{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T11:54:15Z","timestamp":1782129255163,"version":"3.54.5"},"reference-count":90,"publisher":"Walter de Gruyter GmbH","issue":"4","funder":[{"name":"Australian National Health and Medical Research Ideas Grant","award":["1184720"],"award-info":[{"award-number":["1184720"]}]},{"name":"Australian Commonwealth Department of Health Quality Use of Pathology Programme","award":["4-2UJWED1"],"award-info":[{"award-number":["4-2UJWED1"]}]},{"name":"RSTMH Early Career Grants Programme"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_dx-2023-0078_abs_001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early stages of hepatitis B virus (HBV) infection usually involve inflammation of the liver. Patients with chronic infection have an increased risk of progressive liver fibrosis, cirrhosis, and life-threatening clinical complications of end-stage hepatocellular carcinoma (HCC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_dx-2023-0078_abs_002\">\n                    <jats:title>Content<\/jats:title>\n                    <jats:p>Early diagnosis of hepatic fibrosis and timely clinical management are critical to controlling disease progression and decreasing the burden of end-stage liver cancer. Fibrosis staging, through its current gold standard, liver biopsy, improves patient outcomes, but the clinical procedure is invasive with unpleasant post-procedural complications. Routine blood test markers offer promising diagnostic potential for early detection of liver disease without biopsy. There is a plethora of candidate routine blood test markers that have gone through phases of biomarker validation and have shown great promise, but their current limitations include a predictive ability that is limited to only a few stages of fibrosis. However, the advent of machine learning, notably pattern recognition, presents an opportunity to refine blood-based non-invasive models of hepatic fibrosis in the future.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_dx-2023-0078_abs_003\">\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>In this review, we highlight the current landscape of routine blood-based non-invasive models of hepatic fibrosis, and appraise the potential application of machine learning (pattern recognition) algorithms to refining these models and optimising clinical predictions of HBV-associated liver disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_dx-2023-0078_abs_004\">\n                    <jats:title>Outlook<\/jats:title>\n                    <jats:p>Machine learning via pattern recognition algorithms takes data analytics to a new realm, and offers the opportunity for enhanced multi-marker fibrosis stage prediction using pathology profile that leverages information across patient routine blood tests.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/dx-2023-0078","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T11:55:48Z","timestamp":1695124548000},"page":"337-347","source":"Crossref","is-referenced-by-count":7,"title":["Routine blood test markers for predicting liver disease post HBV infection: precision pathology and pattern recognition"],"prefix":"10.1515","volume":"10","author":[{"given":"Busayo I.","family":"Ajuwon","sequence":"first","affiliation":[{"name":"National Centre for Epidemiology and Population Health , ANU College of Health and Medicine, The Australian National University , Acton , Australian Capital Territory , Australia"},{"name":"Department of Microbiology , Faculty of Pure and Applied Sciences, Kwara State University , Malete , Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katrina","family":"Roper","sequence":"additional","affiliation":[{"name":"National Centre for Epidemiology and Population Health , ANU College of Health and Medicine, The Australian National University , Acton , Australian Capital Territory , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice","family":"Richardson","sequence":"additional","affiliation":[{"name":"Statistical Support Network , The Australian National University , Acton , Australian Capital Territory , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brett A.","family":"Lidbury","sequence":"additional","affiliation":[{"name":"National Centre for Epidemiology and Population Health , ANU College of Health and Medicine, The Australian National University , Acton , Australian Capital Territory , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"2025121206461325210_j_dx-2023-0078_ref_001","unstructured":"World Health Organization. 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