Researchers at George Washington University (GWU) have received a $2.2million grant to explore whether 3D optical scans and machine learning can identify fatty liver disease (FLD).
The project will use 3D depth cameras to capture an individual’s body shape and correlate this to obesity-associated health indicators using machine learning algorithms.
The team is specifically focused on the build up of fat in the liver known as fatty liver disease or hepatic steatosis.
FLD is the most common type of liver disease in developed countries and is most frequently developed between the ages of 40 and 60.
FLD can cause long-term scarring called cirrhosis which can lead to liver failure.
The researchers will recruit 250 patients undergoing bariatric [weight loss] surgery to understand how the body changes in response to rapid weight loss.
Using this group of patients will enable the researchers to perform a longitudinal study and collect indicators of health and shape before and after surgery.
Principal investigator, Professor James Hahn from the School of Engineering and Applied Science’s Department of Computer Science, said:
“As they lose weight, we can then see what the relationship is between body shape and X, where X could be things like liver health or the percentage of body fat, or changes in their blood tests that are performed periodically throughout their recovery process.”
The team will use 3D cameras to capture the patients’ body shapes. The patients will also undergo medical imaging using dual-energy X-ray absorptiometry, or DEXA, which measures bone density, muscle mass and body fat.
The researchers will combine this imaging with other clinical tests and a liver biopsy taken prior to surgery which will be used to train the machine learning algorithm.
Patients will have four follow up scans to track how their body shape changes as a function of health indicators over time.
The researchers plan to use the data to train systems to predict health indicators based on body shape, including FLD.
This method could prove to be less expensive and less invasive than existing diagnostic methods, Hahn said.
“This whole approach has a lot of potential,” Hahn said.
“This technology is not going to replace biopsies or other medical tests, but it will perhaps give physicians a new cheaper and more convenient imaging modality they can employ for diagnosis.”
Collaborator and bariatric surgeon Khashayar Vaziri, a professor of surgery at SMHS and program director of general surgery residency at GW Hospital, added:
“Bariatric surgery patients are just a small subset of the obese population. This would allow health care providers to apply this technology and get a better understanding and diagnose liver disease in this patient population much, much earlier.
“Diagnosis of liver disease is elusive, and this would be a very useful tool.”
Hahn said that the 3D body surface scanning is similar to depth camera technology available on newer smartphones and could be used at a doctor’s clinic or even in the home.