Retinal and intra-retinal layer thicknesses are routinely generated from optical coherence tomography (OCT) images, but on-board software capabilities and image scaling assumptions are not consistent across devices. This study evaluates the device-independent Iowa Reference Algorithms (Iowa Institute for Biomedical Imaging) for automated intra-retinal layer segmentation and image scaling for three OCT systems. Healthy participants (n = 25) underwent macular volume scans using a Cirrus HD-OCT (Zeiss), 3D-OCT 1000 (Topcon), and a non-commercial long-wavelength (1040nm) OCT on two occasions. Mean thickness of 10 intra-retinal layers was measured in three ETDRS subfields (fovea, inner ring and outer ring) using the Iowa Reference Algorithms. Where available, total retinal thicknesses were measured using on-board software. Measured axial eye length (AEL)-dependent scaling was used throughout, with a comparison made to the system-specific fixed-AEL scaling. Inter-session repeatability and agreement between OCT systems and segmentation methods was assessed. Inter-session coefficient of repeatability (CoR) for the foveal subfield total retinal thickness was 3.43μm, 4.76μm, and 5.98μm for the Zeiss, Topcon, and long-wavelength images respectively. For the commercial software, CoR was 4.63μm (Zeiss) and 7.63μm (Topcon). The Iowa Reference Algorithms demonstrated higher repeatability than the on-board software and, in addition, reliably segmented all 10 intra-retinal layers. With fixed-AEL scaling, the algorithm produced significantly different thickness values for the three OCT devices (P<0.05), with these discrepancies generally characterized by an overall offset (bias) and correlations with axial eye length for the foveal subfield and outer ring (P<0.05). This correlation was reduced to an insignificant level in all cases when AEL-dependent scaling was used. Overall, the Iowa Reference Algorithms are viable for clinical and research use in healthy eyes imaged with these devices, however ocular biometry is required for accurate quantification of OCT images.

Purpose Choroidal thickness (ChT) and choroidal vascularity index (CVI) represent two important metrics in health-, disease-, and myopia-related studies. Wide-field swept-source optical coherence tomography (OCT) provides improved and extended imaging and extraction of choroidal variables. This study characterizes the topography and repeatability of these parameters in healthy eyes. Methods Swept-source OCT volume scans were obtained on 14 young adult patients on three separate days. ChT and CVI were automatically corrected for image magnification and extracted for different enface regions within an extended ETDRS grid of 10 mm diameter. Topographical distribution, correlation to ocular length, and intersession repeatability of both choroidal parameters were assessed. Results CVI showed little fluctuation between subfields, unlike ChT, which demonstrated thinning toward the peripheral choroid (coefficients of variation 5.92 vs. 0.89). ChT showed a consistent negative correlation with axial length (ρ = −0.05 to −0.61), although this was only statistically significant in the inner superior subfield ( P = 0.02). There was no consistent or significant relationship between CVI and axial length or between CVI and ChT. The repeatability of CVI measurements (3.90%–5.51%) was more consistent between scan regions than ChT measurements (10.37–20.33 µm). Conclusions CVI values were consistent across the central 10 mm of the retina, while ChT reduced with eccentricity. The repeatability of both parameters is similar to the effect size reported in many studies using the choroid as a biomarker, which should be considered in the interpretation of findings. Translational Relevance This study provided normative as well as metrological information for the clinical interpretation of ChT and CVI in health and disease.

Contact lenses in the future will likely have functions other than correction of refractive error. Lenses designed to control the development of myopia are already commercially available. Contact lenses as drug delivery devices and powered through advancements in nanotechnology will open up further opportunities for unique uses of contact lenses.This review examines the use, or potential use, of contact lenses aside from their role to correct refractive error. Contact lenses can be used to detect systemic and ocular surface diseases, treat and manage various ocular conditions and as devices that can correct presbyopia, control the development of myopia or be used for augmented vision. There is also discussion of new developments in contact lens packaging and storage cases.The use of contact lenses as devices to detect systemic disease has mostly focussed on detecting changes to glucose levels in tears for monitoring diabetic control. Glucose can be detected using changes in colour, fluorescence or generation of electric signals by embedded sensors such as boronic acid, concanavalin A or glucose oxidase. Contact lenses that have gained regulatory approval can measure changes in intraocular pressure to monitor glaucoma by measuring small changes in corneal shape. Challenges include integrating sensors into contact lenses and detecting the signals generated. Various techniques are used to optimise uptake and release of the drugs to the ocular surface to treat diseases such as dry eye, glaucoma, infection and allergy. Contact lenses that either mechanically or electronically change their shape are being investigated for the management of presbyopia. Contact lenses that slow the development of myopia are based upon incorporating concentric rings of plus power, peripheral optical zone(s) with add power or non-monotonic variations in power. Various forms of these lenses have shown a reduction in myopia in clinical trials and are available in various markets.

Validation of Pegasus-OCT, an artificial intelligence based software for the automated detection of macula disease from OCT scans, is conducted on independent, multi-centre data. 5,588 volumes spanning multiple populations, device manufacturers and acquisition sites were assessed. Pegasus-OCT achieves AUROCs of >98% on AMD, DME and general anomaly detection.

