Intestinal parasitic infections are currently a source of concern for Public Health agencies in developing and developed countries. Since three ovum-and-parasite stool examinations have been demonstrated to provide sensitive results, we designed a practical and economical kit (TF-Test) that is now commercially available (Immunoassay Com. Ind. Ltda., São Paulo, Brazil). This kit allows the separate collection of three fecal specimens into a preservative solution. The specimens are then pooled, double-filtered, and concentrated by a single rapid centrifugation process. The TF-Test was evaluated in four different laboratories in a study using 1,102 outpatients and individuals living in an endemic area for enteroparasitosis. The overall sensitivity found using the TF-Test (86.2-97.8%) was significantly higher (P<0.01) than the sensitivity of conventional techniques such as the Coprotest (NL Comércio Exterior Ltda, São Paulo, Brazil) and the combination of Lutz/Hoffman, Faust, and Rugai techniques (De Carli, Diagnóstico Laboratorial das Parasitoses Humanas. Métodos e Técnicas, 1994), which ranged from 48.3% to 75.9%. When the above combined three specimen technique was repeated with three specimens collected on different days, its sensitivity became similar (P>0.01) to that of the TF-Test. The kappa index values of agreement for the TF-Test were consistent (P<0.01), being higher and ranking in a better position than conventional techniques. The high sensitivity, cost/benefit ratio, and practical aspects demonstrate that the TF-Test is suitable for individual diagnosis, epidemiological inquiries, or evaluation of chemotherapy in treated communities.

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis.

Intestinal parasitosis is highly prevalent worldwide, being among the main causes of illness and death in humans. Currently, laboratory diagnosis of the intestinal parasites is accomplished through manual technical procedures, mostly developed decades ago, which justifies the development of more sensitive and practical techniques. Therefore, the main objective of this study was to develop, evaluate, and validate a new parasitological technique referred to as TF-Test Modified, in comparison to three conventional parasitological techniques: TF-Test Conventional; Rugai, Mattos & Brisola; and Helm Test/Kato-Katz. For this realization, we collected stool samples from 457 volunteers located in endemic areas of Campinas, São Paulo, Brazil, and statistically compared the techniques. Intestinal protozoa and helminths were detected qualitatively in 42.23% (193/457) of the volunteers by TF-Test Modified technique, against 36.76% (168/457) by TF-Test Conventional, 5.03% (23/457) by Helm Test/Kato-Katz, and 4.16% (19/457) by Rugai, Mattos & Brisola. Furthermore, the new technique presented "almost perfect kappa" agreement in all evaluated parameters with 95% (P < 0.05) of estimation. The current study showed that the TF-Test Modified technique can be comprehensively used in the diagnosis of intestinal protozoa and helminths, and its greater diagnostic sensitivity should help improving the quality of laboratory diagnosis, population surveys, and control of intestinal parasites.

Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.