mlsanityData to 7 Oct 2026

A clinical validation of VinDr-CXR, an AI system for detecting abnormal chest radiographs

arXiv 2104.02256 ↗First posted 6 Apr 2021eess.IV cs.CVcited by 4

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Abstract (arXiv)

Computer-Aided Diagnosis (CAD) systems for chest radiographs using artificial intelligence (AI) have recently shown a great potential as a second opinion for radiologists. The performances of such systems, however, were mostly evaluated on a fixed dataset in a retrospective manner and, thus, far from the real performances in clinical practice. In this work, we demonstrate a mechanism for validating an AI-based system for detecting abnormalities on X-ray scans, VinDr-CXR, at the Phu Tho General Hospital - a provincial hospital in the North of Vietnam. The AI system was directly integrated into the Picture Archiving and Communication System (PACS) of the hospital after being trained on a fixed annotated dataset from other sources. The performance of the system was prospectively measured by matching and comparing the AI results with the radiology reports of 6,285 chest X-ray examinations extracted from the Hospital Information System (HIS) over the last two months of 2020. The normal/abnormal status of a radiology report was determined by a set of rules and served as the ground truth. Our system achieves an F1 score - the harmonic average of the recall and the precision - of 0.653 (95% CI 0.635, 0.671) for detecting any abnormalities on chest X-rays. Despite a significant drop from the in-lab performance, this result establishes a high level of confidence in applying such a system in real-life situations.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 6 Apr 2021.

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12 of 25 references.

References · 12

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  8. 2020VinDr-CXR: An open dataset of chest X-rays with radiologist's annotationscited by 236
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  10. 2020Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labelscited by 56
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  12. 2018Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approachcited by 12

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Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.

  1. 2022Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamesecited by 5
  2. 2022Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slicescited by 1
  3. 2023Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigationcited by 1
  4. 2023Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretationcited by 1