Automatic identification and classification of pediatric glomerulonephritis on ultrasound images based on deep learning and radiomics
Abstract Background Glomerulonephritis (GN) encompasses a heterogeneous group of kidney diseases, often presenting with subclinical manifestations in children, leading to frequent missed diagnoses. Renal biopsy, while considered the gold standard, is invasive, prone to sampling errors, and time-cons...
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2024-11-01
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| Online Access: | https://doi.org/10.1186/s40537-024-01033-1 |
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| author | Jun Kou Zuying Li Yazi You Ruiqi Wang Jingyu Chen Yi Tang |
| author_facet | Jun Kou Zuying Li Yazi You Ruiqi Wang Jingyu Chen Yi Tang |
| author_sort | Jun Kou |
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| description | Abstract Background Glomerulonephritis (GN) encompasses a heterogeneous group of kidney diseases, often presenting with subclinical manifestations in children, leading to frequent missed diagnoses. Renal biopsy, while considered the gold standard, is invasive, prone to sampling errors, and time-consuming, thus hindering rapid diagnosis. This study aimed to develop a noninvasive diagnostic model for childhood GN using renal ultrasound images through the integration of deep learning and radiomics techniques. Methods Ultrasound images were acquired from children undergoing ultrasound-guided biopsy. A total of 469 renal ultrasound images were selected and divided into training and validation sets at a ratio of 8:2 to train a U-Net model for precise kidney image segmentation. Using radiomics, a comprehensive set of radiomic features were extracted from the segmented kidney regions. The extracted features were categorized based on GN types: IgA nephropathy (127 cases), minimal change disease (83 cases), and Henoch–Schönlein purpura nephritis (103 cases). These categories were further randomly split into training and validation sets at a ratio of 8:2. Within the training set, analysis of variance (ANOVA) was used for feature selection, followed by supervised Least Absolute Shrinkage and Selection Operator (LASSO) regression for dimensionality reduction, resulting in the selection of 37 features. These features were then integrated with a random forest algorithm to develop a GN classification model. The model's performance was comprehensively evaluated using the validation set. Results The segmentation model exhibited remarkable performance during training, achieving an accuracy of 95.19% in the validation set. Thirty-seven features were identified through feature selection, leading to the development of a robust classification model. Evaluation on the validation set revealed high accuracy and predictive power across different GN categories, with Area Under the Curve (AUC) values ranging from 0.91 to 0.98. Conclusions The combined use of deep learning and radiomics techniques utilizing renal ultrasound images demonstrates significant potential for classifying childhood GN subtypes. This noninvasive approach holds promise for improving diagnostic efficiency and patient outcomes in GN. |
| format | Article |
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| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
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| series | Journal of Big Data |
| spelling | doaj-art-8206e09d1997400f92d2f70ad43fbc612024-11-17T12:32:28ZengSpringerOpenJournal of Big Data2196-11152024-11-0111111310.1186/s40537-024-01033-1Automatic identification and classification of pediatric glomerulonephritis on ultrasound images based on deep learning and radiomicsJun Kou0Zuying Li1Yazi You2Ruiqi Wang3Jingyu Chen4Yi Tang5Department of Ultrasound, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory DiseasesDepartment of Ultrasound, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory DiseasesDepartment of Ultrasound, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory DiseasesDepartment of Ultrasound, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory DiseasesDepartment of Ultrasound, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory DiseasesDepartment of Ultrasound, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory DiseasesAbstract Background Glomerulonephritis (GN) encompasses a heterogeneous group of kidney diseases, often presenting with subclinical manifestations in children, leading to frequent missed diagnoses. Renal biopsy, while considered the gold standard, is invasive, prone to sampling errors, and time-consuming, thus hindering rapid diagnosis. This study aimed to develop a noninvasive diagnostic model for childhood GN using renal ultrasound images through the integration of deep learning and radiomics techniques. Methods Ultrasound images were acquired from children undergoing ultrasound-guided biopsy. A total of 469 renal ultrasound images were selected and divided into training and validation sets at a ratio of 8:2 to train a U-Net model for precise kidney image segmentation. Using radiomics, a comprehensive set of radiomic features were extracted from the segmented kidney regions. The extracted features were categorized based on GN types: IgA nephropathy (127 cases), minimal change disease (83 cases), and Henoch–Schönlein purpura nephritis (103 cases). These categories were further randomly split into training and validation sets at a ratio of 8:2. Within the training set, analysis of variance (ANOVA) was used for feature selection, followed by supervised Least Absolute Shrinkage and Selection Operator (LASSO) regression for dimensionality reduction, resulting in the selection of 37 features. These features were then integrated with a random forest algorithm to develop a GN classification model. The model's performance was comprehensively evaluated using the validation set. Results The segmentation model exhibited remarkable performance during training, achieving an accuracy of 95.19% in the validation set. Thirty-seven features were identified through feature selection, leading to the development of a robust classification model. Evaluation on the validation set revealed high accuracy and predictive power across different GN categories, with Area Under the Curve (AUC) values ranging from 0.91 to 0.98. Conclusions The combined use of deep learning and radiomics techniques utilizing renal ultrasound images demonstrates significant potential for classifying childhood GN subtypes. This noninvasive approach holds promise for improving diagnostic efficiency and patient outcomes in GN.https://doi.org/10.1186/s40537-024-01033-1GlomerulonephritisPediatricsUltrasoundDeep learningRadiomicsClassification |
| spellingShingle | Jun Kou Zuying Li Yazi You Ruiqi Wang Jingyu Chen Yi Tang Automatic identification and classification of pediatric glomerulonephritis on ultrasound images based on deep learning and radiomics Journal of Big Data Glomerulonephritis Pediatrics Ultrasound Deep learning Radiomics Classification |
| title | Automatic identification and classification of pediatric glomerulonephritis on ultrasound images based on deep learning and radiomics |
| title_full | Automatic identification and classification of pediatric glomerulonephritis on ultrasound images based on deep learning and radiomics |
| title_fullStr | Automatic identification and classification of pediatric glomerulonephritis on ultrasound images based on deep learning and radiomics |
| title_full_unstemmed | Automatic identification and classification of pediatric glomerulonephritis on ultrasound images based on deep learning and radiomics |
| title_short | Automatic identification and classification of pediatric glomerulonephritis on ultrasound images based on deep learning and radiomics |
| title_sort | automatic identification and classification of pediatric glomerulonephritis on ultrasound images based on deep learning and radiomics |
| topic | Glomerulonephritis Pediatrics Ultrasound Deep learning Radiomics Classification |
| url | https://doi.org/10.1186/s40537-024-01033-1 |
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