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Purpose: We describe our offline deep learning algorithm (DLA) and validation of its diagnostic ability to identify vitreoretinal abnormalities (VRA) on ocular ultrasound (OUS). Methods: Enrolled participants underwent OUS. All images were classified as normal or abnormal by two masked vitreoretinal specialists (AS, AM). A data set of 4902 OUS images was collected, and 4740 images of satisfactory quality were used. Of this, 4319 were processed for further training and development of DLA, and 421 images were graded by vitreoretinal specialists (AS and AM) to obtain ground truth. The main outcome measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under receiver operating characteristic (AUROC). Results: Our algorithm demonstrated high sensitivity and specificity in identifying VRA on OUS ([90.8%; 95% confidence interval (CI): 86.1–94.3%] and [97.1% (95% CI: 93.7–98.9%], respectively). PPV and NPV of the algorithm were also high ([97.0%; 95% CI: 93.7–98.9%] and [90.8%; 95% CI: 86.2–94.3%], respectively). The AUROC was high at 0.939, and the intergrader agreement was nearly perfect with Cohen's kappa of 0.938. The model demonstrated high sensitivity in predicting vitreous hemorrhage (100%), retinal detachment (97.4%), and choroidal detachment (100%). Conclusion: Our offline DLA software demonstrated reliable performance (high sensitivity, specificity, AUROC, PPV, NPV, and intergrader agreement) for predicting VRA on OUS. This might serve as an important tool for the ophthalmic technicians who are involved in community eye screening at rural settings where trained ophthalmologists are not available.

Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation “EffUnet” with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed “SpaGen” We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, “EffUnet-SpaGen”, is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings.