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Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a

Ricardo Bigolin Lanfredi, Pritam Mukherjee, Ronald Summers

    Arxiv
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    Summary

    MAPLEZ, a novel Large Language Model (LLM), enhances chest X-ray (CXR) report labeling by extracting detailed findings beyond simple presence. This improves dataset quality and downstream AI model performance.

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    Area of Science:

    • Medical Imaging Analysis
    • Artificial Intelligence in Healthcare
    • Natural Language Processing

    Background:

    • Current chest X-ray (CXR) report labeling relies on rule-based systems or supervised deep learning, often yielding limited, presence-only labels.
    • Existing methods lack adaptability and struggle with nuanced information like location, severity, and uncertainty.
    • The quality of labels directly impacts the utility of datasets for developing and evaluating AI models.

    Purpose of the Study:

    • To introduce MAPLEZ (Medical report Annotations with Privacy-preserving Large language model using Expeditious Zero shot answers), a novel approach for extracting and enhancing CXR report findings labels.
    • To demonstrate MAPLEZ's capability to extract comprehensive annotations, including presence, location, severity, and uncertainty.
    • To evaluate the impact of MAPLEZ-generated annotations on the performance of downstream classification models.

    Main Methods:

    • Leveraging a locally executable Large Language Model (LLM) for zero-shot extraction of medical report annotations.
    • Developing MAPLEZ to extract multi-type findings labels (presence, location, severity, uncertainty) from CXR reports.
    • Comparing MAPLEZ's annotation performance against existing labelers across multiple datasets and abnormalities.

    Main Results:

    • MAPLEZ achieved a 3.6 percentage point (pp) increase in macro F1 score for categorical presence annotations and over 20 pp increase for location annotations compared to competing methods.
    • Models trained with MAPLEZ annotations showed a 1.1 pp increase in AUROC, outperforming those trained with the best alternative annotations.
    • Significant improvements in both label quality and downstream model performance were observed.

    Conclusions:

    • MAPLEZ offers a significant advancement in automated chest X-ray report labeling, providing richer and more accurate annotations.
    • The enhanced labels generated by MAPLEZ lead to substantial improvements in the performance of AI models for medical image analysis.
    • This approach demonstrates the potential of LLMs for improving the quality and utility of medical datasets.