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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Adult, Biopsy, Cross-Sectional Studies, Female, HIV Infections, Hepatitis C, Chronic, Humans, Liver, Liver Cirrhosis, Male, Neural Networks (Computer), Predictive Value of Tests, Retrospective Studies.
user
Does an artificial neural network improve the non-invasive diagnosis of significant fibrosis in HIV/HCV coinfected patients? Context: To develop an artificial neural network to predict significant fibrosis (F≥2) (ANN-SF) in HIV/Hepatitis C (HCV) coinfected patients using clinical data derived from peripheral blood. Patients were randomly divided into an estimation group (217 cases) used to generate the ANN and a test group (145 cases) used to confirm its power to predict F≥2. Liver fibrosis was estimated according to the METAVIR score. The values of the area under the receiver operating characteristic curve (AUC-ROC) of the ANN-SF were 0.868 in the estimation set and 0.846 in the test set. In the estimation set, with a cut-off value of <0.35 to predict the absence of F≥2, the sensitivity (Se), specificity (Sp), and positive (PPV) and negative predictive values (NPV) were 94.1%, 41.8%, 66.3% and 85.4% respectively. Furthermore, with a cut-off value of >0.75 to predict the presence of F≥2, the ANN-SF provided Se, Sp, PPV and NPV of 53.8%, 94.9%, 92.8% and 62.8% respectively. In the test set, with a cut-off value of <0.35 to predict the absence of F≥2, the Se, Sp, PPV and NPV were 91.8%, 51.7%, 72.9% and 81.6% respectively. Furthermore, with a cut-off value of >0.75 to predict the presence of F≥2, the ANN-SF provided Se, Sp, PPV and NPV of 43.5%, 96.7%, 94.9% and 54.7% respectively.
assistant
The ANN-SF accurately predicted significant fibrosis and outperformed other simple non-invasive indices for HIV/HCV coinfected patients. Our data suggest that ANN may be a helpful tool for guiding therapeutic decisions in clinical practice concerning HIV/HCV coinfection. Final Decision: yes