Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms.

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.

Internal carotid artery aneurysm presenting as diplopia via telemedicine during COVID-19.

A patient presented with acute onset of double vision during the start of the COVID-19 pandemic when elective medical care was restricted. Initially declining an in-person evaluation, she was examined using a telehealth video visit, incorporating multiple technological modalities to ascertain ophthalmic examination elements. Her findings prompted emergent neuroimaging, revealing a giant internal carotid artery aneurysm, which was successfully embolized to prevent debilitating and possibly fatal intracranial haemorrhage. This case report illustrates the successful use of telemedicine and remote patient data acquisition to make a life-saving diagnosis.

Logistic Regression Classification of Primary Vitreoretinal Lymphoma versus Uveitis by Interleukin 6 and Interleukin 10 Levels.

PURPOSE: To assess the diagnostic performance and generalizability of logistic regression in classifying primary vitreoretinal lymphoma (PVRL) versus uveitis from intraocular cytokine levels in a single-center retrospective cohort, comparing a logistic regression model and previously published Interleukin Score for Intraocular Lymphoma Diagnosis (ISOLD) scores against the interleukin 10 (IL-10)-to-interleukin 6 (IL-6) ratio. DESIGN: Retrospective cohort study. PARTICIPANTS: Patient histories, pathology reports, and intraocular cytokine levels from 2339 patient entries in the National Eye Institute Histopathology Core database. METHODS: Patient diagnoses of PVRL versus uveitis and associated aqueous or vitreous IL-6 and IL-10 levels were collected retrospectively. From these data, cytokine levels were compared between diagnoses with the Mann-Whitney U test. A logistic regression model was trained to classify PVRL versus uveitis from aqueous and vitreous IL-6 and IL-10 samples and compared with ISOLD scores and IL-10-to-IL-6 ratios. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC) for each classifier and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) at the optimal cutoff (maximal Youden index) for each classifier. RESULTS: Seventy-seven lymphoma patients (10 aqueous samples, 67 vitreous samples) and 84 uveitis patients (19 aqueous samples, 65 vitreous samples) treated between October 5, 1999, and September 16, 2015, were included. Interleukin 6 levels were higher and IL-10 levels were lower in uveitis patients compared with lymphoma patients (P < 0.01). For vitreous samples, the logistic regression model, ISOLD score, and IL-10-to-IL-6 ratio achieved AUCs of 98.3%, 97.7%, and 96.3%, respectively. Sensitivity, specificity, PPV, and NPV at the optimal cutoffs for each classifier were 94.2%, 96.9%, 97%, and 94% for the logistic regression model; 92.7%, 100%, 100%, and 92.9% for the ISOLD score; and 94.2%, 95.3%, 95.6%, and 93.9% for the IL-10-to-IL-6 ratio. All models achieved complete separation between uveitis and lymphoma in the aqueous data set. CONCLUSIONS: The accuracy of the logistic regression model and generalizability of the ISOLD score to an independent patient cohort suggest that intraocular cytokine analysis by logistic regression may be a promising adjunct to cytopathologic analysis, the gold standard, for the early diagnosis of primary vitreoretinal lymphoma. Further validation studies are merited.

Gradient Boosted Decision Tree Classification of Endophthalmitis Versus Uveitis and Lymphoma from Aqueous and Vitreous IL-6 and IL-10 Levels.

PURPOSE: To investigate the effectiveness of gradient boosting to classify endophthalmitis versus uveitis and lymphoma by intraocular cytokine levels. METHOD: Patient diagnoses and aqueous and vitreous levels of interleukin (IL)-6 and IL-10 were retrospectively extracted from a National Eye Institute Histopathology Core database and compared by Kruskal-Wallis and post hoc Dunn tests. A gradient-boosted decision tree classifier was trained to differentiate endophthalmitis versus uveitis and lymphoma from vitreous IL-6 and IL-10, vitreous IL-6 only, and aqueous IL-6 only data sets; and was tested with 80-20 train-test split and 3-fold cross-validation of the training set. RESULTS: Seven endophthalmitis, 29 lymphoma, and 49 uveitis patients were included. IL-6 was higher in endophthalmitis than uveitis (P = 0.0713 aqueous, 0.0014 vitreous) and lymphoma (P = 0.0032 aqueous, 0.0001 vitreous). IL-10 was significantly higher in lymphoma than uveitis (P = 0.0017 aqueous, 0.0014 vitreous). Three-fold cross validation demonstrated 95% ± 5%, 95% ± 4%, and 97% ± 5% predictive accuracy for vitreous IL-6 and IL-10, vitreous IL-6 only, and aqueous IL-6 only data sets. Upon validation with the testing set, vitreous IL-6 and IL-10 and aqueous IL-6 only data sets achieved 100% predictive accuracy and vitreous IL-6 only data achieved 93% predictive accuracy with 100% sensitivity, 92% specificity, and an area under the receiver operating characteristic curve (ROC/AUC) of 96%. CONCLUSIONS: With limited sample size, gradient boosting can differentiate endophthalmitis from uveitis and lymphoma by IL-6 and IL-10 with high sensitivity and specificity; however, a larger cohort is needed for further validation.