Tylerskinner1865

32 D [RC], -0.66 to 0.57 D [PL], K2, 0.93 D [RC], -1.36 to 1.08 D [PL]). The interday repeatability of the curvature power of the steepest point (Kmax, 0.84 D [RC], -0.90 to 1.11 D [PL]) would benefit from being stratified RC= 0.44 D and PL= -0.49 to 0.67 D for Kmax < 49.0 D, and RC= 1.08 D and PL= -1.19 to 1.42 D for Kmax ≥ 49.0 D.

The interday repeatability of measurements, single or replicate, in subjects with keratoconus should be considered when diagnosing progressive disease. K1 exhibited the best intraday repeatability. Kmax benefits from being stratified according to disease severity.

The interday repeatability of measurements, single or replicate, in subjects with keratoconus should be considered when diagnosing progressive disease. K1 exhibited the best intraday repeatability. Kmax benefits from being stratified according to disease severity.

To compare the visual outcome and the rate of intraoperative complications in eyes of diabetic and nondiabetic patients undergoing phacoemulsification over 15 years.

Retrospective clinical cohort study.

Data of 179,159 eyes that underwent phacoemulsification at 8 centers were classified based on the presence or absence of diabetes mellitus. Visual acuity (VA) was defined as the best value of uncorrected or corrected distance measure available. For the VA analysis, eyes with co-pathologies or combined surgical procedures were further excluded, leaving a subset of 90,729 eyes. Main outcome measures were logarithm of the minimum angle of resolution (logMAR) VA at 4-12weeks postoperatively, and rate of intraoperative complications.

Cataract surgery in eyes of diabetic patients was associated with an improvement in mean VA of 0.48 logMAR (5 Snellen lines). Mean postoperative VA was slightly worse in diabetic compared to nondiabetic group (logMAR 0.23 vs 0.13; Snellen 20/30 vs 20/25; P < .0001) and the proportions of eyes achieving a visual gain of ≥3 Snellen lines (≥0.3 logMAR) was lower in the diabetic group (56.6% vs 63.5%; P < .0001). There was a linear relationship between diabetic retinopathy severity and worse postoperative visual acuity (β coefficient 0.098 to 0.288; P < .0001). We observed higher rates of posterior capsule rupture (2.3% vs 1.6%; P < .001) and dropped nuclear fragments (0.3% vs 0.2%; P < .001) in the diabetic group.

Postoperative VA negatively correlated with diabetes and diabetic retinopathy severity. Eyes of diabetic subjects had higher risks of posterior capsule rupture.

Postoperative VA negatively correlated with diabetes and diabetic retinopathy severity. Eyes of diabetic subjects had higher risks of posterior capsule rupture.

We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD).

Development and validation of a deep-learning model for feature segmentation.

Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2712 B-scans. A deep neural network was trained with these data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by 4 independent observers. The main outcome measures were Dice score, intraclass correlation coefficient, and free-response receiver operating characteristic curve.

On 11 of 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared with 0.61 ± 0.17 for the observers. The mean intraclass correlation coefficient for the model was 0.66 ± 0.22, compared with 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively because of a lack of data. Free-response receiver operating characteristic analysis demonstrated that the model scored similar or higher sensitivity per false positives compared with the observers.

The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.

The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.

To assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fundus photographs can predict future development of glaucomatous visual field defects.

Retrospective cohort study.

This study included 1,072 eyes of 827 glaucoma-suspect patients with an average follow-up of 5.9 ± 3.8 years. All eyes had normal standard automated perimetry (SAP) at baseline. Additional SAP and fundus photographs were acquired throughout follow-up. Conversion to glaucoma was defined as repeatable glaucomatous defects on SAP. learn more An OCT-trained deep learning algorithm (machine to machine, M2M) was used to predict RNFL thicknesses from fundus photographs. Joint longitudinal survival models were used to assess whether baseline and longitudinal change in M2M's RNFL thickness estimates could predict development of visual field defects.

A total of 196 eyes (18%) converted to glaucoma during follow-up. The mean rate of change in M2M's predicted RNFL thickness was -1.02μm/y for converters and -0.67μm/y for non-converters (P < .001). Baseline and rate of change of predicted RNFL thickness were significantly predictive of conversion to glaucoma, with hazard ratios in the multivariable model of 1.56 per 10μm lower at baseline (95% CI, 1.33-1.82; P < .001) and 1.99 per 1μm/y faster loss in thickness during follow-up (95% CI, 1.36-2.93; P < .001).

Longitudinal changes in a deep learning algorithm's predictions of RNFL thickness measurements based on fundus photographs can be used to predict risk of glaucoma conversion in eyes suspected of having the disease.

Longitudinal changes in a deep learning algorithm's predictions of RNFL thickness measurements based on fundus photographs can be used to predict risk of glaucoma conversion in eyes suspected of having the disease.