Rhegmatogenous retinal detachment surgery: A review.

Rhegmatogenous retinal detachment (RRD) is a serious surgical condition with significant ocular morbidity if not managed properly. Once untreatable, approaches to the repair of RRD have greatly evolved over the years, leading to outstanding primary surgical success rates. The management of RRD is often a topic of great debate. Scleral buckling, vitrectomy and pneumatic retinopexy have been used successfully for the treatment of RRD. Several factors may affect surgical success and dictate a surgeon's preference for the technique employed. In this review, we provide an overview and supporting literature on the options for RRD repair and their respective preoperative and postoperative considerations in order to guide surgical management.

Feature Tracking and Segmentation in Real Time via Deep Learning in Vitreoretinal Surgery: A Platform for Artificial Intelligence-Mediated Surgical Guidance.

PURPOSE: This study investigated whether a deep-learning neural network can detect and segment surgical instrumentation and relevant tissue boundaries and landmarks within the retina using imaging acquired from a surgical microscope in real time, with the goal of providing image-guided vitreoretinal (VR) microsurgery. DESIGN: Retrospective analysis via a prospective, single-center study. PARTICIPANTS: One hundred and one patients undergoing VR surgery, inclusive of core vitrectomy, membrane peeling, and endolaser application, in a university-based ophthalmology department between July 1, 2020, and September 1, 2021. METHODS: A dataset composed of 606 surgical image frames was annotated by 3 VR surgeons. Annotation consisted of identifying the location and area of the following features, when present in-frame: vitrector-, forceps-, and endolaser tooltips, optic disc, fovea, retinal tears, retinal detachment, fibrovascular proliferation, endolaser spots, area where endolaser was applied, and macular hole. An instance segmentation fully convolutional neural network (YOLACT++) was adapted and trained, and fivefold cross-validation was employed to generate metrics for accuracy. MAIN OUTCOME MEASURES: Area under the precision-recall curve (AUPR) for the detection of elements tracked and segmented in the final test dataset; the frames per second (FPS) for the assessment of suitability for real-time performance of the model. RESULTS: The platform detected and classified the vitrector tooltip with a mean AUPR of 0.972 ± 0.009. The segmentation of target tissues, such as the optic disc, fovea, and macular hole reached mean AUPR values of 0.928 ± 0.013, 0.844 ± 0.039, and 0.916 ± 0.021, respectively. The postprocessed image was rendered at a full high-definition resolution of 1920 × 1080 pixels at 38.77 ± 1.52 FPS when attached to a surgical visualization system, reaching up to 87.44 ± 3.8 FPS. CONCLUSIONS: Neural Networks can localize, classify, and segment tissues and instruments during VR procedures in real time. We propose a framework for developing surgical guidance and assessment platform that may guide surgical decision-making and help in formulating tools for systematic analyses of VR surgery. Potential applications include collision avoidance to prevent unintended instrument-tissue interactions and the extraction of spatial localization and movement of surgical instruments for surgical data science research. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans.

BACKGROUND: Changes in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT). NEW METHOD: Eighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC). RESULTS: The mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/- 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977-0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977-0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively. COMPARISON WITH EXISTING METHOD(S): The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability. CONCLUSIONS: This automated segmentation method can reliably measure choroidal thickness using different OCT platforms.

Optical coherence tomography findings in methanol toxicity.

BACKGROUND: Methanol toxicity poses a significant public health problem in developing countries, and in Southeast Asia, where the most common source of poisoning is via adulterated liquor in local drinks. Methanol toxicity can have devastating visual consequences and retinal specialists should be aware of the features of this toxic optic neuropathy. The authors report a case of severe systemic methanol toxicity and relatively mild optic neuropathy demonstrating unique retinal changes on optical coherence tomography (OCT). CASE PRESENTATION: A previously healthy student developed ataxia, difficulty breathing and loss of consciousness hours after drinking homemade alcohol while traveling in Indonesia. She was found to have a serum pH of 6.79 and elevated methanol levels. She was treated with intravenous ethanol, methylprednisolone and sodium bicarbonate. When she awoke she had bilateral central scotomas. At presentation, she had central depression on visual field testing. OCT of the retinal nerve fiber layer (RNFL) was normal but ganglion cell layer analysis (GCL) showed highly selective loss of the nasal fibers in both eyes. Further, OCT of the macula demonstrated inner nuclear layer (INL) microcysts in the corresponding area of selective GCL loss in both eyes. CONCLUSIONS: The selective involvement of the papillomacular bundle fibers is common in toxic optic neuropathies and represents damage to the small caliber axons rich in mitochondria. Despite severe systemic toxicity, the relative sparing of the optic nerve in this case enabled characterization of the evolution of methanol toxicity with segmental GCL involvement and preservation of the RNFL, corresponding to the papillomacular bundle. This is the first reported case of INL microcysts in methanol optic neuropathy and supports that they are a non-specific finding, and may represent preferential damage to the papillomacular bundle.