Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy
Abstract Despite efforts to diagnose diabetic nephropathy (DN) using biochemical data or ultrasound imaging separately, a significant gap exists regarding the development of integrated models combining both modalities for enhanced early DN diagnosis. Therefore, we aimed to assess the ability of mach...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-47449-2 |
| _version_ | 1797452982531391488 |
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| author | Xuee Su Shu Lin Yinqiong Huang |
| author_facet | Xuee Su Shu Lin Yinqiong Huang |
| author_sort | Xuee Su |
| collection | DOAJ |
| description | Abstract Despite efforts to diagnose diabetic nephropathy (DN) using biochemical data or ultrasound imaging separately, a significant gap exists regarding the development of integrated models combining both modalities for enhanced early DN diagnosis. Therefore, we aimed to assess the ability of machine learning models containing two-dimensional ultrasound imaging and biochemical data to diagnose early DN in patients with type 2 diabetes mellitus (T2DM). This retrospective study included 219 patients, divided into a training or test group at an 8:2 ratio. Features were selected using minimum redundancy maximum relevance and random forest-recursive feature elimination. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) for sensitivity, specificity, Matthews Correlation Coefficient, F1 score, and accuracy. K-nearest neighbor, support vector machine, and logistic regression models could diagnose early DN, with AUC values of 0.94, 0.85, and 0.85 in the training cohort and 0.91, 0.84, and 0.84 in the test cohort, respectively. Early DN diagnosing using two-dimensional ultrasound-based radiomics models can potentially revolutionize T2DM patient care by enabling proactive interventions, ultimately improving patient outcomes. Our integrated approach showcases the power of artificial intelligence in medical imaging, enhancing early disease detection strategies with far-reaching applications across medical disciplines. |
| first_indexed | 2024-03-09T15:16:17Z |
| format | Article |
| id | doaj.art-1c4f9635db8a4dbbbcd17ee1ff8d801c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:16:17Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1c4f9635db8a4dbbbcd17ee1ff8d801c2023-11-26T13:02:05ZengNature PortfolioScientific Reports2045-23222023-11-0113111010.1038/s41598-023-47449-2Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathyXuee Su0Shu Lin1Yinqiong Huang2Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical UniversityCentre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical UniversityCentre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical UniversityAbstract Despite efforts to diagnose diabetic nephropathy (DN) using biochemical data or ultrasound imaging separately, a significant gap exists regarding the development of integrated models combining both modalities for enhanced early DN diagnosis. Therefore, we aimed to assess the ability of machine learning models containing two-dimensional ultrasound imaging and biochemical data to diagnose early DN in patients with type 2 diabetes mellitus (T2DM). This retrospective study included 219 patients, divided into a training or test group at an 8:2 ratio. Features were selected using minimum redundancy maximum relevance and random forest-recursive feature elimination. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) for sensitivity, specificity, Matthews Correlation Coefficient, F1 score, and accuracy. K-nearest neighbor, support vector machine, and logistic regression models could diagnose early DN, with AUC values of 0.94, 0.85, and 0.85 in the training cohort and 0.91, 0.84, and 0.84 in the test cohort, respectively. Early DN diagnosing using two-dimensional ultrasound-based radiomics models can potentially revolutionize T2DM patient care by enabling proactive interventions, ultimately improving patient outcomes. Our integrated approach showcases the power of artificial intelligence in medical imaging, enhancing early disease detection strategies with far-reaching applications across medical disciplines.https://doi.org/10.1038/s41598-023-47449-2 |
| spellingShingle | Xuee Su Shu Lin Yinqiong Huang Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy Scientific Reports |
| title | Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy |
| title_full | Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy |
| title_fullStr | Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy |
| title_full_unstemmed | Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy |
| title_short | Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy |
| title_sort | value of radiomics based two dimensional ultrasound for diagnosing early diabetic nephropathy |
| url | https://doi.org/10.1038/s41598-023-47449-2 |
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