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AI-based radiodiagnosis using chest X-rays: A review.
Yasmeena Akhter1, Richa Singh1, Mayank Vatsa1
1Indian Institute of Technology Jodhpur, Jodhpur, India.
Frontiers in Big Data
|April 24, 2023
Summary
Artificial intelligence (AI) shows promise for analyzing chest X-rays (CXRs) to detect lung diseases, but faces challenges like data privacy and model interpretability. This review explores AI applications, datasets, and challenges in CXR analysis for improved healthcare.
Area of Science:
- Radiological imaging analysis
- Artificial intelligence in healthcare
- Computer vision for medical diagnostics
Background:
- Chest X-rays (CXRs) are vital for diagnosing lung conditions like pneumonia, tuberculosis, and cancer, with 2 billion performed globally each year.
- A significant global workforce shortage exists for interpreting the high volume of CXRs, especially in low-income regions.
- Artificial intelligence (AI), particularly computer vision, offers potential solutions for medical image analysis and diagnostic assistance.
Purpose of the Study:
- To provide a structured review of AI/ML-based analysis of Chest Radiographs (CXRs) for various diagnostic tasks and lung diseases.
- To identify and discuss the key challenges hindering the reliable implementation of AI/ML systems in CXR interpretation.
- To offer an overview of current CXR datasets, evaluation metrics, and issued patents in the field.
Main Methods:
- Systematic review of AI/ML applications in Chest Radiograph analysis.
- Analysis of challenges including data scarcity, privacy concerns, sample quality, adversarial attacks, and model interpretability.
- Compilation of information on existing CXR datasets, performance evaluation metrics, and relevant patents.
Main Results:
- AI/ML demonstrates potential in analyzing CXRs for diverse lung disease diagnoses.
- Significant challenges remain, including data limitations, privacy issues, and the critical need for model interpretability.
- A comprehensive overview of datasets, metrics, and patents provides context for current research.
Conclusions:
- AI/ML holds substantial promise for enhancing CXR analysis and addressing workforce shortages in medical imaging.
- Overcoming challenges related to data, privacy, and interpretability is crucial for the reliable adoption of AI in CXR diagnostics.
- Further research and development are needed to address open problems and fully realize the potential of AI in lung disease screening.
Keywords:
COVID-19Pneumoconiosischest X-rayinterpretable deep learningpneumoniatrusted AItuberculosisMore Related Videos
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