Machine learning approach to support taxonomic species discrimination based on helminth collections data

Abstract

Background There are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Eggs are the parasitic structures most viewed in coprological analysis in both modern and ancient samples; consequently, their presence is indicative of positive diagnosis for infection. The structure of the egg could play a role in genera or species discrimination. Institutional biological collections are taxonomic repositories of specimens described and strictly identified by systematics specialists. Methods The present work aims to characterize eggs of capillariid species deposited in institutional helminth collections and to process the morphological, morphometric and ecological data using machine learning (ML) as a new approach for taxonomic identification. Specimens of 28 species and 8 genera deposited at Coleção Helmintológica do Instituto Oswaldo Cruz (CHIOC, IOC/FIOCRUZ/Brazil) and Collection de Nématodes Zooparasites du Muséum National d’Histoire Naturelle de Paris (MNHN/France) were examined under light microscopy. In the morphological and morphometric analyses (MM), the total length and width of eggs as well as plugs and shell thickness were considered. In addition, eggshell ornamentations and ecological parameters of the geographical location (GL) and host (H) were included. Results The performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores. Conclusions The present research proposed a novel procedure for taxonomic species identification, integrating data from centenary biological collections and the logic of artificial intelligence techniques. This study will support future research on taxonomic identification and diagnosis of both modern and archaeological capillariids. Graphical abstract

First detection and low prevalence of Pearsonema spp. in wild raccoons (Procyon lotor) from Central Europe

“…Measurements of Pearsonema eggs were made using a digital image analysis system (Microimage 4, Microsoft Corp.) at 400× magnification following Borba et al (2021) . The measurements were performed on 25 undamaged eggs, selected regardless of their study area of origin.…”

Variability ofCalodium hepaticumeggs from sigmodontine host species through geometric morphometric analysis

“…Interestingly, C. hepaticum eggs from Meriones persicus and R. rattus are described as having a ‘very peculiar morphology’, that is, a punctuated and a radial eggshell ornamentation, with a thick eggshell (5.54 μm) (Borba et al ., 2021b). Furthermore, Borba et al (2021b) recently reported a new methodology for Capillariid species identification based on artificial intelligence technology. In this case, despite having taken the measurements of the two mucoid polar opercula, the discriminant analysis between representatives of Capillariidae was carried out using only the length and width of the eggs as measurements (Borba et al ., 2021b).…”

“…Furthermore, Borba et al (2021b) recently reported a new methodology for Capillariid species identification based on artificial intelligence technology. In this case, despite having taken the measurements of the two mucoid polar opercula, the discriminant analysis between representatives of Capillariidae was carried out using only the length and width of the eggs as measurements (Borba et al ., 2021b).…”

“…(Petrželková et al , 2010; Klaus et al , 2017; Garcia-Sánchez et al ., 2020; Kamani et al , 2021), or species of the genera Aonchotheca , Baruscapillaria, Capillaria, Calodium , Echinocoleus , Eucoleus , Pearsonema and Tridentocapillaria (Borba, 2019; Borba et al , 2021a). Interestingly, C. hepaticum eggs from Meriones persicus and R. rattus are described as having a ‘very peculiar morphology’, that is, a punctuated and a radial eggshell ornamentation, with a thick eggshell (5.54 μm) (Borba et al ., 2021b). Furthermore, Borba et al (2021b) recently reported a new methodology for Capillariid species identification based on artificial intelligence technology.…”

“…The most widely used approach for the C. hepaticum infection has been based on morphology, morphometry and histopathology (Ferreira & Andrade, 1993; Andrade & Andrade, 2004; Almeida et al ., 2012; Duque et al ., 2012; Fantozzi et al ., 2019). In turn, a novel procedure for taxonomic species identification through morphological and morphometric characteristics of parasite eggs in capillariids has recently been proposed (Borba et al ., 2021b).…”