Impact of the first wave of the SARS-CoV-2 pandemic on the outcome of neurosurgical patients: A nationwide study in Spain

Objective To assess the effect of the first wave of the SARS-CoV-2 pandemic on the outcome of neurosurgical patients in Spain. Settings The initial flood of COVID-19 patients overwhelmed an unprepared healthcare system. Different measures were taken to deal with this overburden. The effect of these measures on neurosurgical patients, as well as the effect of COVID-19 itself, has not been thoroughly studied. Participants This was a multicentre, nationwide, observational retrospective study of patients who underwent any neurosurgical operation from March to July 2020. Interventions An exploratory factorial analysis was performed to select the most relevant variables of the sample. Primary and secondary outcome measures Univariate and multivariate analyses were performed to identify independent predictors of mortality and postoperative SARS-CoV-2 infection. Results Sixteen hospitals registered 1677 operated patients. The overall mortality was 6.4%, and 2.9% (44 patients) suffered a perioperative SARS-CoV-2 infection. Of those infections, 24 were diagnosed postoperatively. Age (OR 1.05), perioperative SARS-CoV-2 infection (OR 4.7), community COVID-19 incidence (cases/10 5 people/week) (OR 1.006), postoperative neurological worsening (OR 5.9), postoperative need for airway support (OR 5.38), ASA grade =3 (OR 2.5) and preoperative GCS 3-8 (OR 2.82) were independently associated with mortality. For SARS-CoV-2 postoperative infection, screening swab test <72 hours preoperatively (OR 0.76), community COVID-19 incidence (cases/10 5 people/week) (OR 1.011), preoperative cognitive impairment (OR 2.784), postoperative sepsis (OR 3.807) and an absence of postoperative complications (OR 0.188) were independently associated. Conclusions Perioperative SARS-CoV-2 infection in neurosurgical patients was associated with an increase in mortality by almost fivefold. Community COVID-19 incidence (cases/10 5 people/week) was a statistically independent predictor of mortality. Trial registration number CEIM 20/217

Inteligencia artificial en Radiología: introducción a los conceptos más importantes

[EN] The interpretation of medical imaging tests is one of the main tasks that radiologists do. For years, it has been a challenge to teach computers to do this kind of cognitive task; the main objective of the field of computer vision is to overcome this challenge. Thanks to technological advances, we are now closer than ever to achieving this goal, and radiologists need to become involved in this effort to guarantee that the patient remains at the center of medical practice. This article clearly explains the most important theoretical concepts in this area and the main problems or challenges at the present time; moreover, it provides practical information about the development of an artificial intelligence project in a radiology department.[ES] La interpretación de la imagen médica es una de las principales tareas que realiza el radiólogo. Conseguir que los ordenadores sean capaces de realizar este tipo de tareas cognitivas ha sido, durante años, un reto y a la vez un objetivo en el campo de la visión artificial. Gracias a los avances tecnológicos estamos ahora más cerca que nunca de conseguirlo y los radiólogos debemos involucrarnos en ello para garantizar que el paciente siga siendo el centro de la práctica médica. Este artículo explica de forma clara los conceptos teóricos más importantes de esta área y los principales problemas o retos actuales; además, aporta información práctica en relación con el desarrollo de un proyecto de inteligencia artificial en un servicio de Radiología.Peer reviewe

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

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

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