Purpose: Despite the widespread practice of gradually adapting all new soft contact lens wearers (neophytes), there is little evidence-based research underpinning such practice. This work determined if a gradual adaptation period is necessary for neophytes when fitted with modern hydrogel or silicone-hydrogel daily disposable contact lenses. Method: At four sites, neophytes (19-32 years) were randomly assigned to an adaptation schedule: fast (10 hours wear from the first day) or gradual (4 hours on the first day, increasing their wear-time by 2 hours on each subsequent day until they had reached 10 hours) and hydrogel (n=24 fast; n=21 gradual) or silicone-hydrogel (n=10 fast; n=10 gradual) contact lenses. Masked investigators graded ocular surface physiology and non-invasive tear breakup-time (NIBUT). A range of subjective scores (using 0-100 visual analogue scales) were recorded at the initial visit and after 10 hours of lens wear, 4-6 days and 12-14 days after initial fitting. Subjective scores were also repeated after 7 days. Results: There was no difference (p>0.05) in ocular surface physiology between the fast and gradual adaptation groups at any time point in either lens type. NIBUT was similar at all time points for both adaptation groups in both lens types with the exception that the gradual adaptation silicone-hydrogel wearers had a slightly longer NIBUT (p=0.007) than the fast adaptation group. Subjective scores were also similar across the visits and lens types with the exception of 'lens awareness' and 'ease of lens removal' which were better (p<0.05) in the fast compared with the gradual adaptation hydrogel lens group at day 7. Additionally, 'end-ofday discomfort' was better (p=0.02) in the fast compared with the gradual adaptation hydrogel lens group at 12-14 days. Conclusion: There appears to be no benefit in soft contact lens adaptation for neophytes with modern contact lens materials.

Refractive errors are associated with a range of pathological conditions such as myopic maculopathy and glaucoma and are highly heritable. Studies of missense and putative loss-of-function (pLOF) variants identified via whole exome sequencing (WES) offer the prospect of directly implicating potentially causative disease genes. We performed a genome-wide association study (GWAS) for refractive error in 51 624 unrelated adults of European ancestry aged 40–69 years from the United Kingdom genotyped using WES. After testing 29 179 pLOF and 495 263 missense variants, 1 pLOF and 18 missense variants in 14 distinct genomic regions were taken forward for fine-mapping analysis. This yielded 19 putative causal variants, of which 18 had a posterior inclusion probability &gt; 0.5. Of the 19 putative causal variants, 12 were novel discoveries. Specific variants were associated with a more myopic refractive error while others were associated with a more hyperopic refractive error. Association with age-of-onset of spectacle wear (AOSW) was examined in an independent validation sample (38 100 early-AOSW cases and 74 243 controls). Of 11 novel variants that could be tested, 8 (73%) showed evidence of association with AOSW status. This work identified COL4A4 and ATM as novel candidate genes associated with refractive error. In addition, novel putative causal variants were identified in the genes RASGEF1, ARMS2, BMP4, SIX6, GSDMA, GNGT2, ZNF652 and CRX. Despite these successes, the study also highlighted the limitations of community-based WES studies compared to high myopia case–control WES studies.

To establish the optimum grading increment which ensured parity between practitioners while maximising clinical precision.Methods: Second year optometry students (n=127, 19.5 ± 1.4 years, 55% female) and qualified eye care practitioners (n=61, 40.2 ±14.8 years, 52% female) had 30 seconds to grade each of bulbar, limbal and palpebral hyperaemia of the upper lid of 4 patients imaged live with a digital slit lamp under 16x magnification, diffuse illumination, with the image projected on a screen. The patients were presented in a randomised sequence 3 times in succession, during which the graders used the Efron printed grading scale once to 0.1 precision, once to 0.5 precision and once to the nearest integer grade in a randomised order. Graders were masked to their previous responses.Results: For most grading conditions less than 20% of clinicians showed a ≤0.1 difference in grade from the mean. In contrast, more than 50% of the student graders and 40% of experienced graders showed a difference in grade from the mean within 0.5 for all conditions under measurement.Student precision in grading was better with both 0.1 and 0.5 grading precision than grading to the nearest unit, except for limbal hyperaemia where they performed more accurately with 0.5 unit precision grading. Limbal grading precision was not affected by grading step precision for experienced practitioners, but 0.1 and 0.5 grading precision were both better than 1.0 grading precision for bulbar hyperaemia and 0.1 grading precision was better than 0.5 grading precision and both were better than 1.0 grading precision for palpebral hyperaemia. Conclusion:Although narrower intervals scales maximise the ability to detect smaller clinical changes, the grading increment should not exceed one standard deviation of the discrepancy between measurements. Therefore, 0.5 grading increments are recommended for subjective anterior eye physiology grading (limbal, bulbar and palpebral redness).

Age-Related Macular Degeneration (AMD) is a progressive eye disease which damages the retina and causes visual impairment. Detecting those in the early stages at most risk of progression will allow more timely treatment and preserve sight. In this paper, we propose a machine learning based method to detect AMD and distinguish the di↵erent stages using choroidal images obtained from optical coherence tomography (OCT). We extract texture features using a Gabor filter bank and non-linear energy transformation. Then the histogram based feature descriptors are used to train the random forests, Support Vector Machine (SVM) and neural networks, which are tested on our choroid OCT image dataset with 21 participants. The experimental results show the feasibility of our method.