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人工智能与奶牛乳腺炎研究的当前趋势：一种文献计量学综述方法

Current Trends in Artificial Intelligence and Bovine Mastitis Research: A Bibliometric Review Approach.

作者信息

Mitsunaga Thatiane Mendes, Nery Garcia Breno Luis, Pereira Ligia Beatriz Rizzanti, Costa Yuri Campos Braga, da Silva Roberto Fray, Delbem Alexandre Cláudio Botazzo, Dos Santos Marcos Veiga

机构信息

Luiz de Queiroz College of Agriculture-ESALQ, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-900, SP, Brazil.

School of Veterinary Medicine and Animal Science, University of São Paulo, Pirassununga 13635-900, SP, Brazil.

出版信息

Animals (Basel). 2024 Jul 9;14(14):2023. doi: 10.3390/ani14142023.

DOI:10.3390/ani14142023
PMID:39061485
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11273831/
Abstract

Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area.

摘要

乳腺炎是奶牛的一种重要疾病，会给牛群的盈利能力造成重大损失。准确诊断对于有效防控至关重要。利用人工智能（AI）模型对乳腺炎进行分类、识别、预测和诊断的研究，在改善乳腺炎防控方面显示出了前景。这篇文献计量学综述旨在评估2011年至2021年Scopus索引的最相关论文中关于人工智能和牛乳腺炎的术语。分析了62篇文献，揭示了关键术语、杰出研究人员、相关出版物、主要主题和关键词聚类。“乳腺炎”和“机器学习”是被引用最多的术语，从2018年到2021年呈上升趋势。其他术语，如“传感器”和“乳腺炎检测”也出现了。美国是被引用最多的国家，并且呈现出最大的合作网络。关于乳腺炎和人工智能模型的出版物从2016年到2021年显著增加，表明兴趣日益浓厚。然而，很少有研究将人工智能用于牛乳腺炎检测，主要采用人工神经网络模型。这表明该领域有明显的进一步研究潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0327/11273831/d329b2eac50a/animals-14-02023-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0327/11273831/3af1a3ce85bf/animals-14-02023-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0327/11273831/2eedd9bac74e/animals-14-02023-g009.jpg
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Novel ways to use sensor data to improve mastitis management.
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