AXpert: human expert facilitated privacy-preserving large language models for abdominal X-ray report labeling.

ABSTRACT:

Importance

The lack of a publicly accessible abdominal X-ray (AXR) dataset has hindered necrotizing enterocolitis (NEC) research. While significant strides have been made in applying natural language processing (NLP) to radiology reports, most efforts have focused on chest radiology. Development of an accurate NLP model to identify features of NEC on abdominal radiograph can support efforts to improve diagnostic accuracy for this and other rare pediatric conditions.

Objectives

This study aims to develop privacy-preserving large language models (LLMs) and their distilled version to efficiently annotate pediatric AXR reports.

Materials and methods

Utilizing pediatric AXR reports collected from C.S. Mott Children's Hospital, we introduced AXpert in 2 formats: one based o

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11809431 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature