SISTEMA INFORMÁTICO PARA LA CLASIFICACIÓN AUTOMÁTICA DE IMÁGENES DE GRANOS DE POLEN / COMPUTER SYSTEM FOR THE AUTOMATIC CLASSIFICATION OF POLLEN GRAIN IMAGES

La presente investigación ofrece un sistema que facilita el proceso de clasificación de imágenes de granos de polen de La Empresa Apícola Cubana, cuyos resultados demostraron que existe lentitud en el proceso de análisis polínicos de las mieles, ya que las especies que pertenecen a una misma familia comparten características de identificación. Por esta razón se hizo necesario desarrollar un sistema informático que permitiera a los técnicos melisopalinólogos clasificar automáticamente una imagen de grano de polen. La implementación del sistema se realizó utilizando Keras para la creación de redes neuronales convolucionales y Tensor Flow para el trabajo con imágenes, ambas librerías de Python lo que posibilita su empleo en cualquier plataforma. Para guiar el proceso de desarrollo se utilizó la metodología Rational Unified Process (RUP). El sistema propuesto posibilita el identificación y clasificación rápida de imágenes de granos de polen. Almacena un conjunto de datos que permite al sistema identificar las especies de plantas

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified