{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:29:09Z","timestamp":1776842949666,"version":"3.51.2"},"reference-count":74,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Foundation for Science and Technology (FCT)","doi-asserted-by":"publisher","award":["UI\/BD\/150902\/2021"],"award-info":[{"award-number":["UI\/BD\/150902\/2021"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Foundation for Science and Technology (FCT)","doi-asserted-by":"publisher","award":["MAR-021.1.3-FEAMPA-00004"],"award-info":[{"award-number":["MAR-021.1.3-FEAMPA-00004"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"INOVAQUA","award":["UI\/BD\/150902\/2021"],"award-info":[{"award-number":["UI\/BD\/150902\/2021"]}]},{"name":"INOVAQUA","award":["MAR-021.1.3-FEAMPA-00004"],"award-info":[{"award-number":["MAR-021.1.3-FEAMPA-00004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMSE"],"abstract":"<jats:p>The gut is the first organ to contact food, and it is often the target of nutrition studies performed on aquaculture fish. Histological analysis reveals morphological changes in fish intestines caused by ingredients in formulated feeds. However, this type of analysis is mainly based on a semi-quantitative approach, often restricted to specialized researchers, and may provide inconsistent results between studies. This study addresses these limitations by combining semi-quantitative and quantitative features to characterize the anterior, intermediate, and distal sections of the intestine of meagre (Argyrosomus regius) subjected to different nutritional status. Collected data were used to build machine learning models, select the most accurate ones, and identify key features for predicting malnutrition. Logistic regression, support vector machines, and ensemble stacking performed best across all intestinal sections. Combining semi-quantitative and quantitative features yielded the best predictions, with villi number, density and area, and goblet cell count being the most crucial for the classification task. When considering the distal intestine alone, semi-quantitative features outperformed quantitative ones. The intermediate section of the intestine showed the best model accuracy, indicating higher sensitivity to nutritional changes. These results demonstrate the potential of machine learning models to streamline histomorphological analyses to evaluate nutritional status, making them more accessible and standard across users.<\/jats:p>","DOI":"10.3390\/jmse12122177","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T05:19:39Z","timestamp":1732771179000},"page":"2177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine Learning-Based Classification of Malnutrition Using Histological Biomarkers of Fish Intestine: Preliminary Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7885-0262","authenticated-orcid":false,"given":"Joana","family":"Oliveira","sequence":"first","affiliation":[{"name":"FCUP\u2014Department of Biology, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"CIIMAR\u2014Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4050-208 Matosinhos, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6965-0958","authenticated-orcid":false,"given":"Marisa","family":"Barata","sequence":"additional","affiliation":[{"name":"IPMA\u2014EPPO\u2014Portuguese Institute for Sea and Atmosphere\u2014Aquaculture Research Station, 8700-194 Olh\u00e3o, Portugal"},{"name":"S2AQUA\u2014Collaborative Laboratory, Association for a Sustainable and Smart Aquaculture, 8700-194 Olh\u00e3o, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-2472","authenticated-orcid":false,"given":"Florbela","family":"Soares","sequence":"additional","affiliation":[{"name":"IPMA\u2014EPPO\u2014Portuguese Institute for Sea and Atmosphere\u2014Aquaculture Research Station, 8700-194 Olh\u00e3o, Portugal"},{"name":"S2AQUA\u2014Collaborative Laboratory, Association for a Sustainable and Smart Aquaculture, 8700-194 Olh\u00e3o, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6746-764X","authenticated-orcid":false,"given":"Pedro","family":"Pous\u00e3o-Ferreira","sequence":"additional","affiliation":[{"name":"IPMA\u2014EPPO\u2014Portuguese Institute for Sea and Atmosphere\u2014Aquaculture Research Station, 8700-194 Olh\u00e3o, Portugal"},{"name":"S2AQUA\u2014Collaborative Laboratory, Association for a Sustainable and Smart Aquaculture, 8700-194 Olh\u00e3o, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5730-836X","authenticated-orcid":false,"given":"Aires","family":"Oliva-Teles","sequence":"additional","affiliation":[{"name":"FCUP\u2014Department of Biology, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"CIIMAR\u2014Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4050-208 Matosinhos, Portugal"}]},{"given":"Ana","family":"Couto","sequence":"additional","affiliation":[{"name":"FCUP\u2014Department of Biology, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"CIIMAR\u2014Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4050-208 Matosinhos, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kim, S.-K. 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