A Novel Expert System for Non-Invasive Liver Iron Overload Estimation in Thalassemic Patients
| Autor: | Emanuele Grassedonio, Maria Antonietta Russo, Massimo Midiri, Patrizia Toia, Luca Agnello, Salvatore Vitabile, Alfonso Farruggia, Elena Murmura |
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| Přispěvatelé: | Farruggia, A, Agnello, L, Toia, P, Murmura, E, Russo, M, Grassedonio, E, Midiri, M, Vitabile, S |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
Liver Iron Concentration Mean squared error Artificial neural network Computer science Remote patient monitoring business.industry Computational intelligence Complex problem solving computer.software_genre Expert system LIOMOT MRI T2* Iron Liver Thalassemia Artificial Neural Network Expert System OsiriX Liver iron Artificial intelligence Data mining business computer |
| Zdroj: | CISIS |
| Popis: | Expert Systems can integrate logic based often on computational intelligence methods and they are used in complex problem solving. In this work an Expert System for classifying liver iron concentration in thalassemic patients is presented. In this work, an ANN is used to validate the output of the L.I.O.MO.T (Liver Iron Overload Monitoring in Thalassemia) method against the output of the state-of-the-art method based on MRI T2 assessment for liver iron concentration. The model has been validated with a dataset of 200 samples. The experimental Mean Squared Error results and Correlation show interesting performances. The proposed algorithm has been developed as a plug in for OsiriX Dicom Viewer. |
| Databáze: | OpenAIRE |
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