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Predicting the Future of Patients with Obstructive Uropathy—A Comprehensive Review

  • Renal (D Noone, Section Editor)
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Abstract

Purpose of Review

This educational review aims to describe available tools that can be utilized for outcome prediction for children with obstructive uropathy.

Recent Findings

New approaches focus on personalized assessment or precision medicine enhanced with machine-learning tools for diagnostics and outcome prediction. In addition, tailored early interventions and the introduction of minimally invasive techniques are likely to impact the future management of obstructive uropathies significantly.

Summary

Obstructive uropathies represent a series of conditions that affect the urinary tract and can be associated with substantial morbidity. Timely diagnosis and management remain challenging. Developing novel methods that enable targeted optimization and personalized care is a matter of intense research. Machine learning tools, prenatal interventions, and minimally invasive operating techniques hold great promise in improving outcomes.

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Authors and Affiliations

  1. Division of Urology, The Hospital for Sick Children and Department of Surgery, University of Toronto, 555 University Ave, Toronto, ON, M5G1X8, Canada

    Juliane Richter, Mandy Rickard, Armando J. Lorenzo & Michael Chua

  2. Department of Surgery, University of Toronto, Toronto, ON, Canada

    Jin Kyu Kim

  3. Center for Computational Medicine, Hospital for Sick Children, Toronto, ON, Canada

    Lauren Erdman

  4. Institute of Urology, St. Luke’s Medical Center, Quezon City, Philippines

    Michael Chua

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40124_2022_272_Fig2_ESM.png

Supplementary file1 Sample image of ML-tool for prediction of recurrent UPJO and re-intervention after pyeloplasty [40]. (PNG 1328 KB)

40124_2022_272_Fig3_ESM.png

Supplementary file2 Grad-CAM heatmap outlining of CNN-model for differentiation between resolving and obstructive HN [41•]. (PNG 1532 KB)

40124_2022_272_Fig4_ESM.png

Supplementary file3 Sample image of the Posterior Urethral Valves Outcomes Prediction (PUVOP) tool [73]. (PNG 1335 KB)

Supplementary file4 (DOCX 14 KB)

Glossary

Glossary

• Convolutional Neural Networks

A machine learning model which consists of “layers” where inputs are run through with important features being extracted from each layer and sent to the next. This process repeats for each layer, resulting in a summary of important features at the end and a prediction output. In a Siamese CNN, 2 inputs are put through layers simultaneously, and the results merged into a single prediction at the end.

More detailed explanation of Convolutional Siamese Networks with examples:

  • Liu Y, Sui X, Liu C, Kuang X, Hu Y. Automatic Lumbar Spine Tracking Based on Siamese Convolutional Network. J Digit Imaging. 2020 Apr;33(2):423–430. https://doi.org/10.1007/s10278-019-00273-5. PMID: 31,602,548; PMCID: PMC7165228.

  • Chang GH, Felson DT, Qiu S, Guermazi A, Capellini TD, Kolachalama VB. Assessment of knee pain from MR imaging using a convolutional Siamese network. Eur Radiol. 2020 Jun;30(6):3538–3548. https://doi.org/10.1007/s00330-020-06658-3. Epub 2020 Feb 13. Erratum in: Eur Radiol. 2020 Dec;30(12):6968. PMID: 32,055,951; PMCID: PMC7786238.

• Gradient-weighted Class Activation Maps (Grad-CAMs)

Gradient-based heatmaps localizing and visualizing different regions of interest present in images, utilized in convolutional networks. These are used to improve explainability and minimize the black box phenomenon.

Overview of functioning of Grad-CAMs:

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Richter, J., Rickard, M., Kim, J.K. et al. Predicting the Future of Patients with Obstructive Uropathy—A Comprehensive Review. Curr Pediatr Rep 10, 202–213 (2022). https://doi.org/10.1007/s40124-022-00272-1

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