What is new in computer vision and artificial intelligence in medical image analysis applications.

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.

Impact of the COVID-19 pandemic on the pediatric population with acute appendicitis: Experience at a general, tertiary care hospital. / Impacto de la pandemia por COVID-19 en la población pediátrica con apendicitis aguda: experiencia en un hospital general de tercer nivel.

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.

COVID-19 Among American Indian and Alaska Native Persons - 23 States, January 31-July 3, 2020.

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.