Machine Learning-Enabled Ultrasound for Assessing Single Kidney Function in Pediatrics - Prospective

Project Summary

Kidneys are a very important organ needed to remove waste from the body and balance the body’s fluids. Currently, the best way to measure single kidney function is to do a test called a DTPA renogram. This requires administering ionizing radiation and nuclear material through an IV. Although the radiation is a very small amount, the procedure is still invasive and exposure to radiation should be minimized. Additionally, this material is costly and only accessible in major hospitals, making it hard for rural populations to access. 

The ultrasound (US) is frequently used as a first-line imaging tool for evaluating kidney conditions due to its non-ionizing, non-invasive, and real-time nature with no additional materials needed. However, it has historically been unable to provide information about kidney function. Machine learning paired with US may provide an alternative, non-invasive, accurate, and inexpensive method of measuring single kidney function. The study team has developed a machine-learning (ML)-enabled US that can be performed quickly during routine healthcare visits and give comparable information to DTPA renogram without imposing additional load on patients. This study will gather prospective US and DTPA data from pediatric patients to determine the effectiveness and accuracy of the ML-enabled US.

Project Status

Status: Active, pre-enrollment 
Study Start Date: January 1, 2024
Study End Date: December 31, 2024

Study Enrollment Status: Active
Start Date: January 1, 2024
End Date: December 31, 2024

Project Team

Principal Investigator

Dr. Soojin Kim


Dr. Tom Blydt-Hansen
Dr. Chris Nguan
Dr. Rohit Singla
Dr. Ilker Hacihaliloglu
Dr. Heather Bray

Research Team Members

Isabella Parrotta
Carolyn Beaton
Isabella Watson
Agah Karagoz 

Enrollment Eligibility Criteria

Any pediatric transplant patients at BC Children's Hospital who have had an ultrasound and DTPA renogram scheduled within 3 months of their clinic appointment date.

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