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Machine Learning (1971) New Methods (1100) Decision Support for Health Professionals (1468) Imaging Informatics (283) Registered Report (748)

Published on in Vol 10, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40878, first published .
Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach

Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach

Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach

Authors of this article:

Ming-Chin Tsai1 Author Orcid Image ;   Henry Horng-Shing Lu2 Author Orcid Image ;   Yueh-Chuan Chang3 Author Orcid Image ;   Yung-Chieh Huang1, 4 Author Orcid Image ;   Lin-Shien Fu1, 4, 5 Author Orcid Image
Article Authors Cited by (10) Tweetations (2) Metrics

Journals

  1. Bhandari M, Yogarajah P, Kavitha M, Condell J. Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP. Applied Sciences 2023;13(5):3125 View
  2. Sloan M, Li H, Lescay H, Judge C, Lan L, Hajiyev P, Giger M, Gundeti M. Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound. Investigative and Clinical Urology 2023;64(6):588 View
  3. Sriraam N, Chinta B, Suresh S, Sudharshan S. Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review. Progress in Biomedical Engineering 2024;6(2):023002 View
  4. Wang D, Lin S, Lyu G. Advances in the Application of Artificial Intelligence in the Ultrasound Diagnosis of Vulnerable Carotid Atherosclerotic Plaque. Ultrasound in Medicine & Biology 2025;51(4):607 View
  5. saber A, Hassan E, Elbedwehy S, Awad W, Emara T. Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification. Scientific Reports 2025;15(1) View
  6. Chung K, Wu S, Jeanne C, Tsai A. Comparison of neural networks for classification of urinary tract dilation from renal ultrasounds: evaluation of agreement with expert categorization. Pediatric Radiology 2025;55(9):1846 View
  7. Sudharson S, Kokil P. Automated diagnosis tool for clinical ultrasound image analysis in kidney disease identification. Biomedical Signal Processing and Control 2026;111:108345 View
  8. Nada A, Ahmed Y, Hu J, Weidemann D, Gorman G, Lecea E, Sandokji I, Cha S, Shin S, Bani-Hani S, Mannemuddhu S, Ruebner R, Kakajiwala A, Raina R, George R, Elchaki R, Moritz M. AI-powered insights in pediatric nephrology: current applications and future opportunities. Pediatric Nephrology 2025 View

Books/Policy Documents

  1. Kim D, Gam K, Gundeti M. Artificial Intelligence in Urology. View

Conference Proceedings

  1. Sharma G, Anand V, Chauhan R, Pokhariya H, Gupta S, Sunil G. 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). Revolutionizing Kidney Disease Diagnosis: A Comprehensive CNN-Based Framework for Multi-Class CT Classification View