Purpose: To determine if optical coherence tomography angiography (OCTA)-derived vessel density measurements can extend the available dynamic range for detecting glaucoma compared to spectral-domain optical coherence tomography (SDOCT)-derived thickness measurements.Design: Observational, cross-sectional study. Participants: A total of 509 eyes from 38 healthy participants, 63 glaucoma suspects and 193 glaucoma patients enrolled in the Diagnostic Innovations in Glaucoma Study. Methods: Relative vessel density and tissue thickness measurement floors of perifoveal superficial vessel density (pfVD), circumpapillary capillary density (cpCD), circumpapillary retinal nerve fiber (cpRNFL) thickness, ganglion cell complex (GCC) thickness and visual field mean deviation were investigated and compared with a previously reported linear change point model (CPM) and locally weighted scatterplot smoothing (LOWESS) curves. Main Outcome Measures: Estimated vessel density and tissue thickness measurement floors and corresponding dynamic ranges. Results: Visual field MD ranged from −30.1 dB to 2.8 dB. No measurement floor was found for pfVD which continued to decrease constantly until very advanced disease. A true floor (i.e. slope ~ 0 after observed CPM change point) was detected for cpRNFL thickness only. Post-CPM estimated floors were 49.5±2.6 μm for cpRNFL thickness, 70.7±1.0 μm for GCC thickness and 31.2± 1.1% for cpCD. pfVD reached the post-CPM estimated floor later in the disease (VF MD: −25.8±3.8 dB) than cpCD (VF MD: −19.3±2.4 dB), cpRNFL thickness (VF MD: −17±3.3 dB) and GCC thickness (VF MD: −13.9±1.8 dB) (p<0.

OCT-A measures detect changes in retinal microvasculature before VF damage is detectable in patients with POAG, and these changes may reflect damage to tissues relevant to the pathophysiology of glaucoma. Longitudinal studies are needed to determine whether OCT-A measures can improve the detection or prediction of the onset and progression of glaucoma.

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Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event‐related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non‐verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non‐verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non‐verbal cognition and adaptive social function.

To assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and to apply this technique to enable automation of liver biometry. Materials and Methods: A two-dimensional U-Net CNN was trained for liver segmentation in two stages by using 330 abdominal MRI and CT examinations. First, the neural network was trained with unenhanced multiecho spoiled gradient-echo images from 300 MRI examinations to yield multiple signal weightings. Then, transfer learning was used to generalize the CNN with additional images from 30 contrast material-enhanced MRI and CT examinations. Performance of the CNN was assessed by using a distinct multiinstitutional dataset curated from multiple sources (498 subjects). Segmentation accuracy was evaluated by computing Dice scores. These segmentations were used to compute liver volume from CT and T1-weighted MRI examinations and to estimate hepatic proton density fat fraction (PDFF) from multiecho T2*-weighted MRI examinations. Quantitative volumetry and PDFF estimates were compared between automated and manual segmentation by using Pearson correlation and Bland-Altman statistics. Results: Dice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1-weighted MRI, and 0.92 ± 0.05 for T2*weighted MRI (n = 168). Liver volume measured with manual and automated segmentation agreed closely for CT (95% limits of agreement: −298 mL, 180 mL) and T1-weighted MRI (95% limits of agreement: −358 mL, 180 mL). Hepatic PDFF measured by the two segmentations also agreed closely (95% limits of agreement: −0.62%, 0.80%). Conclusion: By using a transfer-learning strategy, this study has demonstrated the feasibility of a CNN to be generalized to perform liver segmentation across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization.