NAFLD comprises a spectrum of progressive liver pathologies, ranging from simple steatosis to NASH, fibrosis, and cirrhosis. A liver biopsy is currently required to stratify high‐risk patients. Predicting the degree of liver inflammation and fibrosis using non‐invasive tests remains challenging.
Recent developments of commercially available thermal imaging products have attracted considerable attention for use of infrared thermography in biomedical imaging. Non-invasive thermal imaging combined with advanced image processing algorithms and machine learning-based analysis can correlate surface thermography with structural changes in internal organs of mice such as the heart. RY Brzezinski et al. (Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel) sought to develop a novel screening tool for NAFLD based on thermal imaging. They 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.
They developed an image processing algorithm that measures relative spatial thermal variation across the skin covering the liver. It demonstrated a 100% detection rate and classified all mice correctly according to their disease status.
The authors 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.