[No abstract]

Purpose

To determine factors that influence patient satisfaction scores in individuals who have recently had cataract surgery.

Setting

Byers Eye Institute, Palo Alto, California, USA.

Design

Prospective case series.

Methods

Selected questions from the Press Ganey survey and the National Eye Institute Visual Function Questionnaire-25 were administered to each patient immediately after completion of a clinic visit. The correlation between patient-specific variables and the answer to the survey question "likelihood of recommending our practice to others," a surrogate for overall patient satisfaction, was assessed using the Student t test. A logistical regression model was used to adjust for potentially confounding variables.

Results

One hundred forty-three patients were recruited from 4 providers; 57 (39.8%) were men, and the mean age was 70.0 years ± 11.6 (SD). The main outcome was the proportion of scores less than 5, or "very good," for the likelihood of recommending the practice to others. There was a statistically significant association between a non-5 patient satisfaction score and self-reported ethnicity of Asian or Pacific Islander compared with other ethnicities (odds ratio [OR], 2.3; 95% confidence interval [CI], 1.0-5.1; P = .049); other possible correlates were not statistically significant. The relationship persisted after adjustment for potential confounding variables (OR, 2.6; 95% CI, 1.1-6.3; P = .027).

Conclusion

In postoperative cataract patients, Asian or Pacific Islander ethnicity, a factor out of the control of the provider and clinic staff, was associated with a lower overall Press Ganey patient satisfaction score compared with patients of all other ethnicities.

Importance

A deep learning system (DLS) that could automatically detect glaucomatous optic neuropathy (GON) with high sensitivity and specificity could expedite screening for GON.

Objective

To establish a DLS for detection of GON using retinal fundus images and glaucoma diagnosis with convoluted neural networks (GD-CNN) that has the ability to be generalized across populations.

Design, setting, and participants

In this cross-sectional study, a DLS for the classification of GON was developed for automated classification of GON using retinal fundus images obtained from the Chinese Glaucoma Study Alliance, the Handan Eye Study, and online databases. The researchers selected 241 032 images were selected as the training data set. The images were entered into the databases on June 9, 2009, obtained on July 11, 2018, and analyses were performed on December 15, 2018. The generalization of the DLS was tested in several validation data sets, which allowed assessment of the DLS in a clinical setting without exclusions, testing against variable image quality based on fundus photographs obtained from websites, evaluation in a population-based study that reflects a natural distribution of patients with glaucoma within the cohort and an additive data set that has a diverse ethnic distribution. An online learning system was established to transfer the trained and validated DLS to generalize the results with fundus images from new sources. To better understand the DLS decision-making process, a prediction visualization test was performed that identified regions of the fundus images utilized by the DLS for diagnosis.

Exposures

Use of a deep learning system.

Main outcomes and measures

Area under the receiver operating characteristics curve (AUC), sensitivity and specificity for DLS with reference to professional graders.

Results

From a total of 274 413 fundus images initially obtained from CGSA, 269 601 images passed initial image quality review and were graded for GON. A total of 241 032 images (definite GON 29 865 [12.4%], probable GON 11 046 [4.6%], unlikely GON 200 121 [83%]) from 68 013 patients were selected using random sampling to train the GD-CNN model. Validation and evaluation of the GD-CNN model was assessed using the remaining 28 569 images from CGSA. The AUC of the GD-CNN model in primary local validation data sets was 0.996 (95% CI, 0.995-0.998), with sensitivity of 96.2% and specificity of 97.7%. The most common reason for both false-negative and false-positive grading by GD-CNN (51 of 119 [46.3%] and 191 of 588 [32.3%]) and manual grading (50 of 113 [44.2%] and 183 of 538 [34.0%]) was pathologic or high myopia.

Conclusions and relevance

Application of GD-CNN to fundus images from different settings and varying image quality demonstrated a high sensitivity, specificity, and generalizability for detecting GON. These findings suggest that automated DLS could enhance current screening programs in a cost-effective and time-efficient manner.