# Center for Machine Learning in Urology-Scientific Project

> **NIH NIH P20** · CHILDREN'S HOSP OF PHILADELPHIA · 2020 · $223,322

## Abstract

PROJECT SUMMARY
Kidney stones are characterized by the episodic occurrence of debilitating stone events, which lead to painful
passage, emergent visits, and surgery. Proper selection of medical and surgical treatments depends on accurate
assessment of stone characteristics, including size and location. Current methods for quantifying these
characteristics depend on manual measurement by humans, which introduces unnecessary variation, is
laborious, and makes analyzing the large number of imaging studies performed for clinical trials very difficult.
Existing automated measurements are proprietary, only segment (partition) the stone from the surrounding
structures without considering other clinically important features such as hydronephrosis, and are slow. A critical
barrier to effectively implementing individualized therapies that decrease the burden of nephrolithiasis is the lack
of automated analyses of diagnostic imaging that could accurately measure stone and kidney characteristics,
and predict, in real time, an individual’s risk of stone events, such as spontaneous stone passage.
In this Research Project, the Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania
(Penn) Center for Machine Learning in Urology (CMLU) forges a collaboration among experts in machine
learning of diagnostic imaging, clinical epidemiology, and benign urologic disease. We build upon our recent
discoveries that machine learning (particularly deep learning) of diagnostic images accurately, reliably, and
rapidly predicts disease risk strata and outcomes. This project uses machine learning of CT to automate
measurement of conventional characteristics of stones (e.g. size, location, and shape) and renal anatomy (e.g.
hydronephrosis, ureteral dilation). We then apply this method to predict spontaneous passage of ureteral stones
for individuals across the lifespan. In doing so, the proposed studies will develop clinically useful open-access
prediction tools that will transform the standard of quantifying urinary stones and, in a fully automated way,
accurately, reliably, and rapidly identify patients with ureteral stones most likely to benefit from early surgical
intervention. In Aim 1, we will use deep learning to automatically segment and measure conventional features
of urinary stones (e.g. size, density) and adjacent renal and ureteral anatomy (e.g. degree of hydronephrosis) in
CT images of 2,000 children and adults evaluated at CHOP and Penn, respectively. In Aim 2, we will use deep
learning to extract informative features from CT images that predict ureteral stone passage for 723 unique
children and adults. The features include conventional features, engineered features, and deep-learning features
that may neither be appreciated by nor be able to be measured by humans. These results would transform
clinical care and research and provide insights into those who would be most likely to benefit from early elective
surgery to remove stones to prevent...

## Key facts

- **NIH application ID:** 10133364
- **Project number:** 1P20DK127488-01
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Yong Fan
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $223,322
- **Award type:** 1
- **Project period:** 2020-09-15 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10133364

## Citation

> US National Institutes of Health, RePORTER application 10133364, Center for Machine Learning in Urology-Scientific Project (1P20DK127488-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10133364. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*