An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images

Trajectory-driven computational analysis for element characterization in Trypanosoma cruzi video microscopy

Optical microscopy videos enable experts to analyze the motion of several biological elements. Particularly in blood samples infected with Trypanosoma cruzi (T. cruzi), microscopy videos reveal a dynamic scenario where the parasites' motions are conspicuous. While parasites have self-motion, cells are inert and may assume some displacement under dynamic events, such as fluids and microscope focus adjustments. This paper analyzes the trajectory of T. cruzi and blood cells to discriminate between these elements by identifying the following motion patterns: collateral, fluctuating, and pan-tilt-zoom (PTZ). We consider two approaches: i) classification experiments for discrimination between parasites and cells; and ii) clustering experiments to identify the cell motion. We propose the trajectory step dispersion (TSD) descriptor based on standard deviation to characterize these elements, outperforming state-of-the-art descriptors. Our results confirm motion is valuable in discriminating T. cruzi of the cells. Since the parasites perform the collateral motion, their trajectory steps tend to randomness. The cells may assume fluctuating motion following a homogeneous and directional path or PTZ motion with trajectory steps in a restricted area. Thus, our findings may contribute to developing new computational tools focused on trajectory analysis, which can advance the study and medical diagnosis of Chagas disease.

Chagas parasite classification in blood sample images using different machine learning architectures

Chagas disease is a life-threatening illness mainly found in Latin America. Early identification and diagnosis of Chagas disease are critical for reducing the death rate of individuals since cures and treatments are available at the acute stage. In this work, we test and compare several deep learning classification models on smear blood sample images for the task of Chagas parasite classification. Our experiments showed that the best classification model is a deep learning architecture based on a residual network together with separable convolution blocks as feature extractors and using a support vector machine algorithm as the classifier in the final layer. This optimized model, we named Res2_SVM, with a reduced number of parameters, achieved an accuracy of [Formula: see text], precision of [Formula: see text], recall of [Formula: see text], and F1-score of [Formula: see text] on our test dataset, overcoming other machine learning models.

Automatic detection of the parasite Trypanosoma cruzi in blood smears using a machine learning approach applied to mobile phone images

Chagas disease is a life-threatening illness caused by the parasite Trypanosoma cruzi. The diagnosis of the acute form of the disease is performed by trained microscopists who detect parasites in blood smear samples. Since this method requires a dedicated high-resolution camera system attached to the microscope, the diagnostic method is more expensive and often prohibitive for low-income settings. Here, we present a machine learning approach based on a random forest (RF) algorithm for the detection and counting of T. cruzi trypomastigotes in mobile phone images. We analyzed micrographs of blood smear samples that were acquired using a mobile device camera capable of capturing images in a resolution of 12 megapixels. We extracted a set of features that describe morphometric parameters (geometry and curvature), as well as color, and texture measurements of 1,314 parasites. The features were divided into train and test sets (4:1) and classified using the RF algorithm. The values of precision, sensitivity, and area under the receiver operating characteristic (ROC) curve of the proposed method were 87.6%, 90.5%, and 0.942, respectively. Automating image analysis acquired with a mobile device is a viable alternative for reducing costs and gaining efficiency in the use of the optical microscope.

Collateral motion saliency-based model for Trypanosoma cruzi detection in dye-free blood microscopy

The motion performed by some protozoa is a crucial visual stimulus in microscopy analysis, especially when they have almost imperceptible morphological characteristics. Microorganisms can be distinguished through the interactions of their locomotion with neighboring elements, as observed in some parasitological analysis of Trypanosoma cruzi. In dye-free blood microscopy, the low contrast of this parasite makes it difficult to detect them. Thus, the parasite's interaction with the neighborhood, such as collisions with blood cells and shocks during the escape of confinements in cell clumps, generates collateral motions that assist its detection. Assuming that the collateral motion of the parasite can be sufficiently noticeable to overcome the dynamic contexts of inspection, we propose a novel computational approach that is based on motion saliency. We estimate motion in microscopy videos using dense optical flow and we investigate vestiges in saliency maps that could characterize the collateral motion of parasites. Our biological-inspired method shows that the parasite's collateral motion is a relevant feature for T. cruzi detection. Therefore, our computational model is a promising aid in the research and medical diagnosis of Chagas disease.

Flavonoids as efficient scaffolds: Recent trends for malaria, leishmaniasis, Chagas disease, and dengue

Endemic in 149 tropical and subtropical countries, neglected tropical diseases (NTDs) affect more than 1 billion people annually with over 500,000 deaths. Among the NTDs, some of the most severe consist of leishmaniasis, Chagas disease, and dengue. The impact of the combined NTDs closely rivals that of malaria. According to the World Health Organization, 216 million cases of malaria were reported in 2016 with 445,000 deaths. Current treatment options are associated with various limitations including widespread drug resistance, severe adverse effects, lengthy treatment duration, unfavorable toxicity profiles, and complicated drug administration procedures. Flavonoids are a class of compounds that has been the subject of considerable scientific interest. New developments of flavonoids have made promising advances for the potential treatment of malaria, leishmaniasis, Chagas disease, and dengue, with less toxicity, high efficacy, and improved bioavailability. This review summarizes the current standings of the use of flavonoids to treat malaria and neglected diseases such as leishmaniasis, Chagas disease, and dengue. Natural and synthetic flavonoids are leading compounds that can be used for developing antiprotozoal and antiviral agents. However, detailed studies on toxicity, pharmacokinetics, and mechanisms of action of these compounds are required to confirm the in vitro pharmacological claims of flavonoids for pharmaceutical applications. HIGHLIGHTS: In the current review, we have tried to compile recent discoveries on natural and synthetic flavonoids as well as their implication in the treatment of malaria, leishmaniasis, Chagas disease, and dengue. A total of 373 (220 natural and 153 synthetic) flavonoids have been evaluated for antimalarial, antileishmanial, antichagasic, and antidengue activities. Most of these flavonoids showed promising results against the above diseases. Reports on molecular modeling of flavonoid compounds to the disease target indicated encouraging results. Flavonoids can be prospected as potential leads for drug development; however, more rigorously designed studies on toxicity and pharmacokinetics, as well as the quantitative structure-activity relationship studies of these compounds, need to be addressed.

Chagas parasite detection in blood images using AdaBoost

The Chagas disease is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. Visual detection of such parasite through microscopic inspection is a tedious and time-consuming task. In this paper, we provide an AdaBoost learning solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup. With this method, we get 100% and 93.25% of sensitivity and specificity, respectively. A ROC comparison with the method most commonly used for the detection of malaria parasites based on support vector machines (SVM) is also provided. Our experimental work shows mainly two things: (1) Chagas parasites can be detected automatically using machine learning methods with high accuracy and (2) AdaBoost + SVM provides better overall detection performance than AdaBoost or SVMs alone. Such results are the best ones known so far for the problem of automatic detection of Chagas parasites through the use of machine learning, computer vision, and image processing methods.