Search Results (3)

Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions. Thus, the importance of data cannot be underestimated, and clinically corresponding datasets are required. Many researchers face a lack of medical data due to limited access, privacy concerns, or the absence of available annotations. One of the most widely used diagnostic tools in ophthalmology is Optical Coherence Tomography (OCT). Addressing the data availability issue is crucial for enhancing AI applications in the field of OCT diagnostics. This review aims to provide a comprehensive analysis of all publicly accessible retinal OCT datasets. Our main objective is to compile a list of OCT datasets and their properties, which can serve as an accessible reference, facilitating data curation for medical image analysis tasks. For this review, we searched through the Zenodo repository, Mendeley Data repository, MEDLINE database, and Google Dataset search engine. We systematically evaluated all the identified datasets and found 23 open-access datasets containing OCT images, which significantly vary in terms of size, scope, and ground-truth labels. Our findings indicate the need for improvement in data-sharing practices and standardized documentation. Enhancing the availability and quality of OCT datasets will support the development of AI algorithms and ultimately improve diagnostic capabilities in ophthalmology. By providing a comprehensive list of accessible OCT datasets, this review aims to facilitate better utilization and development of AI in medical image analysis. Full article

The geosciences suffer from a lack of large georeferenced datasets that can be used to assess and monitor the role of soil organic carbon (SOC) in plant growth, soil fertility, and CO₂ sequestration. Publicly available, large field-scale georeferenced datasets are often limited in number and design to serve these purposes. This study provides the first publicly accessible dataset of georeferenced topsoil SOC measurements (n = 840) over a 26-hectare (ha) agricultural field located in southern Ontario, Canada, with a sampling density of ~32 points per ha. As SOC is usually influenced by site topography (i.e., slope and landscape position), each point of the database is associated with a wide range of remote sensing topographic derivatives; as well as with normalized difference vegetation index (NDVI) based value. The NDVI data were extracted from remote sensing Sentinel-2 imagery from over a five-year period (2017–2021). In this paper, the methodology for topsoil sampling, SOC measurement in the lab, as well as producing the suite of topographic derivatives is described. We discuss the opportunities that the database offers in terms of spatially explicit and continuous soil information to support international efforts in digital soil mapping (i.e., SoilGrids250m) as well as other potential applications detailed in the discussion section. We believe that the database with very dense point location measurements can help in conducting carbon stocks and sequestration studies. Such information can be used to help bridge the gap between ground data and remotely sensed datasets or data-derived products from modeling approaches intended to evaluate field-scale rates of agricultural carbon accumulation. The generated topsoil database in this study is archived and publicly available on the Zenodo open-access repository. Full article

Robotic observatories are ideal infrastructures that can be remotely accessed by scientists, amateurs, and general public for research and education in Astronomy. Its robotization is a complex process for ensuring autonomy, safety, and coordination among all subsystems. Some observatories, such as Francisco Sanchez’s, are equipped with two types of telescopes: one for the night and one for the day. The night-time telescope must be protected from exposure to sunlight in order to use them in an automated way. For this purpose, this article proposes the design and construction of a smart cover that opens and closes according to the time of day. The mechatronic design covers the electronic, mechanical, and software programming, and it has been devised taking while taking the principles of open design, ease of reproduction, low-cost, and smart behaviour into account. The design has been parameterized, so that it can be adapted to telescopes of any size. The final prototype is lightweight, cost-effective, and can be built while using common 3D printing and PCB milling machines. The complete design is licensed under the GNU General Public License v3.0 and all the documentation, schematics, and software are available in public repositories, like Zenodo, GitHub, and Instructables. Full article