# Advancing 3D optical body surface scan technology to assess physiological and psychological effects in highly obese population

> **NIH NIH R01** · GEORGE WASHINGTON UNIVERSITY · 2022 · $586,009

## Abstract

Project Summary
Current techniques for measuring the physiological or psychological effects of obesity, and in particular bariatric
surgery, either lack sensitivity, are invasive, or require expensive specialized devices. For measuring the
physiological effects, BMI is commonly used to
diagnose obesity despite the known shortcomings as a marker
for metabolic syndrome. Biomarkers such as anthropometric measurements (e.g., waist-to-hip ratio) and visceral
adipose tissue (VAT) have been shown to be superior to BMI in predicting health risks associated with obesity,
but these measures lack sensitivity, specificity, or are expensive. Another key morbidity associated with obesity
is non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). The gold standard to the
diagnosis and assessment of these conditions is histologic evaluation of liver biopsies. Fibroscan transient
elastography is a noninvasive test which can also assess fibrosis, but high BMI and severe steatosis can
decrease its accuracy. These approaches are either invasive, expensive, or relatively inaccurate. Measures of
self-body perception are commonly used to assess psychological aspects of obesity, as body image is an
important motivator for diet, physical activity, and weight loss intention. Body image perception is commonly
assessed using self-reports and cartoon-like line drawings which are non-subject specific and insensitive.
Based on our preliminary work (R21HL124443) that developed optical body scanning technology to capture 3D
body shapes using inexpensive hardware, we propose to use the technology to study the physiological and
psychological effects on subjects with severe obesity:
 · Develop prediction algorithm for hepatic steatosis, fibrosis, adiposity, and blood biomarkers using optical
 scans. The optical scan and biopsy data will be used as the training and validation set to develop a
 machine learning algorithm to cheaply and non-invasively predict the biomarkers from 3D body surface
data.
 · Develop the use of optical scans for measuring the physiological effects of obesity. We will conduct a
 cross sectional and longitudinal study to further assess the use of optical surface scans to determine
 health indicators associated with obesity (using data from surface scan, DXA and serum biomarkers) for
 one year following surgery. We will establish a database of such data along with software for data mining
 the database.
 · Develop the use of optical scans for measuring the psychological effects of obesity. We will collect 3D
 surface geometry of each subject using optical scans and morph these to produce subject-specific
images of larger or smaller body shape. We will use these images to study perception related to obesity
and in particular patients undergoing post-operative body changes

## Key facts

- **NIH application ID:** 10455037
- **Project number:** 5R01DK129809-02
- **Recipient organization:** GEORGE WASHINGTON UNIVERSITY
- **Principal Investigator:** JAMES K HAHN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $586,009
- **Award type:** 5
- **Project period:** 2021-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10455037

## Citation

> US National Institutes of Health, RePORTER application 10455037, Advancing 3D optical body surface scan technology to assess physiological and psychological effects in highly obese population (5R01DK129809-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10455037. Licensed CC0.

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