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LEAVS: An LLM-based Labeler for Abdominal CT Supervision.

Ricardo Bigolin Lanfredi, Yan Zhuang, Mark Finkelstein

|July 30, 2025

Summary

This summary is machine-generated.

We developed a Large Language Model Extractor for Abdominal Vision Supervision (LEAVS) to extract abnormality labels from abdominal CT radiology reports. LEAVS accurately identifies abnormalities and their urgency, outperforming existing methods.

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Area of Science:

Medical Imaging and Artificial Intelligence
Radiology Report Analysis
Natural Language Processing in Healthcare

Background:

Automated extraction of structured labels from radiology reports aids in developing vision models for abnormality detection.
Existing research primarily focuses on chest radiology, with limited work on abdominal reports due to anatomical complexity and diverse pathologies.
There is a need for robust methods to extract detailed information from abdominal CT reports for downstream AI applications.

Purpose of the Study:

To introduce LEAVS (Large language model Extractor for Abdominal Vision Supervision), a novel system for extracting structured labels from abdominal CT radiology reports.
To annotate the certainty of presence and urgency of seven types of abnormalities across nine abdominal organs.
To enable the training of vision models for classifying abdominal organs as normal or abnormal using extracted labels.

Main Methods:

Utilized a specialized chain-of-thought prompting strategy with a locally-run Large Language Model (LLM).
Employed sentence extraction and multiple-choice questions within a tree-based decision system for label extraction.
Focused on abnormalities encompassing most finding types in CT reports for broad coverage.

Main Results:

Achieved an average F1 score of 0.89 for extracting various abnormality types across abdominal organs, significantly outperforming competing labelers and human performance.
Demonstrated comparable performance to human annotations for extracting urgency labels.
Showcased the utility of extracted abnormality labels in training a single vision model for organ classification (normal/abnormal).

Conclusions:

LEAVS effectively extracts detailed abnormality and urgency information from abdominal CT radiology reports.
The developed system significantly advances automated analysis of abdominal radiology data.
The released code and annotations facilitate further research in AI-driven abdominal imaging analysis.