@article{fdi:010077969,
  title = {{F}asciola gigantica, {F}. hepatica and {F}asciola intermediate forms: geometric morphometrics and an artificial neural network to help morphological identification},
  author = {{S}umruayphol, {S}. and {S}iribat, {P}. and {D}ujardin, {J}ean-{P}ierre and {D}ujardin, {S}. and {K}omalamisra, {C}. and {T}haenkham, {U}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground. {F}asciola hepatica and {F}. gigantica cause fascioliasis in both humans and livestock. {S}ome adult specimens of {F}asciola sp. referred to as "intermediate forms" based on their genetic traits, are also frequently reported. {S}imple morphological criteria are unreliable for their specific identification. {I}n previous studies, promising phenotypic identification scores were obtained using morphometrics based on linear measurements (distances, angles, curves) between anatomical features. {S}uch an approach is commonly termed "traditional" morphometrics, as opposed to "modem" morphometrics, which is based on the coordinates of anatomical points. {M}ethods. {H}ere, we explored the possible improvements that modern methods of morphometrics, including landmark-based and outline-based approaches, could bring to solving the problem of the non-molecular identification of these parasites. {F}. gigantica and {F}asciola intermediate forms suitable for morphometric characterization were selected from {T}hai strains following their molecular identification. {S}pecimens of {F}. hepatica were obtained from the {L}iverpool {S}chool of {T}ropical {M}edicine ({UK}). {U}sing these three taxa, we tested the taxonomic signal embedded in traditional linear measurements versus the coordinates of anatomical points (landmark- and outline-based approaches). {V}arious statistical techniques of validated reclassification were used, based on either the shortest {M}ahalanobis distance, the maximum likelihood, or the artificial neural network method. {R}esults. {O}ur results revealed that both traditional and modern morphometric approaches can help in the morphological identification of {F}asciola sp. {W}e showed that the accuracy of the traditional approach could be improved by selecting a subset of characters among the most contributive ones. {T}he influence of size on discrimination by shape was much more important in traditional than in modern analyses. {I}n our study, the modern approach provided different results according to the type of data: satisfactory when using pseudolandmarks (outlines), less satisfactory when using landmarks. {T}he different reclassification methods provided approximately similar scores, with a special mention to the neural network, which allowed improvements in accuracy by combining data from both morphometric approaches. {C}onclusion. {W}e conclude that morphometrics, whether traditional or modern, represent a valuable tool to assist in {F}asciola species recognition. {T}he general level of accuracy is comparable among the various methods, but their demands on skills and time differ. {B}ased on the outline method, our study could provide the first description of the shape differences between species, highlighting the more globular contours of the intermediate forms.},
  keywords = {{F}asciola ; {M}olecular identification ; {ITS}1&{ITS}2 markers ; {M}orphometrics ; {A}rtificial neural networks ; {THAILANDE}},
  booktitle = {},
  journal = {{P}eer{J}},
  volume = {8},
  numero = {},
  pages = {e8597 [25 p.]},
  ISSN = {2167-8359},
  year = {2020},
  DOI = {10.7717/peerj.8597},
  URL = {https://www.documentation.ird.fr/hor/fdi:010077969},
}