DIVULGA.net: internacionalización de la divulgación del conocimiento científico y académico en internet

El proyecto DIVULGA.net tiene la finalidad específica de internacionalizar la difusión de conocimiento científico y académico, en una iniciativa liderada por alumnos UCM y cuyo propósito es incorporar a estudiantes de universidades extranjeras. De este modo se pueden crear sinergias que fortalezcan una red en internet de divulgación de cultura científica con los universitarios como agentes principales

What kind of legal and ethical issues will arise from using AI systems in the medical practice?

Trabajo presentado al Annual Meeting of the European Society for Medical Imaging Informatics (EuSoMII): “Medical Imaging Informatics – AI, Clinical Applications and more”, celebrado en Valencia (España) del 18 al 19 de octubre de 2019

Convolutional neural networks: the state-of-the-art of Artificial Intelligence for medical imaging

Trabajo presentado al Annual Meeting of the European Society for Medical Imaging Informatics (EuSoMII): “Medical Imaging Informatics – AI, Clinical Applications and more”, celebrado en Valencia (España) del 18 al 19 de octubre de 2019

Aspectos etico-legales de la aplicación de sistemas de inteligencia artificial a la radiología

Trabajo presentado al X Congreso de la Sociedad Centro-Norte de Radiología (CENORA), celebrado en Logroño del 4 al 5 de octubre de 2019

Generalization of Deep Learning Algorithms for Chest X-rays

The dataset is divided by institutions and by x-ray machines.-- Appropriate images were selected for this project using the MicroDicom software. All images are anonymised.-- Methods for processing the data: 1. Resize [512, 512], 2. Remove the letters by cropping the image 0.15%, 3. Resize [512, 512], 4. Max-min normalize, 5. Convert to JPG.Dataset con imágenes de rayos-X patológicas y de control de neumonía provocada por COVID-19 tomadas en distintos hospitales y equipos de adquisición de imagen. Todos los pacientes tenían PCR positiva a la hora de realizarles la radiografía.Peer reviewe

Head-CT 2D/3D images with and without ICH prepared for Deep Learning

For the "ICH detection" part, radiologists selected parients according to inclusion and exclusion criteria. - Images with HIC: - Inclusion criteria: Diagnosis of an ICH between 2010 and 2015. - Exclusion criteria: Significant motion artifact Significant postsurgical changes. - Images without ICH (healthy): - Inclusion criteria: Having done a head-CT between 2010 and 2015 reported as “normal” or “ICH is ruled out”. - Exclusion criteria: Significant motion artifact. Other major diagnoses (such as tumors). For the "prognosis" part, only the pathological head-CTs were used. All images are de-identified.In order to access this dataset, it is necessary to send an email to [email protected] specifying the intended use. Commercial use is prohibited under license: Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA).Para poder acceder a este dataset es necesario enviar un email a [email protected] especificando el uso que se le va a dar. Se prohíbe su uso comercial bajo la licencia Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA).[EN] This dataset contains two sets of images and tabular data anonymised and prepared for its use in the training and/or validation of artificial neural networks. The first set of images includes 3322 JPG files with 2D images of head computed tomography (CT) scans with and without intracranial hemorrhage (ICH), as well as a CSV with demographic data (age and gender) associated to each file. The second set of images consists of 262 NPY files with 3D images of head-CT scans with ICH, together with a CSV that includes clinical data related to each image.[ES] Este dataset contiene dos conjuntos de imágenes y datos tabulares anonimizados y preparados para su uso en el entrenamiento o validación de redes neuronales artificiales. El primer conjunto de imágenes incluye 3322 archivos JPG con imágenes 2D de tomografías computarizadas (TCs) craneales sin y con hemorragia intracranial (HIC), así como un CSV con datos demográficos (edad y sexo) asociados a cada archivo. El segundo conjunto de imágenes consiste en 262 archivos NPY con imágenes 3D de TCs con HIC junto con un CSV que incluye varios datos clínicos asociados a las imágenes.Peer reviewe

Developing a Training Web Application for Improving the COVID-19 Diagnostic Accuracy on Chest X-ray

