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EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-311360


In this work we present a method for the detection of radiological findings, their location and differential diagnoses from chest x-rays. Unlike prior works that focus on the detection of few pathologies, we use a hierarchical taxonomy mapped to the Unified Medical Language System (UMLS) terminology to identify 189 radiological findings, 22 differential diagnosis and 122 anatomic locations, including ground glass opacities, infiltrates, consolidations and other radiological findings compatible with COVID-19. We train the system on one large database of 92,594 frontal chest x-rays (AP or PA, standing, supine or decubitus) and a second database of 2,065 frontal images of COVID-19 patients identified by at least one positive Polymerase Chain Reaction (PCR) test. The reference labels are obtained through natural language processing of the radiological reports. On 23,159 test images, the proposed neural network obtains an AUC of 0.94 for the diagnosis of COVID-19. To our knowledge, this work uses the largest chest x-ray dataset of COVID-19 positive cases to date and is the first one to use a hierarchical labeling schema and to provide interpretability of the results, not only by using network attention methods, but also by indicating the radiological findings that have led to the diagnosis.

EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-323256


This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. The findings have been mapped onto standard Unified Medical Language System (UMLS) terminology and cover a wide spectrum of thoracic entities, unlike the considerably more reduced number of entities annotated in previous datasets. Images are stored in high resolution and entities are localized with anatomical labels and stored in a Medical Imaging Data Structure (MIDS) format. In addition, 10 images were annotated by a team of radiologists to include semantic segmentation of radiological findings. This first iteration of the database includes 1,380 CX, 885 DX and 163 CT studies from 1,311 COVID-19+ patients. This is, to the best of our knowledge, the largest COVID-19+ dataset of images available in an open format. The dataset can be downloaded from

Quant Imaging Med Surg ; 11(8): 3830-3853, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1410783


Computer vision and artificial intelligence applications in medicine are becoming increasingly important day by day, especially in the field of image technology. In this paper we cover different artificial intelligence advances that tackle some of the most important worldwide medical problems such as cardiology, cancer, dermatology, neurodegenerative disorders, respiratory problems, and gastroenterology. We show how both areas have resulted in a large variety of methods that range from enhancement, detection, segmentation and characterizations of anatomical structures and lesions to complete systems that automatically identify and classify several diseases in order to aid clinical diagnosis and treatment. Different imaging modalities such as computer tomography, magnetic resonance, radiography, ultrasound, dermoscopy and microscopy offer multiple opportunities to build automatic systems that help medical diagnosis, taking advantage of their own physical nature. However, these imaging modalities also impose important limitations to the design of automatic image analysis systems for diagnosis aid due to their inherent characteristics such as signal to noise ratio, contrast and resolutions in time, space and wavelength. Finally, we discuss future trends and challenges that computer vision and artificial intelligence must face in the coming years in order to build systems that are able to solve more complex problems that assist medical diagnosis.

Arch Argent Pediatr ; 119(4): 224-229, 2021 08.
Article in English, Spanish | MEDLINE | ID: covidwho-1325943


INTRODUCTION: Appendicitis is the leading cause of surgical acute abdomen in pediatrics. During the COVID-19 pandemic, management strategies were reassessed and the number of visits to the emergency department dropped down, which may be associated with delayed diagnoses and complications. The objective of this study was to analyze the impact of the pandemic on children with acute appendicitis. METHODS: Analytical, retrospective, comparative study of pediatric patients with acute appendicitis in the 5 months of COVID-19 lockdown versus the same period in the previous year. Incidence, clinical data, stage, surgical approach, and complications were analyzed. RESULTS: The total number of appendicitis cases went down by 25% (n = 67 versus n = 50 in 2020). The mean time to consultation was 24 hours in both periods (p = 0.989). The incidence of peritonitis was 44% (n = 22) versus 37% (n = 22) (p = 0.22) in 2019. No differences were observed in terms of appendicitis stage based on surgery reports. In 2019, all surgeries were laparoscopic; while in 2020, only 42% (n = 21). The incidence of complications was 6% versus 7.5% in the previous period (p = 0.75). One patient was COVID-19 positive. CONCLUSION: Although in our population the number of appendicitis cases dropped down, consultation was not delayed. The greater impact was associated with the reformulation of management strategies, in which the laparoscopic approach is avoided to reduce virus transmission.

Introducción. La apendicitis constituye la principal causa de abdomen agudo quirúrgico en pediatría. Durante la pandemia por COVID-19, se replantearon las estrategias de manejo y disminuyeron las consultas en las guardias, lo que podría asociarse a diagnósticos tardíos y complicaciones. El objetivo de este estudio fue analizar el impacto de la pandemia en los niños con apendicitis aguda. Métodos. Estudio analítico retrospectivo comparativo de pacientes pediátricos con apendicitis aguda durante los cinco meses del confinamiento por COVID-19 versus los meses equivalentes del año previo. Se analizaron la incidencia, la clínica, el estadio, el abordaje quirúrgico y las complicaciones. Resultados. Los casos totales de apendicitis se redujeron un 25% (n = 67 versus n = 50 en 2020). El tiempo medio hasta la consulta fue de 24 horas en ambos períodos (p = 0,989). La incidencia de peritonitis fue del 44% (n = 22) versus el 37% (n = 22) (p = 0,22) en 2019. No se evidenció diferencia en los estadios de enfermedad de acuerdo con lo informado en los partes quirúrgicos. En 2019, todas las cirugías se realizaron por vía laparoscópica; en 2020, solo un 42% (n = 21). La incidencia de complicaciones fue del 6%, contra 7,5% en el período previo (p = 0,75). Un paciente fue COVID-19 positivo. Conclusión. A pesar de la reducción en el número de casos de apendicitis, no se evidenció una demora en la consulta en nuestra población. El mayor impacto se asoció a la readecuación del manejo, evitando el abordaje laparoscópico para reducir la diseminación del virus.

