# Improved Diagnosis of Shunt Malfunction with Automatic Quantification of Ventricular Space

> **NIH NIH R41** · CARINA MEDICAL, LLC · 2022 · $339,999

## Abstract

ABSTRACT
Hydrocephalus is the buildup of cerebrospinal fluid (CSF) in the cavities (ventricles) deep within the brain. The
most common treatment for hydrocephalus is CSF diversion via ventriculoperitoneal (VP) shunting. Over
30,000 VP shunts are placed per year in the United States by some estimates. Despite how commonly this
surgery is performed, the complication rate has been estimated at almost 24%, with one report citing a 22%
rate of revision. Nearly 50% of patients admitted with shunt related issues require a stay of five or more days.
Given the rate of surgical site infections and complications associated with shunt explorations and revisions,
accurate diagnosis of a shunt malfunction remains a critical, if elusive, goal for many neurosurgeons. One of
the difficulties in establishing a diagnosis based on imaging alone is the lack of standardized robust methods of
measuring ventricular size. Recently volumetric analyses have been studied as a method for measuring
ventricular size, as compared to the Evans’ Index or frontal-occipital horn ratios and have been suggested is
more accurate and a better tool for measuring response of ventricular size to shunting. However, the
associated human efforts and inter- and intra-observer variability in segmenting the ventricles prohibits its wide
clinical adoption. The other difficulty with establishing a diagnosis of ventriculomegaly or hydrocephalus,
involves a lack of a standardized, normative dataset with a range of what is considered "normal" for various
age ranges as the ventricle size increases with age. Current literature lacks a robust normative dataset of
ventricular size by age and gender and only recently has such a dataset been produced for the pediatric age
range. Establishment of normative values for ventricular volume and morphology across all age population is
sorely needed and will allow for the investigation of a variety of topics related to hydrocephalus and ultimately
assisting in the detection and triage of hydrocephalus and VP shunt related complications or malfunctions. In
recent years, the rapid development of deep learning (DL) models has led to great impact on many areas of
medicine, especially for automatic image analysis tasks including segmentation. Taking advantage of DL
models, two aims are proposed in this project: 1) develop and validate a robust DL model for ventricle
segmentation including multi-modality support and automatic failure detection and build a normative database;
2) develop a software prototype that incorporates the DL model and normative values and fits the clinical
workflow for image-based diagnosis of shunt malfunction. Ultimately, a unique software product will be
developed and commercialized to improve the diagnosis of shunt malfunction and hydrocephalus and benefit
the patients with better surgical outcome and reduced cost.

## Key facts

- **NIH application ID:** 10384590
- **Project number:** 1R41NS125874-01
- **Recipient organization:** CARINA MEDICAL, LLC
- **Principal Investigator:** Xue Feng
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $339,999
- **Award type:** 1
- **Project period:** 2022-09-20 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10384590

## Citation

> US National Institutes of Health, RePORTER application 10384590, Improved Diagnosis of Shunt Malfunction with Automatic Quantification of Ventricular Space (1R41NS125874-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10384590. Licensed CC0.

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