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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
Published on: October 27, 2023
AI-assisted detection for chest X-rays (AID-CXR): a multi-reader multi-case study protocol
Farhaan Khan1, Indrajeet Das2, Marusa Kotnik3
1Oxford University Hospitals NHS Foundation Trust, Oxford, UK farhaan.a.khan@outlook.com.
This study evaluates an AI tool to aid healthcare professionals in interpreting chest X-rays (CXRs), aiming to improve diagnostic accuracy and speed for various abnormalities.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Healthcare
- Radiology
Background:
- Chest X-rays (CXRs) are globally prevalent diagnostic tools.
- Artificial intelligence (AI) offers potential for enhanced CXR interpretation.
- Evidence is limited on AI's impact for non-radiologist healthcare professionals using CXRs.
Purpose of the Study:
- To assess the effectiveness of an AI-based CXR interpretation tool.
- To evaluate improvements in diagnostic accuracy, speed, and confidence among diverse healthcare professionals.
- To determine the utility of AI in supporting daily CXR review practices.
Main Methods:
- Utilizing 500 retrospective inpatient and emergency department CXRs.
- Establishing a reference standard through expert radiologist reviews.
- Comparing the Lunit INSIGHT CXR tool's performance against the reference standard and reader performance with/without AI assistance.
Main Results:
- Calculating Area Under the Receiver Operating Characteristic Curve (AUROC) for 10 key abnormalities.
- Measuring improvements in sensitivity, specificity, AUROC, confidence, and interpretation speed.
- The study is ongoing, with results pending.
Conclusions:
- AI tools show promise in augmenting diagnostic capabilities for chest X-rays.
- Further research is needed to validate AI's utility across different clinical settings and professional groups.
- The findings will inform the integration of AI into routine healthcare practices for improved patient care.
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