Science News from research organizations AI blood test finds silent liver disease years before symptoms Date: March 6, 2026 Source: Johns Hopkins Medicine Summary: Researchers created an AI-driven liquid biopsy that scans patterns in fragments of DNA circulating in the blood. The system detected early liver fibrosis and cirrhosis—conditions that often go unnoticed until serious damage occurs. By analyzing genome-wide DNA fragmentation patterns rather than specific mutations, the approach captures hidden signals about a person’s overall health. Early detection could help doctors treat liver disease sooner and potentially prevent cancer. Share: Facebook Twitter Pinterest LinkedIN Email FULL STORY An AI-powered blood test that reads DNA fragmentation patterns could detect early liver damage—and possibly other chronic diseases—years before symptoms emerge. Credit: Shutterstock Researchers at the Johns Hopkins Kimmel Cancer Center have developed an artificial intelligence (AI) driven liquid biopsy that analyzes genome wide patterns of cell free DNA (cfDNA) fragments circulating in the blood. The test examines how these DNA pieces break apart and where they appear across the genome. Using this information, the system can identify early signs of liver fibrosis and cirrhosis and may also detect broader indicators of chronic disease. The study, partly funded by the National Institutes of Health, was published March 4 in Science Translational Medicine . It marks the first time that this type of DNA fragmentation analysis, known as fragmentome technology, has been systematically applied to detecting chronic diseases unrelated to cancer. Previously, the approach had mainly been investigated as a method for finding cancer. Genome Wide DNA Fragment Patterns Reveal Disease Signals Liquid biopsies that measure cfDNA have already shown promise for identifying cancer. However, scientists have not widely explored their potential for diagnosing other illnesses. In this new research, investigators performed whole genome sequencing on cfDNA samples from 1,576 individuals with liver disease and additional medical conditions. By examining DNA fragments across the entire genome, they searched for patterns that might signal disease. The team analyzed both the size of DNA fragments and their distribution throughout the genome, including repetitive DNA regions that have rarely been studied. Each analysis included about 40 million fragments spanning thousands of genomic regions, producing an enormous dataset compared with most liquid biopsy tests. Machine learning algorithms processed this information to identify fragmentation patterns linked to disease. Using these patterns, researchers created a classification system that detected early liver disease, advanced fibrosis and cirrhosis with high sensitivity. "This builds directly on our earlier fragmentome work in cancer, but now using AI and genome-wide fragmentation profiles of cell-free DNA to focus on chronic diseases," says Victor Velculescu, M.D., Ph.D., co-director of the cancer genetics and epigenetics program at the Johns Hopkins Kimmel Cancer Center and co-senior author of the study. "For many of these illnesses, early detection could make a profound difference, and liver fibrosis and cirrhosis are important examples. Liver fibrosis is reversible in early its stages, but if left undetected, it can progress to cirrhosis and ultimately increase the risk of liver cancer." Why DNA Fragment Analysis Is Different Unlike many liquid biopsy methods that search for specific cancer related gene mutations, the fragmentome approach focuses on how DNA fragments are cut, packaged and distributed throughout the genome. According to the researchers, this broader view makes the method applicable to conditions beyond cancer, including diseases that can eventually raise cancer risk. The study was also co led by Robert Scharpf, Ph.D., professor of oncology, and Jill Phallen, Ph.D., assistant professor of oncology. "The fact that we are not looking for individual mutations is what makes this study so powerful," says first author Akshaya Annapragada, an M.D./Ph.D. student in the Velculescu lab. "We are analyzing the entire fragmentome, which contains a tremendous amount of information about a person's physiologic state. The scale of these data, coupled with machine learning, enables development of specific classifiers for many different health conditions." Early Detection Could Benefit Millions at Risk Velculescu notes that roughly 100 million people in the United States have liver conditions that increase their risk of cirrhosis and liver cancer. Current blood based tests for fibrosis often lack sensitivity, especially in early stages of disease. Standard blood markers typically fail to detect early fibrosis and identify cirrhosis only about half the time. Imaging techniques such as specialized ultrasound or magnetic resonance scans can help, but these tools