Objectives To investigate the associations of alcohol consumption and smoking with the development of perimetric glaucoma in patients with suspected glaucoma. Design A retrospective cohort study of patients suspected to have glaucoma enrolled in the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES). Setting Three tertiary glaucoma centres in the USA. Participants 825 eyes of 610 patients with glaucoma suspect eyes with normal visual fields (VF) at baseline were followed over an average of 9 years from the DIGS and ADAGES studies. Outcome measures Development of glaucoma was defined as occurrence of three consecutive abnormal VF tests during follow-up. Univariable and multivariable Cox regression models were used to investigate lifestyle-related factors associated with development of VF loss over time. Results VF tests were abnormal three times in a row in 235 (28.5%) eyes. Alcohol consumption was associated with a higher risk of developing glaucoma (HR 1.57, 95% CI 1.03 to 2.38, p=0.037). In men, the risk of developing glaucoma in alcohol drinkers (HR 1.92, 95% CI 1.00 to 3.68, p=0.048) was greater than non-alcohol drinkers. In individuals of African descent, the risk of developing glaucoma in alcohol drinkers (HR 1.79, 95% CI 1.02 to 3.15, p=0.043) was greater than non-alcohol drinkers. Age was a modifier of the relationship between smoking and glaucomatous VF defects (p=0.048). The risk of developing glaucoma in smokers (HR 1.73, 95% CI 1.10 to 2.72, p=0.019) was greater than never smokers after adjustment for confounding factors in older patients (age >61 years). Conclusion Alcohol consumption was associated with an increased risk of developing glaucoma, particularly in men and individuals of African descent. The risk of developing glaucoma among smokers suspected of having glaucoma was influenced by age, with older individuals having a higher risk than younger people. Trial registration number NCT00221897 and NCT00221923.

To evaluate RETFound, a foundation artificial intelligence model, using a diverse clinical research dataset to assess its accuracy in detecting glaucoma using optic disc photographs. The model's accuracy for glaucoma detection was evaluated across race, age, glaucoma severity, and various training cycles (epochs) and dataset sample sizes. Evaluation of a diagnostic technology. The study included 9787 color fundus photographs (CFPs) from 2329 participants of diverse race (White [73.4%], Black [13.6%] and other [13%]), disease severity (21.8% mild glaucoma, 7.2% moderate or advanced glaucoma, 60.3% not glaucoma, and 10.7% unreported), and age (48.8% 60 years) from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. All fundus photographs were graded as "Glaucomatous" or "Non-glaucomatous." The study employed RETFound, a self-supervised learning model, to perform binary glaucoma classification. The diagnostic accuracy of RETFound was iteratively tested across different combinations of dataset sample sizes (50–2000 optic disc photographs), training cycles (5–50), and study subpopulations stratified by severity of glaucoma, age, and race). Diagnostic accuracy area under the receiver operating characteristic curve (AUC) for classifying CFP as "Glaucomatous" or "Non-glaucomatous." Performance increased with larger training datasets and more training cycles, improving from 50 training images and 5 epochs (AUC: 0.52) to 2000 training images and 50 epochs (AUC: 0.86), with reduced gain in performance from approximately 500 and 1000 training images (AUC of 0.82 and 0.83, respectively). Performance was consistent across race and age for all training size and cycle number combinations: Black (AUC = 0.87) vs. other (AUC = 0.86), and >60 years (AUC = 0.84) vs. <60 years (AUC = 0.87). Performance was significantly higher in patients with moderate to severe vs. mild glaucoma (AUC = 0.95 vs. 0.84, respectively). Good RETFound performance was observed with a relatively small sample size of optic disc photographs used for fine-tuning and across differences in race and age. RETFound’s ability to adapt across a range of CFP training conditions and populations suggests it is a promising tool to automate glaucoma detection in a variety of use cases. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Background/aims To assess the long-term variability of macular optical coherence tomography (OCT)/OCT angiography (OCTA) and visual field (VF) parameters. Methods Healthy and glaucoma eyes with ≥1-year follow-up were included. 24–2 VF and macular OCT/OCTA parameters, including VF mean deviation (MD), whole-image vessel density (wiVD) and ganglion cell complex thickness (wiGCC) were analysed. Intraclass correlation coefficient (ICC), root mean squared error (RMSE), within-subject test–retest SD (Sw) and test–retest variability were calculated for stable eye cohort (max follow-up=1.5 years). Rates of change and RMSE were evaluated in the extended cohort including all eyes (unlimited follow-up). Results From a total of 230 eyes (150 participants; age=67.7 years), 86 eyes (37%, 62 participants) were stable. In stable eyes, OCT parameters showed the highest mean (95%) ICC (wiGCC=0.99 (0.99, 0.99)), followed by VF (VF MD=0.91 (0.88, 0.93)) and OCTA (wiVD=0.82 (0.75, 0.87)). RMSE and Sw for VF MD were 0.92 dB and 0.81 dB, respectively, for wiVD were 1.64% and 1.48%, respectively, and for wiGCC, 0.91 µm and 0.78 µm, respectively. The long-term test–rest variability of VF MD, wiVD and wiGCC was 2.2 dB, 4.1% and 2.2 µm, respectively. In the extended cohort (mean follow-up=3.0 years), all parameters had significant rates of change (p<0.001), and compared with the stable cohort, only slightly higher RMSE (VF MD=1.07 dB; wiGCC=2.03 µm; wiVD=2.57%) were found. Conclusions VF and macular OCT/OCTA, particularly OCT parameters, showed small long-term variability in all eyes, including stable ones, supporting the use of these instruments in glaucoma follow-up. Changes in macular VD and GCC greater than 4%–5% and 2 µm, respectively, indicate possible progression. Trial registration number NCT00221897.

Ultra-widefield (UWF) imaging is commonly used in ophthalmology in tandem with scleral depressed examinations (SDE) to evaluate peripheral retinal disease. Because of the increased reliance on this technology in tele-ophthalmology, it is critical to evaluate its efficacy for detecting the peripheral retina when performed in isolation. Therefore, we sought to evaluate UWF imaging sensitivity in detecting retinal horseshoe tears (HSTs). Retrospective clinical validity and reliability study. A single-institutional retrospective analysis was performed on patients at the Shiley Eye Institute, University of California, San Diego. Patients with HSTs seen on SDE who underwent treatment with laser were included in the study. A total of 140 patients with HSTs in the right and/or left eyes met the inclusion criteria. Those with concomitant ruptured globes, retinal detachments, and vitreous hemorrhages were excluded. A total of 123 patients with 135 HSTs were included in the final analysis. The primary outcome was the number of HSTs detected by UWF imaging. A secondary outcome was HST location. Sensitivity was measured with respect to HST location, and statistical significance was calculated by Fisher exact testing. A total of 69 (51.1%) HSTs were visualized on UWF images and 66 (48.9%) were not visualized. The sensitivity of UWF imaging in capturing HSTs was 7 of 41 (17.1%), 8 of 25 (32.0%), 7 of 14 (50.0%), and 47 of 55 (85.5%) for the superior, inferior, nasal, and temporal quadrants, respectively. Sensitivities between HST visibility and location were statistically significant (P < .001). Nearly half of HSTs were missed by UWF imaging. This study demonstrates that UWF imaging alone is not sufficiently sensitive to exclude the presence of HSTs.

A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.