DaTSCAN, SPECT con 123 I – Ioflupano. Su papel en el diagnóstico diferencial del Temblor Esencial.

I. RESUMEN. 1.1. Objetivos. Registrar, clasificar y procesar 250 peticiones consecutivas para la prueba de DaTSCAN, para obtener qué porcentaje de estas se solicitan con el objetivo de realizar un diagnóstico diferencial entre Temblor Esencial (TE) y Parkinsonismos Primarios. Posteriormente, analizar los informes realizados por los especialistas en medicina nuclear, estudiando su concordancia con la sospecha clínica. Por último, obtener qué porcentaje de informes otorga un resultado concluyente en relación al diagnóstico diferencial entre Temblor Esencial y Parkinsonismos Primarios. 1.2. Material y Métodos. Se trata de un estudio descriptivo y retrospectivo. Han sido procesadas 250 solicitudes consecutivas para la prueba de DaTSCAN del Servicio de Medicina Nuclear del Hospital Universitario Miguel Servet (HUMS), entre enero de 2015 y mayo de 2016, así como de los informes con el resultado de la prueba. 1.3. Resultados. De las 250 solicitudes analizadas, 167 (66,80%) tenían como indicación una sospecha clínica de Parkinsonismo Primario y 31 (12,40%) planteaban un diagnóstico diferencial con Temblor Esencial. El resto presentaban como motivo de solicitud realizar un diagnóstico diferencial con Parkinsonismos farmacológicos o vasculares, confirmación diagnóstica por mala respuesta a tratamiento dopaminérgico u otros motivos no clasificables. De los 166 informes correspondientes con un diagnóstico de presunción de Parkinsonismo Primario: 160 (96,39%) son concluyentes para confirmar o descartar la alteración de la vía dopaminérgica presináptica y 6 (3,61%) son no concluyentes. En cuanto a los 83 informes en los que la petición de la prueba estaba orientada desde un diagnóstico diferencial con los Parkinsonismos: 77 (92,77%) dan un resultado concluyente y 6 (7,23%) no. Por último, en relación a los 31 informes correspondientes con un diagnóstico diferencial con Temblor Esencial: 28 (90,32%) presentan un resultado concluyente y 3 (9,68%) no concluyente. 1.4. Conclusiones. Del total de peticiones, un 12,40% venían solicitadas con un diagnóstico diferencial de Temblor Esencial. El estudio de DaTSCAN otorga un resultado concluyente entre un 96,39% y un 92,77% de los casos, en función del si el motivo de solicitud viene expresado como Parkinsonismo Primario o si se plantea un diagnóstico diferencial, respectivamente. En las peticiones definidas como diagnóstico diferencial con el Temblor Esencial, la prueba fue concluyente en un 90,32% de los casos. 1.5. Palabras Clave. DaTSCAN, 123-Ioflupano, Temblor Esencial, Enfermedad de Parkinson, Parkinsonismos Primarios

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

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

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

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

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