In December 2019, a new coronavirus known as 2019-nCoV emerged in Wuhan, China. The virus has spread globally and the infection was declared pandemic in March 2020. Although most cases of coronavirus disease 2019 (COVID-19) are mild, some of them rapidly develop acute respiratory distress syndrome. In the clinical management, chest X-rays (CXR) are essential, but the evaluation of COVID-19 CXR could be a challenge. In this context, we developed COVID-19 TRAINING, a free Web application for training on the evaluation of COVID-19 CXR. The application included 196 CXR belonging to three categories: non-pathological, pathological compatible with COVID-19, and pathological non-compatible with COVID-19. On the training screen, images were shown to the users and they chose a diagnosis among those three possibilities. At any time, users could finish the training session and be evaluated through the estimation of their diagnostic accuracy values: sensitivity, specificity, predictive values, and global accuracy. Images were hand-labeled by four thoracic radiologists. Average values for sensitivity, specificity, and global accuracy were .72, .64, and .68. Users who achieved better sensitivity registered less specificity (p < .0001) and those with higher specificity decreased their sensitivity (p < .0001). Users who sent more answers achieved better accuracy (p = .0002). The application COVID-19 TRAINING provides a revolutionary tool to learn the necessary skills to evaluate COVID-19 on CXR. Diagnosis training applications could provide a new original manner of evaluation for medical professionals based on their diagnostic accuracy values, and an efficient method to collect valuable data for research purposes.Peer reviewe

A primer on deep learning and convolutional neural networks for clinicians

Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).Consejo Superior de Investigaciones Cientificas (JS-CSIC-BMCSO-0920) Deep-Hybrid DataCloud (H2020—Grant agreement No 777435) Servicio Cantabro de Salud.Peer reviewe

A deep learning model for prognosis prediction after intracranial hemorrhage

[Background and purpose]: Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis.[Methods]: We included patients with ICH (n = 262), and we trained a custom model for the classification of patients into poor prognosis and good prognosis, using a hybrid input consisting of brain CT images and other clinical variables. We compared it with two other models, one trained with images only (I-model) and the other with tabular data only (D-model).[Results]: Our hybrid model achieved an area under the receiver operating characteristic curve (AUC) of .924 (95% confidence interval [CI]: .831-.986), and an accuracy of .861 (95% CI: .760-.960). The I- and D-models achieved an AUC of .763 (95% CI: .622-.902) and .746 (95% CI: .598-.876), respectively.[Conclusions]: The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision-making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.Peer reviewe

Pseudomonas aeruginosa antibiotic susceptibility profiles, genomic epidemiology and resistance mechanisms: a nation-wide five-year time lapse analysisResearch in context

Summary: Background: Pseudomonas aeruginosa healthcare-associated infections are one of the top antimicrobial resistance threats world-wide. In order to analyze the current trends, we performed a Spanish nation-wide high-resolution analysis of the susceptibility profiles, the genomic epidemiology and the resistome of P. aeruginosa over a five-year time lapse. Methods: A total of 3.180 nonduplicated P. aeruginosa clinical isolates from two Spanish nation-wide surveys performed in October 2017 and 2022 were analyzed. MICs of 13 antipseudomonals were determined by ISO-EUCAST. Multidrug resistance (MDR)/extensively drug resistance (XDR)/difficult to treat resistance (DTR)/pandrug resistance (PDR) profiles were defined following established criteria. All XDR/DTR isolates were subjected to whole genome sequencing (WGS). Findings: A decrease in resistance to all tested antibiotics, including older and newer antimicrobials, was observed in 2022 vs 2017. Likewise, a major reduction of XDR (15.2% vs 5.9%) and DTR (4.2 vs 2.1%) profiles was evidenced, and even more patent among ICU isolates [XDR (26.0% vs 6.0%) and DTR (8.9% vs 2.6%)] (p < 0.001). The prevalence of Extended-spectrum β-lactamase/carbapenemase production was slightly lower in 2022 (2.1%. vs 3.1%, p = 0.064). However, there was a significant increase in the proportion of carbapenemase production among carbapenem-resistant strains (29.4% vs 18.1%, p = 0.0246). While ST175 was still the most frequent clone among XDR, a slight reduction in its prevalence was noted (35.9% vs 45.5%, p = 0.106) as opposed to ST235 which increased significantly (24.3% vs 12.3%, p = 0.0062). Interpretation: While the generalized decrease in P. aeruginosa resistance, linked to a major reduction in the prevalence of XDR strains, is encouraging, the negative counterpart is the increase in the proportion of XDR strains producing carbapenemases, associated to the significant advance of the concerning world-wide disseminated hypervirulent high-risk clone ST235. Continued high-resolution surveillance, integrating phenotypic and genomic data, is necessary for understanding resistance trends and analyzing the impact of national plans on antimicrobial resistance. Funding: MSD and the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación and Unión Europea—NextGenerationEU