A Standard Framework for Converting Coronary Angiography Reports into Machine-Readable Format Using Large Language Models

Background and ObjectivesCoronary angiography (CAG) reports contain many details about coronary anatomy, lesion characteristics, and interventional procedures. However, their free-text format limits their research utility. Therefore, we sought to develop and validate a framework leveraging large language models (LLMs) to convert CAG reports automatically into a standardized structured format. MethodsUsing 50 CAG reports from a tertiary hospital, we developed a multi-step framework to standardize and extract key information from CAG reports. First, a standard annotation schema was developed by cardiologists. Thereafter, an LLM (GPT-4o) converted the free-text CAG reports into the hierarchical annotation schema in a standardized format. Finally, clinically relevant information was extracted from the standardized schema. One hundred CAG reports from each of two hospitals were used for internal and external test, respectively. The 12 key information points included four CAG-related (previous stent information, lesion characteristics, and anatomical diagnosis) and eight percutaneous coronary intervention (PCI)-related key points (complex PCI criteria and current stent information). For internal test, two interventional cardiologists independently extracted information, with discrepancies resolved through consensus, as reference standard. ResultsBased on the reference standard, the proposed framework demonstrated superior accuracy for CAG-related (99.5% vs. 91.8%; p < 0.001) and comparable accuracy for PCI-related key points (98.3% vs. 97.4%; p = 0.512) in the internal test. External test confirmed high accuracy for both CAG-(96.2%) and PCI-related key points (99.4%). ConclusionsThis framework demonstrated excellent accuracy in standardizing free-text CAG reports, potentially enabling more efficient utilization of detailed clinical data for cardiovascular research. Authors SummaryThe novel framework that standardizes CAG report is a practical solution to a significant challenge in cardiovascular research -- the efficient utilization of detailed procedural and anatomical information untapped in free-text CAG reports. Our framework could enable systematic analysis of large-scale coronary intervention outcomes, reduce the burden of cardologists clinical trial recruitment, and support evidence-based clinical decision-making.