascular age-related macular degeneration • Random forest • BCVA at Baseline and 3 months follow-up • Accuracy (R 2 ) • R 2 = 0.21 baseline • R 2 = 0.70 3 months Gerenda et al [ 29 ] Computational image analysis for prognosis determination in DME Diabetic macular edema • Random forest • BCVA at Baseline and 1-year follow-up • Accuracy (R 2 ) • R 2 = 0.21 baseline • R 2 = 0.23 1 year Aslam et al [ 30 ] Use of a neural net to model the impact of optical coherence tomography abnormalities on vision in age-related macular degeneration Neovascular age-related macular degeneration • Scaled conjugate gradient backpropagation (supervised learning) • BCVA • Root Mean Sqaured Error (RMSE) • 8.21 Letters Pfau et al [ 31 ] Artificial intelligence in ophthalmology: guideline for physicians for the critical evaluation of studies Neovascular age-related macular degeneration • Nested cross validation • BCVA (LogMAR) • MAE • 0.142 Müller et al [ 23 ] Prediction of function in ABCA4-related retinopathy using ensemble machine learning ABCA4-related Retinopathy • Ensemble machine learning algorithms • Three models...…”