Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis
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 ultras...
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| Format: | Article |
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Taylor & Francis Group
2025-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2025.2539938 |
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| author | Chenlu Wang Ritwik Banerjee Harry Kuperstein Hamza Malick Ruqiyya Bano Robin L. Cunningham Hira Tahir Priyal Sakhuja Janos Hajagos Farrukh M. Koraishy |
| author_facet | Chenlu Wang Ritwik Banerjee Harry Kuperstein Hamza Malick Ruqiyya Bano Robin L. Cunningham Hira Tahir Priyal Sakhuja Janos Hajagos Farrukh M. Koraishy |
| author_sort | Chenlu Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-150f33c19aba42919ca79d2de761611e |
| institution | DOAJ |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-150f33c19aba42919ca79d2de761611e2025-08-20T03:22:19ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492025-12-0147110.1080/0886022X.2025.2539938Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosisChenlu Wang0Ritwik Banerjee1Harry Kuperstein2Hamza Malick3Ruqiyya Bano4Robin L. Cunningham5Hira Tahir6Priyal Sakhuja7Janos Hajagos8Farrukh M. Koraishy9Department of Computer Science, Stony Brook University, NY, USADepartment of Computer Science, Stony Brook University, NY, USARenaissance School of Medicine, Stony Brook University, NY, USARenaissance School of Medicine, Stony Brook University, NY, USADepartment of Medicine, Stony Brook University, NY, USADepartment of Radiology, Stony Brook University, NY, USADepartment of Medicine, Stony Brook University, NY, USADepartment of Medicine, Stony Brook University, NY, USADepartment of Biomedical Informatics, Stony Brook University, NY, USADepartment of Medicine, Stony Brook University, NY, USAIntroduction 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.https://www.tandfonline.com/doi/10.1080/0886022X.2025.2539938Natural language processingdeep syntactic structureslanguage embeddingskidney ultrasound reportschronic kidney disease |
| spellingShingle | Chenlu Wang Ritwik Banerjee Harry Kuperstein Hamza Malick Ruqiyya Bano Robin L. Cunningham Hira Tahir Priyal Sakhuja Janos Hajagos Farrukh M. Koraishy Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis Renal Failure Natural language processing deep syntactic structures language embeddings kidney ultrasound reports chronic kidney disease |
| title | Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis |
| title_full | Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis |
| title_fullStr | Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis |
| title_full_unstemmed | Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis |
| title_short | Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis |
| title_sort | natural language processing for kidney ultrasound analysis correlating imaging reports with chronic kidney disease diagnosis |
| topic | Natural language processing deep syntactic structures language embeddings kidney ultrasound reports chronic kidney disease |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2025.2539938 |
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