Progression to exudative ‘wet’ age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the second eye. By combining models based on three-dimensional (3D) optical coherence tomography images and corresponding automatic tissue maps, our system predicts conversion to exAMD within a clinically actionable 6-month time window, achieving a per-volumetric-scan sensitivity of 80% at 55% specificity, and 34% sensitivity at 90% specificity. This level of performance corresponds to true positives in 78% and 41% of individual eyes, and false positives in 56% and 17% of individual eyes at the high sensitivity and high specificity points, respectively. Moreover, we show that automatic tissue segmentation can identify anatomical changes before conversion and high-risk subgroups. This AI system overcomes substantial interobserver variability in expert predictions, performing better than five out of six experts, and demonstrates the potential of using AI to predict disease progression.
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The clinical data used for the training, validation and test sets were collected at Moorfields Eye Hospital NHS Foundation Trust and transferred to DeepMind in a de-identified format. Data were used with both local and national permissions. They are not publicly available and restrictions apply to their use. The data, or a test subset, may be available from Moorfields Eye Hospital NHS Foundation Trust subject to local and national ethical approvals. Moorfields Eye Hospital NHS Foundation Trust intends to make the raw data shared with DeepMind openly available to researchers as part of the Ryan Initiative for Macular Research (http://rimr.doheny.org/).
We made use of several open-source libraries to conduct our experiments, namely the machine learning framework TensorFlow (https://github.com/tensorflow/tensorflow) along with the TensorFlow library Sonnet (https://github.com/deepmind/sonnet), which provides implementations of individual model components53. For image augmentation we used the multidimension image augmentation library previously open sourced by DeepMind (https://github.com/deepmind/multidim-image-augmentation). The model architecture source code is available from (https://github.com/google-health/imaging-research). Other aspects of the experimental system made use of proprietary libraries and we are unable to publicly release this code. We detail the experiments and implementation details in Methods and in the Supplementary figures to allow for independent replication.
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We thank the patients under the care of Moorfields Eye Hospital. We would also like to thank B. Romera-Paredes, O. Ronneberger, N. Tomasev, S. Blackwell, J. Schrouff, M. Chesus, C. Cooper, V. Cornelius, A. Khawaja, R. Ahamed, T. Corkett, R. Ogbe, Y. Ibitoye, M. Bawn, J. Besley, C. Meaden, C. Chorley, S. Rowley, A. Ahmad, K. Clancy, C. Semturs, A. Varadarajan, B. Babenko, I. Traynis, Y. Liu, L. Peng, N. Hammel, K. Blumer, K. Kavukcuoglu, S. Bouton, G. Corrado, E. Manna, A. C. Bird, W. Tucker, Y. Obadeyi, Z. Jessa, D. Sim, M. Natkunarajah and A. Jindal. P.A.K. is supported by an NIHR Clinician Scientist Award (no. NIHR-CS-2014-14-023). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. R.C. receives studentship support from the College of Optometrists, United Kingdom.
P.A.K. and G.R. are paid contractors of DeepMind. P.A.K. has received speaker fees from Heidelberg Engineering, Topcon, Haag-Streit, Allergan, Novartis and Bayer. P.A.K. has served on advisory boards for Novartis and Bayer, and is an external consultant for DeepMind and Optos. M.L. received travel grants and a speaker fee from Bayer.
Peer review information Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
A breakdown of training (60%), validation (20%) and test (20%) datasets by unique patients and unique scans.
a, Histogram of scans per unique eye (pre-conversion) in training/validation set and test set. b, Histogram of difference between conversion and injection date for fellow eyes in training and test set (n = 537, 214 have matching dates).
ROC curves for model predictions over time windows of a, 3 months, b, 6 months, c, 12 months, and d, 24 months. Note that the difference in AUC between 12 and 24 month predictions is not statistically significant (p-value = 0.54, two-sided permutation test).
Confusion matrices for the prediction decision for all 6 experts for the single scan and sequential scan tasks, and for the system at two chosen operating points. n = 1053 trials (380 unique patients).
a, Metrics for each expert for the single scan and sequential scan tasks. Intra-observer agreement was assessed using Fleiss’ Kappa. (PPV: positive predictive value, NPV: negative predictive value). b, Agreement between the clinical experts for the single and sequential tasks, measured using Fleiss’ Kappa. N = 1053 for both single and sequential task.
Extended Data Fig. 6 Aggregate volumes and volume change per 3 months before conversion for major ocular structures and abnormal tissues in patients who converted (n = 549 unique patients).
The box extends from the lower to upper quartile values of the data, with an orange line at the median. The whiskers show the 5th & 95th percentiles. For the left column, the statistics are calculated across patients, where patients with multiple scans per quarter are volumes averaged across these scans. For the right column the statistics for volume ch ange over 3 months were calculated on the difference for each patient between the mean volume for that quarter against the previous quarter. Volumes are calculated using the whole 2.3*6*6mm OCT volume.
Extended Data Fig. 7 Kaplan-Meier survival curves for full dataset and subgroups stratified by drusen stage and presence of HRF.
a, A Kaplan-Meier survival curve for fellow eye conversion to exAMD from baseline (defined as the first presentation of first eye conversion) in number of months, showing a little over 40% of patients converted during over 6 years of available follow up. The table shows number of eyes remaining at risk per month. b, The same plot for comparison with following plots. (c–g) Plots for varying amounts of drusen, showing increasing numbers of patient convert as drusen volume increases. Drusen size categories are calculated as quartiles. The same plots are shown for patients h, without and i, with geographic atrophy (GA), those j, without and k, with hyper-reflective foci (HRF), and those l, without and m, with fibrovascular pigment epithelial detachment (PED). In all plots the timeline is with reference to the first incidence of the feature in the eye.
Data labelling of the Moorfields Eye Hospital AMD dataset. Manual opt outs before data transfer are not included as none of the patients who manually opted out had digital OCT within the study dates.
a, Colour key for 13 tissues and 3 artefacts segmented by the network. b, Left: Raw OCT input to the segmentation network. Right: Output of the retrained segmentation network. Three hyperreflective foci apparent in the intraretinal layers were successfully segmented in this B-scan (purple).
Flow chart of the deep learning system including ensembling and TTA. Model inputs are shaped as trapezoids. Deep learning networks are shaped as rectangles. Model outputs are shaped as pointed rectangles. a, The segmentation network takes a raw OCT scan as input to generate a dense segmentation of the OCT which is then fed into a Diagnosis and referral network to obtain auxiliary task referral and diagnosis labels. b, The auxiliary labels along with either the raw OCT scan or dense segmentation are inputted into each exAMD prediction network across each cross validation fold group. Although the arrows apply to one fold group and instance, they generalise across all fold groups and instances. c, Ten TTA predictions are obtained from each instance. All TTA predictions are combined via averaging to obtain the final ensembled prediction. d, Architecture of a single block in our network. Green circles are convolution layers applied sequentially to the input of the previous layer. Each convolution has stride 1 and uses ReLU activation. Four convolutions are shown for demonstrative purposes but the number of convolutions and the kernels used for each will differ between blocks. Each convolution has a skip connection to the last orange node which concatenates all the intermediate and final activations along the channel dimension as the output.
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Yim, J., Chopra, R., Spitz, T. et al. Predicting conversion to wet age-related macular degeneration using deep learning. Nat Med 26, 892–899 (2020). https://doi.org/10.1038/s41591-020-0867-7
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