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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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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.

|January 14, 2025

Summary

This summary is machine-generated.

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.

Keywords:

Chest imaging Diagnostic Imaging RESPIRATORY MEDICINE (see Thoracic Medicine)

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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.