# Leveraging Novel Sources of Data with Analytic Morphomics to Improve Delivery of Specialty Care to At-Risk Veterans with Liver Disease

> **NIH VA I01** · VETERANS HEALTH ADMINISTRATION · 2021 · —

## Abstract

Background: Chronic liver diseases (CLD) are a group of common, costly, and clinically
consequential disorders that are increasingly prevalent in Veterans. Patients with CLD are
typically referred to specialty care for further evaluation. Such referrals can improve clinical
outcomes, but universal referral is not feasible due to resource limitations. Moreover, universal
specialty care referral is not only impractical – it is also unnecessary. Most patients with CLD will
have stable disease for years to decades (favorable prognosis), while others will rapidly progress
to liver fibrosis and cirrhosis (unfavorable prognosis). Given the vast number of Veterans with
CLD, we need a way to systematically and reliably identify patients with an unfavorable prognosis
who are at increased risk for poor clinical outcomes (and would therefore benefit from early
specialty care referral). Identifying patients with an unfavorable prognosis is challenging due to a
lack of clinical signs and symptoms in most patients with CLD; however, signs are routinely
detectable on radiological imaging. Because these radiologic findings are qualitative in nature and
encoded within imaging data, they have traditionally been of limited clinical utility. We propose to
utilize a novel technology, analytic morphomics, to leverage this phenotypic data. Analytic
morphomics utilizes high throughput computational image processing algorithms to provide
precise and detailed measurements of organs and body tissues. Within computed tomography
(CT) scans, an immense amount of data about patient phenotypes has been largely ignored and
unused. By digitally extracting and quantitatively analyzing structural data from these scans, we
hypothesize that we can identify patients with an unfavorable prognosis.
Objectives: (1) to refine and validate risk prediction models using analytic morphomics in Veterans
with chronic liver disease; (2) to increase the throughput and automation of the analytic
morphomics image processing algorithms using machine learning; (3) to quantify the clinical
impact of a risk-based specialty care clinic triage strategy (using prediction models) versus a
standard triage strategy (usual care), using simulation modeling.
Methods: This study will use advanced quantitative methods including analytic morphomics and
deep learning (a type of machine learning). Aim 1 will refine and validate risk prediction models
using analytic morphomics in Veterans with CLD. Aim 2 will examine the role of deep learning to
increase the throughput and automation of the image processing algorithms used in Aim 1.
Finally, Aim 3 will quantify the clinical impact of a risk-based specialty care clinic triage strategy
(using prediction models) versus a standard triage strategy (usual care).
Impacts: The Veterans Health Administration (VHA) is in a unique position to link routinely-
collected imaging data to important clinical outcomes. This study will lay the groundwork for the
use of this currently un...

## Key facts

- **NIH application ID:** 10186546
- **Project number:** 5I01HX002548-03
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** Sameer Dev Saini
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10186546

## Citation

> US National Institutes of Health, RePORTER application 10186546, Leveraging Novel Sources of Data with Analytic Morphomics to Improve Delivery of Specialty Care to At-Risk Veterans with Liver Disease (5I01HX002548-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10186546. Licensed CC0.

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