This study aimed to understand the profile of hydroxychloroquine-treated patients, referral patterns, and dosing and to assess the adherence of eye care providers to the latest 2016 screening guidelines provided by the American Academy of Ophthalmology. Patients were identified using electronic health records (EHR) taking hydroxychloroquine and were seen by optometrists, retinal specialists, and non-retinal ophthalmologists. Review of EHR data includes demographic characteristics, indications, and dosing profile of hydroxychloroquine use, eye care provider managing the patient, and imaging modalities performed. A total of 166 patients were included in the study. The most common indications for screening were systemic lupus erythematosus and discoid lupus (52.4%) followed by rheumatoid arthritis (18.7%) and Sjögren's syndrome (9.6%). Ninety-two (55.4%) patients were on a higher-than-recommended dose of > 5 mg/kg/day. Patients who weighed less (mean 63.9 kg) were taking a higher-than-recommended dose (vs. 81.5 kg, p < 0.001). Although retinal specialists adhered best to the use of all three recommended imaging modalities, visual field testing was done appropriately for only 8.3% of Asian and 71.1% of non-Asian patients. In conclusion, there is substantial variability in screening by ophthalmic providers and prescribing practices compared with the current recommendations. In particular, there is a marked deficiency in correct visual field testing in Asian patients. These findings are important to highlight potential interventions to improve screening for hydroxychloroquine toxicity.

Objective

To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging.

Design

Evaluation of a diagnostic test or technology.

Subjects

Overall, 42 385 consecutive OCT images (865 volumetric OCT scans) obtained with Heidelberg Spectralis from 865 eyes from 464 patients at an academic retina clinic between October 2020 and December 2021 were retrospectively reviewed.

Methods

We developed a customized computer vision algorithm based on image filtering and edge detection to detect the posterior vitreous cortex for the determination of PVD status. A second deep learning (DL) image classification model based on convolutional neural networks and ResNet-50 architecture was also trained to identify PVD status from OCT images. The training dataset consisted of 674 OCT volume scans (33 026 OCT images), while the validation testing set consisted of 73 OCT volume scans (3577 OCT images). Overall, 118 OCT volume scans (5782 OCT images) were used as a separate external testing dataset.

Main outcome measures

Accuracy, sensitivity, specificity, F1-scores, and area under the receiver operator characteristic curves (AUROCs) were measured to assess the performance of the automated algorithms.

Results

Both the customized computer vision algorithm and DL model results were largely in agreement with the PVD status labeled by trained graders. The DL approach achieved an accuracy of 90.7% and an F1-score of 0.932 with a sensitivity of 100% and a specificity of 74.5% for PVD detection from an OCT volume scan. The AUROC was 89% at the image level and 96% at the volume level for the DL model. The customized computer vision algorithm attained an accuracy of 89.5% and an F1-score of 0.912 with a sensitivity of 91.9% and a specificity of 86.1% on the same task.

Conclusions

Both the computer vision algorithm and the DL model applied on OCT imaging enabled reliable detection of PVD status, demonstrating the potential for OCT-based automated PVD status classification to assist with vitreoretinal surgical planning.

Financial disclosures

Proprietary or commercial disclosure may be found after the references.