Development and validation of an educational software based in artificial neural networks for training in radiology (JORCAD) through an interactive learning activity.

The use of Computer Aided Detection (CAD) software has been previously documented as a valuable tool to improve specialist training in Radiology. This research assesses the utility of an educational software tool aimed to train residents in Radiology and other related medical specialties and students from Medicine degree. This in-house developed software, called JORCAD, integrates a CAD system based in Convolutional Neural Networks (CNNs) with annotated cases from radiological image databases. The methodology followed for software validation was expert judgement after completing an interactive learning activity. Participants received a theoretical session and a software usage tutorial and afterwards utilized the application in a dedicated workstation to analyze a series of proposed cases of thorax computed tomography (CT) and mammography. A total of 26 expert participants from the Radiology Department at Salamanca University Hospital (15 specialists and 11 residents) fulfilled the activity and evaluated different aspects through a series of surveys: software usability, case navigation tools, CAD module utility for learning and JORCAD educational capabilities. Participants also graded imaging cases to establish JORCAD usefulness for training radiology residents. According to the statistical analysis of survey results and expert cases scoring, along with their opinions, it can be concluded that JORCAD software is a useful tool for training future specialists. The combination of CAD with annotated cases from validated databases enhances learning, offering a second opinion and changing the usual training paradigm. Including software as JORCAD in residency training programs of Radiology and other medical specialties would have a positive effect on trainees' background knowledge.

Modeling optical fiber space division multiplexed quantum key distribution systems.

We report a model to use to evaluate the performance of multiple quantum key distribution (QKD) channel transmission using spatial division multiplexing (SDM) in multicore (MCF) and few-mode fibers (FMF). This model is then used to analyze the feasibility of QKD transmission in 7-core MCFs in two practical scenarios involving the (1) transmission of only QKD channels and (2) simultaneous transmission of QKD and classical channels. In the first case, standard homogeneous MCFs enable transmission distances per core compatible with transmission parameters (distance and net key rate) very close to those of single-core single-mode fibers. For the second case, heterogeneous MCFs must be employed to make this option feasible.

Nódulos cutáneos en antebrazo tras punción accidental de un dedo / Skin nodules on forearm after accidental puncture of a finger

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Artritis séptica de codo por Actinomyces spp. y cuerpo extraño / Septic arthritis of the elbow due to actynomyces sp. and foreign body

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