A natural language processing system helped identify lumbar spine imaging findings, providing significant gains in model sensitivity, according to a study in Academic Radiology.
Researchers evaluated an NLP system built with open-source tools for identifying lumbar spine imaging findings related to low back pain on MRI and X-ray radiology reports from four health systems. The study authors selected 871 reports to form a reference-standard dataset, and four spine experts annotated the presences of 26 findings.
The researchers calculated inter-rater agreement and finding prevalence from the annotated data, which was split into development (80 percent) and testing (20 percent) sets. The study authors developed an NLP system from both rule-based and machine-learned models, and the system with validated using accuracy metrics such as sensitivity, specificity and area under the receiver operating characteristic curve.
The researchers concluded the NLP system performed well in identifying the 26 lumbar spine findings. Machine-learned models provided substantial gains in model sensitivity with only a slight loss of specificity and overall higher AUC.