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A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder
Xuanyi Li1, Qiang Kang2, Hanxing Gu3
1Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Frontiers in Human Neuroscience
|November 29, 2023
Summary
Obsessive-compulsive disorder (OCD) is increasingly diagnosed. Artificial intelligence and neuroimaging offer new ways to understand OCD neurological changes beyond traditional scales.
Area of Science:
- Neuroscience
- Psychiatry
- Artificial Intelligence
Background:
- Obsessive-compulsive disorder (OCD) is a prevalent mental health condition with rising incidence rates.
- Traditional diagnostic methods relying solely on clinical scales are becoming insufficient.
- Neuroimaging is emerging as a crucial tool for understanding the neurological underpinnings of OCD.
Approach:
- This article reviews recent advancements in artificial intelligence (AI) applications within neuroimaging for OCD research.
- AI and machine learning enhance the display and interpretation of complex neuroimaging data.
- The focus is on exploring the link between neurological function changes and OCD.
Key Points:
- AI facilitates the analysis of neuroimaging data to identify biomarkers for OCD.
- Machine learning models can potentially aid in early diagnosis and personalized treatment strategies for OCD.
- Neuroimaging combined with AI provides a more objective measure of neurological alterations in OCD patients.
Conclusions:
- AI-driven neuroimaging analysis represents a significant step forward in understanding and potentially treating OCD.
- Future research will likely leverage these technologies for more precise diagnostic and therapeutic interventions.
- Integrating AI into neuroimaging offers a promising avenue for advancing OCD research and clinical practice.
