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One possible way to resolve this visualization issue is to create digital 3D models which may aid researchers in studying morphology and function of the monogenean. However, the development of 3D models is a tedious procedure as one will have to repeat an entire complicated modelling process for every new target 3D shape using a comprehensive 3D modelling software. This study was designed to develop an alternative 3D modelling approach to build 3D models of monogenean anchors, which can be used to understand these morphological structures in three dimensions. This alternative 3D modelling approach is aimed to avoid repeating the tedious modelling procedure for every single target 3D model from scratch.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Result<\/jats:title>\n<jats:p>An automated 3D modeling pipeline empowered by an Artificial Neural Network (ANN) was developed. This automated 3D modelling pipeline enables automated deformation of a generic 3D model of monogenean anchor into another target 3D anchor. The 3D modelling pipeline empowered by ANN has managed to automate the generation of the 8 target 3D models (representing 8 species: <jats:italic>Dactylogyrus primaries, Pellucidhaptor merus, Dactylogyrus falcatus, Dactylogyrus vastator, Dactylogyrus pterocleidus, Dactylogyrus falciunguis, Chauhanellus auriculatum<\/jats:italic> and <jats:italic>Chauhanellus caelatus<\/jats:italic>) of monogenean anchor from the respective 2D illustrations input without repeating the tedious modelling procedure.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Despite some constraints and limitation, the automated 3D modelling pipeline developed in this study has demonstrated a working idea of application of machine learning approach in a 3D modelling work. This study has not only developed an automated 3D modelling pipeline but also has demonstrated a cross-disciplinary research design that integrates machine learning into a specific domain of study such as 3D modelling of the biological structures.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-019-3210-x","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T02:02:43Z","timestamp":1577152963000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An automated 3D modeling pipeline for constructing 3D models of MONOGENEAN HARDPART using machine learning techniques"],"prefix":"10.1186","volume":"20","author":[{"given":"Bee Guan","family":"Teo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9579-9502","authenticated-orcid":false,"given":"Sarinder Kaur","family":"Dhillon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"issue":"2","key":"3210_CR1","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1645\/GE-3544RN.1","volume":"92","author":"P Galli","year":"2006","unstructured":"Galli P, Strona G, Villa AM, Benzoni F, Doglia SM, Kritsky DC. 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