Table 1:

Sample of significant publications exhibiting recent breakthrough trends in AI with translational impact, segmented by disease^* (ophthalmic / non-ophthalmic) and methodological.

Novelty & significance	Key new findings and implications
Diabetic retinopathy
Screening; scale and generalizability [54] (2019)	Study novelty - Numbers: > 100k patients; > 800k images; >400 clinics. - Generalizability: (“authors believe” so because of (i) sheer volume of patients and locations, and (ii) a previous study showing the EyeArt system was not affected by gender, ethnicity or camera type) [19]. Implication - Validated on large numbers in a real world setting against real world trained human graders; however, there was no gold standard comparing graders vs algorithm. - Although heterogenous in provider setting, the study would benefit from an additional external source of validation.
Severity prediction [55] (2020)	Study novelty - Demonstrate feasibility of predicting future DR progression by leveraging CFPs of a patient acquired at a single visit from a 7-field color fundus photos (CFP). - The model predicted a 2-step or more ETDRS DR severity score worsening at 6, 12, and 24 months with an area under the curve (AUC) of 0.68 ± 0.13, 0.79 ± 0.05 and 0.77 ± 0.04 respectively. - Detected a predictive signal located in the peripheral retinal fields, which is not routinely collected for DR assessments, and the importance of microvascular abnormalities. Implication - May help stratify and identify high risk / rapidly progressive patients earlier that could prompt either intervention or for clinical trial recruitment assessing nuanced personalization
Treatment personalization [56] (2020)	Study novelty - This algorithm achieved an average AUC of 0.866 in discriminating responsive from non-responsive patients with DME who are treated with various anti-VEGF agents. - Classification precision was significantly higher when differentiating between very responsive and very unresponsive patients. - A new feature-learning and classification framework. At the heart of this framework is a novel convolutional neural network (CNN) model called ‘CADNet’. - As compared to most previous studies that use longitudinal ocular coherence tomography (OCT) series, this model required the pre-treatment ocular coherence tomography (OCT) only to predict treatment outcomes. Implication - Critical step toward using non-invasive imaging and automated analysis to select the most effective therapy to select for a patient’s specific biomarker profile. - May help identify poor responders a priori, who could be more suitable for clinic trials. - However, need for larger studies (only 127 patients), further validation studies and better explainability (no specific predictive imaging features were identified). - Could not comparatively assess efficacy of different agents due to small sample.
Smartphones [57] (2019)	Study novelty - First study assessing an offline AI algorithm on a smart phone for detection of referable DR (RDR = moderate DR or worse, or clinically significant macular edema (CSME)) in real-time in a real-world setting using local standards as benchmark for comparison (single ophthalmologist). - High sensitivity for detection of RDR (100% in both the eye-wise and patient-wise analysis). -Non-mydriatic patients; retained 100% sensitivity even without excluding low quality images (lower specificity; 88.4% vs 81.9%). Implication - Does not require internet, nor mydriasis; if well validated, ideal for deployment in developing world setting for screening (given high sensitivity). - Given low numbers (255 patients) and the gold standard of a single ophthalmologist (producing sensitivity of 100%), this algorithm requires both external validation and larger validation on both low and high quality images.
Glaucoma
Remote / home monitoring [58] (2020)	Study novelty - Demonstrates early reliability of remote intraocular pressure (IOP) measurement devices and visual field monitoring devices. - Both ancillary tests were easy-to-use. Implication - New reliable home devices can improve access, augment telemedicine, reduce burden on health systems and capture more data than infrequent office visits. - May improve patient engagement and compliance. - Wider validation required.
Surrogate device inference: (CFP from OCT) [59] (2019)	Study novelty - Infer glaucomatous changes on a CFP from a corresponding SD-OCT with strong correlation between predicted and observed RNFL thickness values (Pearson r = 0.832; R2 . 69.3%; P < 0.001), with mean absolute error of the predictions of 7.39 mm. - Provided a quantitative assessment of the amount of neural tissue lost from a CFP alone. Implication - Avoided the need for human grading by leveraging quantitative data generated by OCT analysis; not only does this eliminate the burdensome workforce need, but human determinations of glaucoma are known to be highly variable.
Age-related Macular Degeneration (AMD)
Biomarker-driven prognostication [60] (2020)	Study novelty - A review of the phenotypic (clinical and imaging), demographic, environmental, genetic and molecular biomarkers that have been studied in prognostication of AMD progression. Implication - Risk factors can be combined in prediction models to predict disease progression, but the selection of the proper risk factors for personalised risk prediction will differ among individuals and is dependent on their current disease stage.
Predicting progression [61] (2019)	Study novelty - Deep learning (DL) CNN predicted conversion to neovascular AMD with greater sensitivity than gradings from the AREDS dataset using both 4-step and 9-step grades for intermediate AMD or better. Implication - Promising, as this could further stratify high and low risk patients; needs external validation.
Genomic-based personalized risk [62] (2020)	Study novelty - Fundus images coupled with genotypes could predict late AMD progression with an averaged AUC value of 0.85 from the AREDS dataset (31 652 fundus photos) and 52 known AMD - associated genetic variants - Fundus images alone showed an averaged AUC of 0.81 (95%CI: 0.80–0.83). Implication - Adding genetic information can improve prediction of AMD progression; currently routine generic testing not recommended [63].
Non-ophthalmic diseases
Cardiovascular (CV) risk factor identification [64] (2019)	Study novelty - The prevalence and systemic risk factors for DR in multi-ethnic population could be determined accurately using a DL system, in significantly less time than human assessors. - Generalized across 5 races and 8 different datasets from 5 countries. Implication - This study highlights the potential use of AI for future epidemiology or clinical trials for DR grading in the global communities. - Notably, much larger and more diverse study than previous external validation of CV risk factor identification in a small Asian population from the original algorithm developed on images from the UK biobank [65].
Anemia Detection [66] (2019)	Study novelty -Using fundus imaging as a non-invasive way to detect anemia. -Achieving AUC 0.88 on a validation dataset of > 10k patients from the UK biobank, the DL algorithm could predict mean absolute error (MAE) of 0.63 g/dl in quantifying haemoglobin concentration. Implication -Ophthalmic imaging AI can be applied to systemic disease. -In this particular case, for diabetic patients who undergo regular screening retinal imaging are at increased risk of further morbidity and mortality from anemia.
Methods: OCT analysis
Improving device agnostic analysis [67] (2020)	Study novelty - New approach - unsupervised unpaired generative adversarial networks “cycleGANs” – translating OCT images from one vendor to another effectively reduces the covariate shift (the difference between the target and source domain), thereby demonstrating potential to overcome existing limitations is cross-device generalizability. - Applicability of existing methods is generally limited to samples that match training data. - Larger image patch sizes (256×256, 460×460) performed better in identifying and segmenting fluid, whereas for segmenting the thin photoreceptor layer, smaller image patch sizes (64×64, 128×128) achieved better performance. This suggests this image-context preservation is beneficial. Implication - Automated segmentation methods are expected to be part of routine diagnostic workflows, regardless of device; this method advances performance of cross-vendor translation and presents a new methodological basis for further investigation.

(Publication year in brackets)

^*A detailed review of many ophthalmic subspecialties is contained elsewhere in this Journal’s edition.