Clinical Research Directory

Integrating an AI-Driven Hydronephrosis Decision-Making Tool

Hydronephrosis is a common congenital kidney anomaly. While most cases resolve on their own, some require surgery. Clinicians rely on repeated ultrasounds and sometimes invasive tests to decide if surgery is needed, but predicting outcomes is difficult. Researchers at SickKids developed an AI model that analyzes ultrasound images to assist in diagnosing and managing hydronephrosis. This study tests how well the AI integrates into real-world care. Clinicians will first make care decisions without AI and then review the AI's prediction before deciding whether to change their plan. A separate expert, unaware of whether AI influenced the first clinician's plan, will make the final decision to ensure care remains unchanged. The study will assess whether AI improves decision-making, reduces unnecessary tests, and fits into clinical workflows. If successful, the AI model could serve as a complementary tool to make diagnoses more efficient and precise while minimizing invasive procedures.

Ultrasound Localization Microscopy in Patient With Congenital Anomalies of the Kidney and Urinary Tract (CAKUT)

This clinical study aims to non-invasively visualize perfusion and microvascularization, as well as individual glomeruli, using Ultrasound Localization Microscopy (ULM) and CEUS in patients with congenital anomalies of the kidney and urinary tract (CAKUT).