Background: The elimination of the Hepatitis C virus (HCV) will only be possible if rapid and efficient actions are taken. Artificial neural networks (ANNs) are computing systems based on the topology of the biological brain, containing connected artificial neurons that can be tasked with solving medical problems. Aim: We expanded the previously presented HCV micro-elimination project started in September 2020 that aimed to identify HCV infection through coordinated screening in asymptomatic populations and developed two ANN models able to identify at-risk subjects selected through a targeted questionnaire. Material and method: Our study included 14,042 screened participants from a southwestern region of Oltenia, Romania. Each participant completed a 12-item questionnaire along with anti-HCV antibody rapid testing. Hepatitis-C-positive subjects were linked to care and ultimately could receive antiviral treatment if they had detectable viremia. We built two ANNs, trained and tested on the dataset derived from the questionnaires and then used to identify patients in a similar, already existing dataset. Results: We found 114 HCV-positive patients (81 females), resulting in an overall prevalence of 0.81%. We identified sharing personal hygiene items, receiving blood transfusions, having dental work or surgery and re-using hypodermic needles as significant risk factors. When used on an existing dataset of 15,140 persons (119 HCV cases), the first ANN models correctly identified 97 (81.51%) HCV-positive subjects through 13,401 tests, while the second ANN model identified 81 (68.06%) patients through only 5192 tests. Conclusions: The use of ANNs in selecting screening candidates may improve resource allocation and prioritize cases more prone to severe disease.

Background: In response to the goal of the World Health Organisation to eliminate hepatitis C virus infections by 2030, Romania is striving for national elimination. An already successful micro-elimination project was expanded to test-and-treat specific populations and at-risk groups. The aim of this project was to identify the individuals with HCV infection in disadvantaged regions who do not have proper medical care access. Materials and Methods: Our two-arm interventional cross-sectional study used rapid anti-HCV antibody testing on two population groups from the Romanian southwestern region of Oltenia, approached between September 2020 and May 2021. The first group consisted of predominantly over 40 years old individuals, recruited through five family doctors from two medium-sized towns (community lot—CL). We approached a second group, aged 18–65, through 11 medical offices of five large factories in the same region (industry lot, IL). A 12-items questionnaire was given to each participant, to determine risk factors and record demographic data. Eligible patients initiated antiviral therapy using direct-acting antivirals (DAAs). Results: We enrolled 15,383 individuals between all 16 locations. The overall prevalence by antibody testing was 0.77% (119 cases). Of these, 57 subsequently received treatment with DAAs. We identified blood transfusions as a risk factor within the CL. Participants in the IL reported a relatively high risk for the following situations: sharing of personal hygiene belongings with another person, performing previous blood transfusions, dental interventions and previous surgery. Conclusions: In this global context, the use of micro-elimination allows interventions to be faster and more efficient. This is possible by targeting smaller and specific HCV risk groups.