Appendectomy/trends , Appendicitis , COVID-19/prevention & control , Delayed Diagnosis/trends , Health Services Accessibility/trends , Practice Patterns, Physicians'/trends , Acute Disease , Adolescent , Appendectomy/methods , Appendicitis/diagnosis , Appendicitis/epidemiology , Appendicitis/surgery , Argentina/epidemiology , COVID-19/diagnosis , COVID-19/epidemiology , Child , Child, Preschool , Female , Hospitals, General , Humans , Incidence , Infant , Infant, Newborn , Laparoscopy/trends , Male , Pandemics/prevention & control , Retrospective Studies , Tertiary Care Centers
MMWR Morb Mortal Wkly Rep ; 69(34): 1166-1169, 2020 Aug 28.
Article in English | MEDLINE | ID: covidwho-732630


Although non-Hispanic American Indian and Alaska Native (AI/AN) persons account for 0.7% of the U.S. population,* a recent analysis reported that 1.3% of coronavirus disease 2019 (COVID-19) cases reported to CDC with known race and ethnicity were among AI/AN persons (1). To assess the impact of COVID-19 among the AI/AN population, reports of laboratory-confirmed COVID-19 cases during January 22†-July 3, 2020 were analyzed. The analysis was limited to 23 states§ with >70% complete race/ethnicity information and five or more laboratory-confirmed COVID-19 cases among both AI/AN persons (alone or in combination with other races and ethnicities) and non-Hispanic white (white) persons. Among 424,899 COVID-19 cases reported by these states, 340,059 (80%) had complete race/ethnicity information; among these 340,059 cases, 9,072 (2.7%) occurred among AI/AN persons, and 138,960 (40.9%) among white persons. Among 340,059 cases with complete patient race/ethnicity data, the cumulative incidence among AI/AN persons in these 23 states was 594 per 100,000 AI/AN population (95% confidence interval [CI] = 203-1,740), compared with 169 per 100,000 white population (95% CI = 137-209) (rate ratio [RR] = 3.5; 95% CI = 1.2-10.1). AI/AN persons with COVID-19 were younger (median age = 40 years; interquartile range [IQR] = 26-56 years) than were white persons (median age = 51 years; IQR = 32-67 years). More complete case report data and timely, culturally responsive, and evidence-based public health efforts that leverage the strengths of AI/AN communities are needed to decrease COVID-19 transmission and improve patient outcomes.

Alaskan Natives/statistics & numerical data , Coronavirus Infections/ethnology , Health Status Disparities , Indians, North American/statistics & numerical data , Pneumonia, Viral/ethnology , Adolescent , Adult , Aged , Betacoronavirus/isolation & purification , COVID-19 , COVID-19 Testing , Child , Child, Preschool , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/therapy , Female , Humans , Incidence , Infant , Infant, Newborn , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/therapy , SARS-CoV-2 , Severity of Illness Index , Treatment Outcome , United States/epidemiology , Young Adult
Rev. colomb. cardiol ; 27(3): 142-152, May-June 2020. tab, graf
Article in Spanish | WHO COVID, LILACS (Americas) | ID: covidwho-655734


Resumen La infección por SARS-CoV2 es una pandemia. Se creía que el primer caso de esta enfermedad ocurrió el 8 de diciembre de 2019 en la provincia de Hubei en China, aunque posteriormente se indicó que el primer caso confirmado por laboratorio ocurrió el 1.( de diciembre de 2019 ante la presencia de un brote de neumonía en 59 pacientes sospechosos en un mercado local de mariscos en Wuhan. No solo produce patología respiratoria, con frecuencia compremete el sistema cardiovascular ya que produce lesión miocárdica, miocarditis, y, con cierta frecuencia, aumenta la descompensación de enfermedades cardiovasculares preestablecidas. En este artículo se trata de dilucidar el componente cardiovascular hasta ahora existente en la literatura y se sugieren algunos pasos a seguir en pacientes con estas enfermedades, acorde con la evidencia actual.

Abstract Infection due to SARS-CoV2 is a pandemic. It is believed that the first case occurred on 8 December 2019 in Hubei province in China, although it was later indicated that the first laboratory-confirmed case occurred on 1 December 2019 due to the presence of an outbreak of suspected pneumonia in 59 patients in a shellfish market in Wuhan. It not only caused a respiratory disease, it often compromised the cardiovascular system since it produces a myocardial lesion, myocarditis, and, less often, increased the decompensation of pre-established cardiovascular diseases. An attempt is made in this article to elucidate the cardiovascular component presented in the current literature, and to suggest some steps to follow in patients with these diseases in accordance with the current evidence.

Humans , Male , Female , Coronavirus , Heart Failure , Pneumonia , Respiratory Distress Syndrome, Newborn , Myocardial Reperfusion Injury , Severe Acute Respiratory Syndrome , Myocarditis