Butaru AE, Mămuleanu M, Streba CT, Doica IP, Diculescu MM, Gheonea DI, Oancea CN. Resource Management through Artificial Intelligence in Screening Programs-Key for the Successful Elimination of Hepatitis C.
Diagnostics (Basel) 2022;
12:diagnostics12020346. [PMID:
35204437 PMCID:
PMC8871056 DOI:
10.3390/diagnostics12020346]
[Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 01/23/2022] [Accepted: 01/26/2022] [Indexed: 02/05/2023] Open
Abstract
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.
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