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Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations. Deep-learning models trained on external eye photographs can detect diabetic retinopathy, diabetic macular oedema and poor blood glucose control more accurately than models relying on demographic and medical history data.

Purpose Teleophthalmology has become the subject of heightened interest and scrutiny in the wake of the SARS-CoV-2 global pandemic. A streamlined implementation framework becomes increasingly important as demand grows. This study identified obstacles to teleophthalmology implementation through summative content analysis of key stakeholders’ perceptions. Design Summative content analysis of transcribed interviews with key stakeholders (including patients, technicians, ophthalmic readers, staff, nurses, and administrators at two teleophthalmology clinic sites). Methods Keyword Were counted and compared to examine underlying meaning. Two analysts coded text independently using MAXQDA for summative qualitative content analysis to derive themes and hierarchical relationships as a basis for future refinement of TECS implementation. xMind ZEN was used to map conceptual relationships and overarching themes that emerged to identify perceived facilitators and barriers to implementation Results Analysis revealed two themes common to perceptions: (1) benefits of care, and (2) ease of implementation. Perceived benefits included efficiency, accessibility, and earlier intervention in disease course. The quality and quantity of training was heavily weighted in its influence on stakeholders’ commitment to and confidence in the program, as were transparent organizational structure, clear bidirectional communication, and the availability of support staff. Conclusion Using a determinant framework of implementation science, this report highlighted potential hindrances to teleophthalmology implementation and offered solutions in order to increase access to screening, improve the quality of care provided, and facilitate sustainability of the innovation.

To evaluate the performance of the RETeval device, a handheld instrument using flicker electroretinography (ERG) and pupillography on undilated subjects with diabetes, to detect vision-threatening diabetic retinopathy (VTDR). Performance was measured using a cross-sectional, single armed, non-interventional, multi-site study with Early Treatment Diabetic Retinopathy Study 7-standard field, stereo, color fundus photography as the gold standard. The 468 subjects were randomized to a calibration phase (80%), whose ERG and pupillary waveforms were used to formulate an equation correlating with the presence of VTDR, and a validation phase (20%), used to independently validate that equation. The primary outcome was the prevalence-corrected area under the receiver operating characteristic (ROC) curve for the detection of VTDR. The area under the ROC curve was 0.86 for VTDR. With a sensitivity of 83%, the specificity was 78% and the negative predictive value was 99%. The average testing time was 2.3min. With a VTDR prevalence similar to that in the US, the RETeval device will identify about 75% of the population as not having VTDR with 99% accuracy. The device is simple to use, does not require pupil dilation, and has a short testing time.

To determine the prevalence of eye disease in new \"routine\" eye patients at the Atlanta Veteran Affairs Medical Center. Retrospective chart review of all new eye patients seen in the Atlanta Veteran Affairs Medical Center Comprehensive Eye Clinic over a 2-month period (January 1, 2008-February 28, 2008). 691 charts met inclusion criteria, with 33 charts excluded for insufficient documentation in the medical record. This left a total of 658 charts for the study. Charts were reviewed for the following information: demographic data, vision, ocular diagnoses (International Classification of Diseases, 9th Revision, Clinical Modification codes), and planned minor/laser/incisional surgical procedures. Additional data collected included whether glasses were prescribed and legal blindness. Vision-threatening ocular diagnoses and need for minor/laser/incision surgery were tabulated. There was a very high prevalence of potentially blinding disease in this population of new \"routine\" eye patients. About 63.4% of veterans were diagnosed with at least one ocular diagnosis other than refractive error; 25% had glaucoma or were suspects, 6% had cataracts, 5% had age-related macular degeneration, and 8% required a surgical procedure. The rate of ocular pathology is high in the veteran population.

Glaucoma is the leading cause of preventable, irreversible blindness world-wide. The disease can remain asymptomatic until severe, and an estimated 50%-90% of people with glaucoma remain undiagnosed. Glaucoma screening is recommended for early detection and treatment. A cost-effective tool to detect glaucoma could expand screening access to a much larger patient population, but such a tool is currently unavailable. We trained a deep learning algorithm using a retrospective dataset of 86,618 images, assessed for glaucomatous optic nerve head features and referable glaucomatous optic neuropathy (GON). The algorithm was validated using 3 datasets. For referable GON, the algorithm had an AUC of 0.945 (95% CI, 0.929-0.960) in dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of Glaucoma Specialists (GSs); 0.855 (95% CI, 0.841-0.870) in dataset B (9642 images, 1 image/patient; 9.2% referable), images from Atlanta Veterans Affairs Eye Clinic diabetic teleretinal screening program; and 0.881 (95% CI, 0.838-0.918) in dataset C (346 images, 1 image/patient; 81.7% referable), images from Dr. Shroff's Charity Eye Hospital's glaucoma clinic. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders, while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels. An algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.

External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about additional systemic medical conditions. We developed a deep learning system (DLS) that takes external eye photos as input and predicts multiple systemic parameters, such as those related to the liver (albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH); and blood count (Hgb, WBC, platelets). Development leveraged 151,237 images from 49,015 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA. Evaluation focused on 9 pre-specified systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869 patients with and without diabetes undergoing eye screening in 3 independent sites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared against baseline models incorporating available clinicodemographic variables (e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST>36, calcium<8.6, eGFR<60, Hgb<11, platelets<150, ACR>=300, and WBC<4 on validation set A (a patient population similar to the development sets), where the AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7.3-13.2%. Our findings provide further evidence that external eye photos contain important biomarkers of systemic health spanning multiple organ systems. Further work is needed to investigate whether and how these biomarkers can be translated into clinical impact.