Abstract
Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.

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Abstract
Classifying grapevine varieties is crucial in precision viticulture, as it allows for accurate estimation of vineyard row growth for different varieties and ensures authenticity in the wine industry. This task can be performed with time-consuming destructive methods, including data collection and analysis in the laboratory. In contrast, unmanned aerial vehicles (UAVs) offer a markedly more efficient and less restrictive method for gathering hyperspectral data, even though they may yield data with higher levels of noise. Therefore, the first task is the processing of these data to correct and downsample large amounts of data. In addition, the hyperspectral signatures of grape varieties are very similar. In this study, we propose the use of a convolutional neural network (CNN) to classify seventeen different varieties of red and white grape cultivars. Instead of classifying individual samples, our approach involves processing samples alongside their surrounding neighborhood for enhanced accuracy. The extraction of spatial and spectral features is addressed with (1) a spatial attention layer and (2) inception blocks. The pipeline goes from data preparation to dataset elaboration, finishing with the training phase. The fitted model is evaluated in terms of response time, accuracy and data separability and is compared with other state-of-the-art CNNs for classifying hyperspectral data. Our network was proven to be much more lightweight by using a limited number of input bands (40) and a reduced number of trainable weights (560 k parameters). Hence, it reduced training time (1 h on average) over the collected hyperspectral dataset. In contrast, other state-of-the-art research requires large networks with several million parameters that require hours to be trained. Despite this, the evaluated metrics showed much better results for our network (approximately 99% overall accuracy), in comparison with previous works barely achieving 81% OA over UAV imagery. This notable OA was similarly observed over satellite data. These results demonstrate the efficiency and robustness of our proposed method across different hyperspectral data sources.

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Abstract
Purpose: To evaluate the outcomes and durability of drug -eluting stents (DESs) for the treatment of hemodialysis access outflow stenosis. Material and Methods: A single -center retrospective analysis was conducted of all patients with hemodialysis vascular access outflow stenosis treated with a paclitaxel-coated DES (Eluvia; Boston Scientific, Marlborough, Massachusetts) between January 2020 and July 2022. A total of 34 DESs were implanted to treat outflow stenosis in 32 patients. Primary target lesion patency after stent deployment was the main outcome. Comparison between the time interval free from target lesion reintervention (TLR) after previous plain balloon angioplasty (PBA) and that after stent deployment for the same target lesion was considered a secondary outcome. Results: The primary patency at 6, 12, and 18 months was 63.1%, 47.6%, and 41.7%, respectively. The secondary patency rate was 100% at 18 months. The median time interval free from TLR increased from 4.1 to 11.9 months (P < .001). No adverse events were observed during the median follow-up period of 387 days. Conclusions: The patency rates after use of DES for hemodialysis access outflow stenosis were comparable with results for drug -coated balloons and stent grafts, addressing recoil and minimizing the risk of jailing by a covered stent.

Abstract