ArticleUrology2020
Multi-instance Deep Learning of Ultrasound Imaging Data for Pattern Classification of Congenital Abnormalities of the Kidney and Urinary Tract in Children.
Article in Urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 37 citations in OpenAlex.
- Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.Pediatric nephrology (Berlin, Germany) · 2026Review
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- AI-powered insights in pediatric nephrology: current applications and future opportunities.Pediatric nephrology (Berlin, Germany) · 2026Review
- Prognostic evaluation of boys with posterior urethral valves using a recursive feature elimination machine learning framework.Frontiers in pediatrics · 2026Article
- Deep learning framework for automated frame selection in kidney ultrasound.Scientific reports · 2025Article
- Longitudinal image-based prediction of surgical intervention in infants with hydronephrosis using deep learning: Is a single ultrasound enough?PLOS digital health · 2025Article
- Deep learning in renal ultrasound: applications, challenges, and future outlook.Frontiers in oncology · 2025Review
- Enhancing Whole Slide Image Classification with Discriminative and Contrastive Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.Scientific reports · 2024Article
- Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment.Nature reviews. Urology · 2024Review
- [Automatic classification of immune-mediated glomerular diseases based on multi-modal multi-instance learning].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Artificial intelligence-aided ultrasound in renal diseases: a systematic review.Quantitative imaging in medicine and surgery · 2023Review
- Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach.JMIR medical informatics · 2022Article
- Current research and future directions in non-malignant urologic research - proceedings of the annual CAIRIBU meeting.American journal of clinical and experimental urology · 2022Article
- Ultrasound-Based Renal Parenchymal Area and Kidney Function Decline in Infants With Congenital Anomalies of the Kidney and Urinary Tract.Seminars in nephrology · 2021Review
- Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study.Frontiers in oncology · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
objectiveTo reliably and quickly diagnose children with posterior urethral valves (PUV), we developed a multi-instance deep learning method to automate image analysis.
methodsWe built a robust pattern classifier to distinguish 86 children with PUV from 71 children with mild unilateral hydronephrosis based on ultrasound images (3504 in sagittal view and 2558 in transverse view) obtained during routine clinical care.
resultsThe multi-instance deep learning classifier performed better than classifiers built on either single sagittal images or single transverse images. Particularly, the deep learning classifiers built on single images in the sagittal view and single images in the transverse view obtained area under the receiver operating characteristic curve (AUC) values of 0.796 ± 0.064 and 0.815 ± 0.071, respectively. AUC values of the multi-instance deep learning classifiers built on images in the sagittal and transverse views with mean pooling operation were 0.949 ± 0.035 and 0.954 ± 0.033, respectively. The multi-instance deep learning classifiers built on images in both the sagittal and transverse views with a mean pooling operation obtained an AUC of 0.961 ± 0.026 with a classification rate of 0.925 ± 0.060, specificity of 0.986 ± 0.032, and sensitivity of 0.873 ± 0.120, respectively. Discriminative regions of the kidney located using classification activation mapping demonstrated that the deep learning techniques could identify meaningful anatomical features from ultrasound images.
conclusionThe multi-instance deep learning method provides an automatic and accurate means to extract informative features from ultrasound images and discriminate infants with PUV from male children with unilateral hydronephrosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.