Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review.
Qian Wu, Hui Guo, Ruihan Li et al.
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In 30 seconds
This systematic review examined the application of artificial intelligence (AI) in diagnosing Preserved Ratio Impaired Spirometry (PRISm) across eleven studies. The findings indicate that AI technologies, including machine learning and deep learning, show high performance in diagnostic tasks, with multi-source data integration yielding superior results. The review highlights the potential for AI to enhance early identification and risk stratification of PRISm.
Key findings
- Eleven studies were included, focusing on diagnostic and classification tasks in PRISm.
- Nine studies were published within the last two years, indicating a growing interest in AI applications.
- Modeling strategies integrating multi-source data demonstrated superior discriminatory performance compared to single-modality approaches.
Why it matters
The integration of AI in diagnosing PRISm could significantly improve early detection and precision care, ultimately enhancing patient outcomes in respiratory diseases. Understanding these advancements is crucial for clinicians involved in respiratory health.