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Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features

  • Q. Zheng
    Affiliations
    Department of Radiology, School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

    School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
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  • S.L. Furth
    Affiliations
    Department of Pediatrics, Division of Pediatric Nephrology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
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  • G.E. Tasian
    Affiliations
    Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA

    Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, PA, USA

    Department of Biostatistics, Epidemiology, and Informatics, The University of Pennsylvania, Philadelphia, PA, USA
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  • Y. Fan
    Correspondence
    Corresponding author. Richards Building, 7th floor, RM D703, 3700 Hamilton Walk. Department of Radiology, Perelman School of Medicine, University of Pennsylvania. Philadelphia, PA 19104, USA. Tel.: +1 215 746 4065.
    Affiliations
    Department of Radiology, School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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Published:October 31, 2018DOI:https://doi.org/10.1016/j.jpurol.2018.10.020

      Summary

      Introduction

      Anatomic characteristics of kidneys derived from ultrasound images are potential biomarkers of children with congenital abnormalities of the kidney and urinary tract (CAKUT), but current methods are limited by the lack of automated processes that accurately classify diseased and normal kidneys.

      Objective

      The objective of the study was to evaluate the diagnostic performance of deep transfer learning techniques to classify kidneys of normal children and those with CAKUT.

      Study design

      A transfer learning method was developed to extract features of kidneys from ultrasound images obtained during routine clinical care of 50 children with CAKUT and 50 controls. To classify diseased and normal kidneys, support vector machine classifiers were built on the extracted features using (1) transfer learning imaging features from a pretrained deep learning model, (2) conventional imaging features, and (3) their combination. These classifiers were compared, and their diagnosis performance was measured using area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity.

      Results

      The AUC for classifiers built on the combination features were 0.92, 0.88, and 0.92 for discriminating the left, right, and bilateral abnormal kidney scans from controls with classification rates of 84%, 81%, and 87%; specificity of 84%, 74%, and 88%; and sensitivity of 85%, 88%, and 86%, respectively. These classifiers performed better than classifiers built on either the transfer learning features or the conventional features alone (p < 0.001).

      Discussion

      The present study validated transfer learning techniques for imaging feature extraction of ultrasound images to build classifiers for distinguishing children with CAKUT from controls. The experiments have demonstrated that the classifiers built on the transfer learning features and conventional image features could distinguish abnormal kidney images from controls with AUCs greater than 0.88, indicating that classification of ultrasound kidney scans has a great potential to aid kidney disease diagnosis. A limitation of the present study is the moderate number of patients that contributed data to the transfer learning approach.

      Conclusions

      The combination of transfer learning and conventional imaging features yielded the best classification performance for distinguishing children with CAKUT from controls based on ultrasound images of kidneys.

      Keywords

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