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Purpose: To examine deep learning (DL)-based methods for accurate segmentation of geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared (NIR) images., Methods: This retrospective analysis utilized imaging data from study eyes of patients enrolled in Proxima A and B (NCT02479386; NCT02399072) natural history studies of GA. Two multimodal DL networks (UNet and YNet) were used to automatically segment GA lesions on FAF; segmentation accuracy was compared with annotations by experienced graders. The training data set comprised 940 image pairs (FAF and NIR) from 183 patients in Proxima B; the test data set comprised 497 image pairs from 154 patients in Proxima A. Dice coefficient scores, Bland-Altman plots, and Pearson correlation coefficient (r) were used to assess performance., Results: On the test set, Dice scores for the DL network to grader comparison ranged from 0.89 to 0.92 for screening visit; Dice score between graders was 0.94. GA lesion area correlations (r) for YNet versus grader, UNet versus grader, and between graders were 0.981, 0.959, and 0.995, respectively. Longitudinal GA lesion area enlargement correlations (r) for screening to 12 months (n = 53) were lower (0.741, 0.622, and 0.890, respectively) compared with the cross-sectional results at screening. Longitudinal correlations (r) from screening to 6 months (n = 77) were even lower (0.294, 0.248, and 0.686, respectively)., Conclusions: Multimodal DL networks to segment GA lesions can produce accurate results comparable with expert graders., Translational Relevance: DL-based tools may support efficient and individualized assessment of patients with GA in clinical research and practice.

Objective: To develop deep learning models for annualized geographic atrophy (GA) growth rate prediction using fundus autofluorescence (FAF) images and spectral-domain OCT volumes from baseline visits, which can be used for prognostic covariate adjustment to increase power of clinical trials., Design: This retrospective analysis estimated GA growth rate as the slope of a linear fit on all available measurements of lesion area over a 2-year period. Three multitask deep learning models-FAF-only, OCT-only, and multimodal (FAF and OCT)-were developed to predict concurrent GA area and annualized growth rate., Participants: Patients were from prospective and observational lampalizumab clinical trials., Methods: The 3 models were trained on the development data set, tested on the holdout set, and further evaluated on the independent test sets. Baseline FAF images and OCT volumes from study eyes of patients with bilateral GA (NCT02247479; NCT02247531; and NCT02479386) were split into development (1279 patients/eyes) and holdout (443 patients/eyes) sets. Baseline FAF images from study eyes of NCT01229215 (106 patients/eyes) and NCT02399072 (169 patients/eyes) were used as independent test sets., Main Outcome Measures: Model performance was evaluated using squared Pearson correlation coefficient (r 2 ) between observed and predicted lesion areas/growth rates. Confidence intervals were calculated by bootstrap resampling (B = 10 000)., Results: On the holdout data set, r 2 (95% confidence interval) of the FAF-only, OCT-only, and multimodal models for GA lesion area prediction was 0.96 (0.95-0.97), 0.91 (0.87-0.95), and 0.94 (0.92-0.96), respectively, and for GA growth rate prediction was 0.48 (0.41-0.55), 0.36 (0.29-0.43), and 0.47 (0.40-0.54), respectively. On the 2 independent test sets, r 2 of the FAF-only model for GA lesion area was 0.98 (0.97-0.99) and 0.95 (0.93-0.96), and for GA growth rate was 0.65 (0.52-0.75) and 0.47 (0.34-0.60)., Conclusions: We show the feasibility of using baseline FAF images and OCT volumes to predict individual GA area and growth rates using a multitask deep learning approach. The deep learning-based growth rate predictions could be used for covariate adjustment to increase power of clinical trials., Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references., (Copyright © 2022 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Objective: To determine if the two-minute walk test (2MWT) could serve as an alternative measure of high-level mobility in lower limb prosthesis users when circumstances preclude administration of the Comprehensive High-level Activity Mobility Predictor (CHAMP)., Design: Cross-sectional study., Setting: Indoor recreational athletic field and gymnasium., Subjects: Fifty-eight adult lower limb prosthesis users with unilateral or bilateral lower limb amputation who participate in recreational athletic activities., Intervention: N/A., Main Measures: The 2MWT and CHAMP while using their preferred prosthesis(es) on an indoor artificial athletic field or hardwood gymnasium floor., Results: Thirty-nine men and nineteen women with a median age of 38.3 years participated in the study. Most participants experienced amputation(s) due to trauma (62%) or tumor (10%) and were generally higher functioning (K4 (91.4%) and K3 (8.6%)). The median (range) score for the CHAMP was 23.0 points (1.5-33.5) and the mean ± standard deviation (range) 2MWT distance walked was 188.6 ± 33.9 m (100.2-254.3 m). The CHAMP demonstrated a strong positive relationship with 2MWT (r = 0.83, p < 0.001). The 2MWT distance predicted 70% of the variance in CHAMP score., Conclusions: Although the 2MWT does not test multi-directional agility like the CHAMP, they were found to be highly correlated. If space is limited, the two-minute walk test can serve as an alternative measure for assessing high-level mobility capabilities in lower limb prosthesis users.

Aryl hydrocarbon receptor (AHR)/nuclear factor-erythroid 2-related factor 2 (NRF2) modulation is emerging as novel targets in the treatment of atopic dermatitis and other inflammatory skin disorders. Agonist activation of this pathway has downstream effects on epidermal barrier function, immunomodulation, oxidative stress reduction and cutaneous microbiome modulation. Tapinarof, a dual agonist of the AHR/NRF2 signalling pathway, has shown promise in phase 2 trials for atopic dermatitis. In this review, we summarize current knowledge of the AHR/NRF2 pathway and implications in skin disease process. We also review the therapeutic potential of current AHR agonists and propose future directions to address knowledge gaps., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)