About
Biography
I am a computational researcher aiming to develop technologies to translate and transform pediatric clinical care using machine learning. My foundational training is in economics and statistics. I strive to improve the efficiency and impact of health interventions by getting more out of the information already collected through standards of care.
My work primarily focuses on extracting more information from biomedical imaging data, with the goal of enabling faster and easier interpretation by a larger share of clinicians, such as novices and those who don’t specialize in pediatrics.
I have already developed models for prediction in hydronephrosis ultrasound, pediatric echocardiography, hepatocellular carcinoma recurrence, irritable bowel disease (IBD) diagnosis and drug response, and the diagnosis of motile ciliopathy. I have been a researcher for over 10 years and began working at Cincinnati Children’s in 2023.
Research Areas
Publications
AI-PEDURO - Artificial intelligence in pediatric urology: Protocol for a living scoping review and online repository. Journal of Pediatric Urology. 2025; 21:532-538.
CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare. Nature Medicine. 2025; 31:9-11.
Use of prenatal ultrasound findings to predict postnatal outcome in fetuses with lower urinary tract obstruction. Ultrasound in Obstetrics and Gynecology. 2024; 64:768-775.
The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone. Scientific Reports. 2024; 14:22748.
Trade-Offs in Deep Learning Model Loss and Configuration for Sparse Histological Segmentation: A Case Study in Pediatric Ileal Histology. (2024) Institute of Electrical and Electronics Engineers (IEEE). 00:1-8.
Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances. Diagnostics. 2024; 14:2059.
Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Assessment of the Landscape and Current Utilization. Current treatment options in pediatrics. 2024; 10:88-100.
Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data. BMJ Paediatrics Open. 2024; 8:e002365.
Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning. Pediatric Research. 2024; 95:1818-1825.
Application of STREAM-URO and APPRAISE-AI reporting standards for artificial intelligence studies in pediatric urology: A case example with pediatric hydronephrosis. Journal of Pediatric Urology. 2024; 20:455-467.
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