# Accurate and Reliable Diagnostics for Injured Children: Machine Learning for Ultrasound

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $162,432

## Abstract

PROJECT SUMMARY/ABSTRACT
Dr. Aaron Kornblith, a general and pediatric emergency physician at the University of California, San Francisco
(UCSF) is establishing himself as a future investigator in patient-oriented clinical research of novel diagnostics
in injured children. This award will enable him to accomplish the following goals: (1) become an expert at patient-
oriented clinical research in pediatric abdominal trauma; (2) develop novel machine learning models for a
bedside ultrasound application; (3) implement advanced computational methods to develop, validate, and test
clinical decision rules incorporating bedside ultrasound; and (4) develop an independent clinical research career.
To achieve these goals, Dr. Kornblith has assembled an expert mentoring team: primary mentor Dr. Jeffrey
Fineman, Chief of Pediatric Critical Care at UCSF (conducts clinical investigations in children with critical illness
and is an expert in career development of early-stage investigators), co-mentors Dr. Atul Butte, (an expert in
healthcare and data science), Drs. James Holmes and Nathan Kuppermann (experts in the diagnostic evaluation
of pediatric trauma and clinical decision rules), scientific advisor Dr. John Mongan, (expert in developing,
validating, and implementing machine learning for imaging tasks), and statistical advisor Dr. Bin Yu (an expert
in statistical theory including accurate, reliable, and interpretable computational methods, and implicit bias).
Hemorrhage from blunt intraabdominal injury is a leading cause of death in children. Identifying abdominal
hemorrhage early is essential to minimizing morbidity and mortality from delayed or missed diagnoses. The
reference standard test, abdominal computed tomography (CT), has drawbacks including risk of radiation-
induced malignancy. For 25 years, CT use in children has increased dramatically without proportional
improvements in outcomes. Focused Assessment with Sonography for Trauma (FAST) is a bedside ultrasound
method to evaluate children for abdominal hemorrhage. FAST may help clinicians balance the risk of missed
intraabdominal injury with unnecessary exposure to ionizing radiation from CT. Dr. Kornblith’s research will focus
on improving pediatric FAST’s accuracy and reliability using machine learning models (Aim 1) and
developing/validating novel clinical decision rules incorporating FAST to identify children at very low risk for injury
who can forgo CT (Aim 2). Dr. Kornblith will use an existing dataset and computing infrastructure to develop and
validate a machine learning model using >2.1 million frames from 1,264 pediatric FAST studies to detect
hemorrhage as accurately as an expert (Aim 1), and two pre-existing datasets to develop and validate novel
clinical decision rules incorporating FAST and compare their performance to existing clinical decision rules (Aim
2). The proposed research and training plan will position Dr. Kornblith with cross-disciplinary skills to transition
to independen...

## Key facts

- **NIH application ID:** 10805422
- **Project number:** 5K23HD110716-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Aaron Edward Kornblith
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $162,432
- **Award type:** 5
- **Project period:** 2023-03-15 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10805422

## Citation

> US National Institutes of Health, RePORTER application 10805422, Accurate and Reliable Diagnostics for Injured Children: Machine Learning for Ultrasound (5K23HD110716-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10805422. Licensed CC0.

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