Abstract：
OBJECTIVE: Brain disorders are one of the major global mortality issues, and their early detection is crucial for healing. Machine learning, specifically deep learning, is a technology that is increasingly being used to detect and diagnose brain disorders. Our objective is to provide a quantitative bibliometric analysis of the field to inform researchers about trends that can inform their Research directions in the future.
METHODS: We carried out a bibliometric analysis to create an overview of brain disorder detection and diagnosis using machine learning and deep learning. Our bibliometric analysis includes 1550 articles gathered from the Scopus database on automated brain disorder detection and diagnosis using machine learning and deep learning published from 2015 to May 2023. A thorough bibliometric análisis is carried out with the help of Biblioshiny and the VOSviewer platform. Citation analysis and various measures of collaboration are analyzed in the study.
RESULTS: According to a study, maximum research is reported in 2022, with a consistent rise from preceding years. The majority of the authors referenced have concentrated on multiclass classification and innovative convolutional neural network models that are effective in this field. A keyword analysis revealed that among the several brain disorder types, Alzheimer\'s, autism, and Parkinson\'s disease had received the greatest attention. In terms of both authors and institutes, the USA, China, and India are among the most collaborating countries. We built a future research agenda based on our findings to help progress research on machine learning and deep learning for brain disorder detection and diagnosis.
CONCLUSIONS: In summary, our quantitative bibliometric analysis provides useful insights about trends in the field and points them to potential directions in applying machine learning and deep learning for brain disorder detection and diagnosis..
METHODS: We carried out a bibliometric analysis to create an overview of brain disorder detection and diagnosis using machine learning and deep learning. Our bibliometric analysis includes 1550 articles gathered from the Scopus database on automated brain disorder detection and diagnosis using machine learning and deep learning published from 2015 to May 2023. A thorough bibliometric análisis is carried out with the help of Biblioshiny and the VOSviewer platform. Citation analysis and various measures of collaboration are analyzed in the study.
RESULTS: According to a study, maximum research is reported in 2022, with a consistent rise from preceding years. The majority of the authors referenced have concentrated on multiclass classification and innovative convolutional neural network models that are effective in this field. A keyword analysis revealed that among the several brain disorder types, Alzheimer\'s, autism, and Parkinson\'s disease had received the greatest attention. In terms of both authors and institutes, the USA, China, and India are among the most collaborating countries. We built a future research agenda based on our findings to help progress research on machine learning and deep learning for brain disorder detection and diagnosis.
CONCLUSIONS: In summary, our quantitative bibliometric analysis provides useful insights about trends in the field and points them to potential directions in applying machine learning and deep learning for brain disorder detection and diagnosis..
摘要：
目的：脑部疾病是全球主要的死亡率问题之一，它们的早期发现对治愈至关重要。机器学习,特别是深度学习，是一种越来越多地用于检测和诊断脑部疾病的技术。我们的目标是提供该领域的定量文献计量分析，以告知研究人员未来可以告知其研究方向的趋势。
方法：我们进行了文献计量分析，以使用机器学习和深度学习对脑部疾病的检测和诊断进行概述。我们的文献计量分析包括从Scopus数据库收集的1550篇关于使用机器学习和深度学习进行自动化脑部疾病检测和诊断的文章，发表于2015年至2023年5月。在Biblioshiny和VOSviewer平台的帮助下，进行了全面的文献计量分析。在研究中分析了引文分析和各种合作措施。
结果：根据一项研究，据报道，2022年的研究最多，比前几年持续增长。引用的大多数作者都专注于在该领域有效的多类分类和创新的卷积神经网络模型。一项关键词分析显示，在几种脑部疾病类型中，老年痴呆症,自闭症,和帕金森病受到了最大的关注。就作者和研究所而言，美国,中国,和印度是最合作的国家之一。我们根据我们的发现制定了未来的研究议程，以帮助推进机器学习和深度学习在大脑疾病检测和诊断方面的研究。
结论：总之，我们的定量文献计量分析提供了有关该领域趋势的有用见解，并指出了将机器学习和深度学习应用于脑部疾病检测和诊断的潜在方向。。
方法：我们进行了文献计量分析，以使用机器学习和深度学习对脑部疾病的检测和诊断进行概述。我们的文献计量分析包括从Scopus数据库收集的1550篇关于使用机器学习和深度学习进行自动化脑部疾病检测和诊断的文章，发表于2015年至2023年5月。在Biblioshiny和VOSviewer平台的帮助下，进行了全面的文献计量分析。在研究中分析了引文分析和各种合作措施。
结果：根据一项研究，据报道，2022年的研究最多，比前几年持续增长。引用的大多数作者都专注于在该领域有效的多类分类和创新的卷积神经网络模型。一项关键词分析显示，在几种脑部疾病类型中，老年痴呆症,自闭症,和帕金森病受到了最大的关注。就作者和研究所而言，美国,中国,和印度是最合作的国家之一。我们根据我们的发现制定了未来的研究议程，以帮助推进机器学习和深度学习在大脑疾病检测和诊断方面的研究。
结论：总之，我们的定量文献计量分析提供了有关该领域趋势的有用见解，并指出了将机器学习和深度学习应用于脑部疾病检测和诊断的潜在方向。。
