BACKGROUND: With the improvement of capillary electrophoresis, much progress has been made in terms of sensitivity and automation, but the interpretation of the patterns, actually, depends totally on expert personnel. The aim of this work was to evaluate Neurosoft-Sebia, an expert system developed to discriminate between regular and anomalous serum protein electrophoresis patterns performed on Capillarys2. METHODS: Neurosoft-Sebia, based on six auto-associative neural networks, was trained to create the initial knowledge base. In the tuning phase, 3000 electrophoretic patterns were performed in three different laboratories, and the discordances between human experts and Neurosoft-Sebia classifications were added to the initial knowledge base. Finally, the performances of Neurosoft-Sebia were evaluated using a benchmark dataset. RESULTS: The initial knowledge base was created with 2685 fractions. In the tuning phase, 241 discordances were found: 56 as regular by Neurosoft-Sebia and anomalous by human experts, and 185 as anomalous by Neurosoft-Sebia and regular by human experts. Sensitivity values were evidenced as the ability of Neurosoft-Sebia in selecting anomalous fractions, with an increase from 66.67% using the initial knowledge base to 97.40% using the enriched knowledge base. CONCLUSIONS: This work demonstrated how the ability of Neurosoft-Sebia in selecting anomalous pattern was comparable to that of human experts, saving time and providing rapid and standardized interpretations.

An expert system for the classification of serum protein electrophoresis patterns

ZANINOTTO, MARTINA;PLEBANI, MARIO
2008

Abstract

BACKGROUND: With the improvement of capillary electrophoresis, much progress has been made in terms of sensitivity and automation, but the interpretation of the patterns, actually, depends totally on expert personnel. The aim of this work was to evaluate Neurosoft-Sebia, an expert system developed to discriminate between regular and anomalous serum protein electrophoresis patterns performed on Capillarys2. METHODS: Neurosoft-Sebia, based on six auto-associative neural networks, was trained to create the initial knowledge base. In the tuning phase, 3000 electrophoretic patterns were performed in three different laboratories, and the discordances between human experts and Neurosoft-Sebia classifications were added to the initial knowledge base. Finally, the performances of Neurosoft-Sebia were evaluated using a benchmark dataset. RESULTS: The initial knowledge base was created with 2685 fractions. In the tuning phase, 241 discordances were found: 56 as regular by Neurosoft-Sebia and anomalous by human experts, and 185 as anomalous by Neurosoft-Sebia and regular by human experts. Sensitivity values were evidenced as the ability of Neurosoft-Sebia in selecting anomalous fractions, with an increase from 66.67% using the initial knowledge base to 97.40% using the enriched knowledge base. CONCLUSIONS: This work demonstrated how the ability of Neurosoft-Sebia in selecting anomalous pattern was comparable to that of human experts, saving time and providing rapid and standardized interpretations.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2268538
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