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Brain Disorder Detection and Diagnosis using Machine Learning and Deep Learning - A Bibliometric Analysis
Jyotismita Chaki1, Gopikrishna Deshpande2,3,4,5,6,7,8,9,10
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
Current Neuropharmacology
|June 7, 2024
Summary
This study analyzes machine learning and deep learning for brain disorder detection. Research is rapidly growing, with a focus on Alzheimer's, autism, and Parkinson's disease, highlighting key trends and future research directions.
Area of Science:
- Neurology
- Computer Science
- Medical Informatics
Background:
- Brain disorders represent a significant global mortality challenge, underscoring the critical need for early detection and diagnosis.
- Machine learning (ML) and deep learning (DL) are emerging as powerful tools for identifying and diagnosing neurological conditions.
- A comprehensive bibliometric analysis is essential to map the evolving landscape of ML/DL applications in brain disorder research.
Purpose of the Study:
- To conduct a quantitative bibliometric analysis of research on machine learning and deep learning for brain disorder detection and diagnosis.
- To identify key trends, influential works, and collaborative patterns within this rapidly advancing field.
- To provide insights that can guide future research directions and accelerate progress in the application of AI for neurological health.
Main Methods:
- A bibliometric analysis was performed on 1550 articles published between 2015 and May 2023, sourced from the Scopus database.
- Data analysis utilized Biblioshiny and VOSviewer platforms to examine citation patterns, collaboration metrics, and keyword frequencies.
- The study focused on automated detection and diagnosis of brain disorders using ML and DL techniques.
Main Results:
- Research output has shown a consistent upward trend, peaking in 2022.
- Multiclass classification and convolutional neural network models are dominant approaches.
- Alzheimer's disease, autism, and Parkinson's disease are the most frequently studied conditions.
- The USA, China, and India are leading countries in terms of author and institute collaborations.
Conclusions:
- This bibliometric analysis offers valuable insights into the current trends in ML and DL for brain disorder detection.
- The findings highlight key areas of focus and identify opportunities for future research and development.
- The study provides a roadmap for researchers to advance the application of AI in diagnosing and managing brain disorders.
