Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis.

Abstract

Introduction: Natural language processing (NLP) has been used to analyze unstructured imaging report data, yet its application in identifying chronic kidney disease (CKD) features from kidney ultrasound reports remains unexplored.

Methods: In a single-center pilot study, we analyzed 1,068 kidney ultrasound reports using NLP techniques. To identify kidney echogenicity as either "normal" or "increased," we used two methods: one that looks at individual words and another that analyzes full sentences. Kidney length was identified as "small" if its length was below the 10th percentile. Nephrologists reviewed 100 randomly selected reports to create the reference standard (ground truth) for initial model training followed by model validation on an independent set of 100 reports.

Results: The word-level NLP model outperformed the sentence-level approach in classifying increased echogenicity (accuracy: 0.96 vs. 0.89 for the left kidney; 0.97 vs. 0.92 for the right kidney). This model was then applied to the full dataset to assess associations with CKD. Multivariable logistic regression identified bilaterally increased echogenicity as the strongest predictor of CKD (odds ratio [OR] = 7.642, 95% confidence interval [CI]: 4.887-11.949; p < 0.0001), followed by bilaterally small kidneys (OR = 4.981 [1.522, 16.300]; p = 0.008). Among individuals without CKD, those with bilaterally increased echogenicity had significantly lower kidney function than those with normal echogenicity.

Conclusions: State-of-the-art NLP models can accurately extract CKD-related features from ultrasound reports, with the potential of providing a scalable tool for early detection and risk stratification. Future research should focus on validating these models across different healthcare systems.