James Hahn
George Washington University, DC·Biomedical Engineering
Body mass index (BMI) is the usual way to diagnose obesity, but it is a poor guide to health risk. Better measures, such as waist-to-hip ratio or the amount of visceral fat around the organs, are either imprecise or need expensive machines.
- Fields
Biomedical Engineering
Computer Science
Medicine & Clinical Research
- Topics
obesity
bariatric surgery
3D optical body scanning
- Grants
- 1 known grant, last ended Aug 2026
What they work on
- Develop machine learning algorithms that predict liver fat (hepatic steatosis), liver scarring (fibrosis), body fat and blood biomarkers from 3D optical body surface scans, trained and validated against liver biopsy data.
- Assess optical body scans as measures of obesity-related health in a cross-sectional and one-year longitudinal study of bariatric surgery patients, comparing scans with DXA body composition imaging and blood biomarkers.
- Build a database of body scan, DXA and blood biomarker data from people with severe obesity, along with software for mining it.
- Morph each patient's 3D body scan into larger or smaller versions of their own shape to study body image perception, especially as bodies change after weight-loss surgery.
More about the research
Fatty liver disease linked to obesity is usually confirmed by liver biopsy, an invasive procedure, and non-invasive scans lose accuracy in people with high BMI. Body image, a key motivator for weight loss, is still judged mostly with self-reports and simple cartoon drawings. This project asks whether cheap 3D scans of the body's outer shape, now possible with phones and tablets, can capture health and psychological information in people with severe obesity.
Why it matters
Cheap 3D body scans from consumer devices could give doctors a non-invasive, low-cost way to estimate liver fat, liver scarring, body fat and blood markers in people with severe obesity, possibly reducing the need for biopsies and costly imaging. Person-specific body images could also improve how body image is assessed in weight-loss surgery patients.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Ren Zhang
Wayne State University, MI·Biochemistry
Determine whether MNADK, the mitochondrial NAD kinase, preserves liver energy balance and insulin sensitivity and reduces risk of NASH and type 2 diabetes, using genetically engineered mouse models.
Shuxia Wang
University of Kentucky, KY·Medicine & Clinical Research
Determine how the macrophage protein SMPDL3B regulates the receptor CD36 and the inflammatory signaling triggered when TSP1, a signaling protein, binds CD36.
Yong-Xu Wang
University of Massachusetts Chan Medical School, MA·Physiology
Test whether a newly identified adipokine, a hormone released by fat tissue, resolves NASH and associated metabolic problems in two different NASH mouse models.
The school
George Washington University: 13 labs on PI Pages, $382M a year on research, 225 research doctorates a year.
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