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Disclosures: Kezia Hobson: Perspectum Ltd: Employee, Dylan Windell: Perspectum: Employee, asd: Employee, Paul Aljabar: Nothing to Disclose, George Ralli: Nothing to Disclose, Rob Goldin: Nothing to Disclose, Kenneth Fleming: Nothing to Disclose, Caitlin Langford: Nothing to Disclose 775 HISTOLOGICAL DIAGNOSTIC TOOL FOR DETECTING AND CHARACTERISING PORTAL TRACTS IN LIVER DISEASE USING ARTIFICIAL INTELLIGENCE Dylan Windell 1 Alastair Magness 1 Abhishek Roy 1 Paul Aljabar 1 Timothy Kendall 2 Eve Fryer 3 Kenneth Fleming 4 Rob Goldin 5 Caitlin Langford 1 , 1 Perspectum, 2 University of Edinburgh, 3 Oxford University Hospitals NHS, 4 University of Oxford, 5 Imperial College London Background: AI- driven digital workflows for liver biopsy grading and staging have the potential to produce more accurate and reproducible results than traditional assessments

The proportion of treatment-nave patients (71.6% in their study and with all were treatment-experienced in our study), male participants (91.9 and 51.1% in Japan and our study, respectively), in addition to study design, sociodemographic characteristics and differences in ethnicity might contribute to this incongruency of findings Although individuals of Africans origins had the lowest hyperuricemia and gout risk allele frequencies than Asian descent (61), they may respond differently to ART to result in hyperuricemia

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