Central serous chorioretinopathy imaging biomarkers.

PURPOSE: To identify the factors predicting the visual and anatomical outcomes in eyes with central serous chorioretinopathy (CSCR) through 12 months. METHODS: Patients with diagnosis of CSCR, either acute or chronic, were included in this multicentric, retrospective study. Demographic factors; systemic risk factors; central macular thickness (CMT), subfoveal choroidal thickness (SFCT), linear extent of ellipsoid zone (EZ) and interdigitation zone damage on optical coherence tomography; details of leak on fluorescein angiography and indocyanine green angiography were included as predictors of anatomical and visual outcomes. Regression analysis was performed to correlate the changes in best corrected visual acuity (BCVA) and resolution of disease activity. RESULTS: A total of 231 eyes of 201 patients with a mean age (49.7±11.8 years) were analysed. A total of 97 and 134 eyes were classified as acute and chronic CSCR. BCVA (0.35±0.31 to 0.24±0.34; p<0.001), baseline optical coherence tomography (OCT) parameters including CMT (p<0.001), subretinal fluid (SRF) height (p<0.001) and SFCT (p=0.05) showed a significant change through 12 months. Multivariate regression analysis showed change in CMT (p≤0.01) and SRF height at baseline (p=0.05) as factors predictive of good visual outcome. Logistic regression analysis revealed changes in both CMT (p=0.009) and SFCT (p=0.01) through 12 months to correlate with the resolution of disease. CONCLUSION: OCT parameters such as changes in both CMT and SFCT along with subfoveal EZ damage can be predictive of disease resolution whereas changes in CMT and baseline SRF height correlate well with changes in BCVA through 12 months.

ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI.

Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).