Growth, feed utilization, and quantitative histological assessment of the distal intestine and liver of common carp (Cyprinus carpio L.) fed formulated diets containing grains of different soybean cultivars

Rašković, Božidar; Stanković, Marko; Markelić, Milica; Poleksić, Vesna; Božić, Gavrilo; Janković, Snežana; Marković, Zoran

doi:10.1007/s10499-024-01494-4

Aquacult Int

2024

DOI: 10.1007/s10499-024-01494-4

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Growth, feed utilization, and quantitative histological assessment of the distal intestine and liver of common carp (Cyprinus carpio L.) fed formulated diets containing grains of different soybean cultivars

Božidar Rašković,

Marko Stanković,

Milica Markelić

et al.

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Machine Learning-Based Classification of Malnutrition Using Histological Biomarkers of Fish Intestine: Preliminary Data

Oliveira,

Barata,

Soares

et al. 2024

JMSE

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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.

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Machine Learning-Based Classification of Malnutrition Using Histological Biomarkers of Fish Intestine: Preliminary Data

Oliveira,

Barata,

Soares

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

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Growth, feed utilization, and quantitative histological assessment of the distal intestine and liver of common carp (Cyprinus carpio L.) fed formulated diets containing grains of different soybean cultivars

Cited by 1 publication

References 75 publications

Machine Learning-Based Classification of Malnutrition Using Histological Biomarkers of Fish Intestine: Preliminary Data

Machine Learning-Based Classification of Malnutrition Using Histological Biomarkers of Fish Intestine: Preliminary Data

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