Automated thermal imaging for the detection of fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) comprises a spectrum of progressive liver pathologies, ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. A liver biopsy is currently required to stratify high-risk patients, and predicting the degree of liver inflammation and fibrosis using non-invasive tests remains challenging. Here, we sought to develop a novel, cost-effective screening tool for NAFLD based on thermal imaging. We used a commercially available and non-invasive thermal camera and developed a new image processing algorithm to automatically predict disease status in a small animal model of fatty liver disease. To induce liver steatosis and inflammation, we fed C57/black female mice (8 weeks old) a methionine-choline deficient diet (MCD diet) for 6 weeks. We evaluated structural and functional liver changes by serial ultrasound studies, histopathological analysis, blood tests for liver enzymes and lipids, and measured liver inflammatory cell infiltration by flow cytometry. We developed an image processing algorithm that measures relative spatial thermal variation across the skin covering the liver. Thermal parameters including temperature variance, homogeneity levels and other textural features were fed as input to a t-SNE dimensionality reduction algorithm followed by k-means clustering. During weeks 3,4, and 5 of the experiment, our algorithm demonstrated a 100% detection rate and classified all mice correctly according to their disease status. Direct thermal imaging of the liver confirmed the presence of changes in surface thermography in diseased livers. We conclude that non-invasive thermal imaging combined with advanced image processing and machine learning-based analysis successfully correlates surface thermography with liver steatosis and inflammation in mice. Future development of this screening tool may improve our ability to study, diagnose and treat liver disease.

Non-invasive thermal imaging of cardiac remodeling in mice.

Thermal infrared imaging has been suggested as a non-invasive alternative to monitor physiological processes and disease. However, the use of this technique to image internal organs, such as the heart, has not yet been investigated. We sought to determine the ability of our novel thermal image-processing algorithm to detect structural and functional changes in a mouse model of hypertension and cardiac remodeling. Twelve mice were randomly assigned to receive either the pro-inflammatory, hypertensive hormone angiotensin-II (2 mg/kg/day, n = 6) or saline (n = 6) infusion for 28 days. We performed weekly blood pressure measurements, together with serial trans-thoracic echocardiography studies and histopathological evaluation of the hearts. Thermal images were captured with a commercially available thermal camera, and images were processed by our novel algorithm which analyzes relative spatial temperature variation across the animal's thorax. We assessed cardiac inflammation by measuring inflammatory cell infiltration through flow cytometry. Angiotensin infusion increased blood pressure together with cardiac hypertrophy and fibrosis. Thermal imaging at day 28 of the experiment detected an increase in the fraction of the skin heated by the heart in angiotensin-treated mice. Thermal image findings were significantly correlated to left ventricular volume and mass parameters seen on echocardiography (r = 0.8, p < 0.01 and r = 0.6, p = 0.07). We also identified distinct changes in the spatial heat profiles of all angiotensin-treated hearts, possibly reflecting remodeling processes in the hypertensive heart. Finally, a machine learning based model using thermal imaging parameters predicted intervention status in 10 out of 11 mice similar to a model using echocardiographic measurements. Our findings suggest, for the first time, that a new thermal image-processing algorithm successfully correlates surface thermography with cardiac structural changes in mice with hypertensive heart disease.