Johns Hopkins and collaborators expanded cell‑free DNA fragmentomics beyond oncology, showing fragment‑level cfDNA patterns can detect liver fibrosis and cirrhosis and flag chronic disease signals. The Science Translational Medicine pilot exploited genome‑wide cfDNA fragmentation and machine learning to identify organ‑specific injury signatures in blood. A companion Johns Hopkins study applied an AI‑based fragmentome classifier that links genome packaging changes to immune and tissue signals, demonstrating a route to noninvasive screening for chronic liver disease. The teams described correlations with clinical fibrosis and cirrhosis metrics and suggested pathways to scale as a diagnostic alternative to invasive liver biopsy. Fragmentomics interrogates cfDNA fragmentation patterns—nucleosome footprints and fragment endpoints—rather than mutations, enabling detection of tissue‑specific cell death or remodeling across non‑malignant diseases.