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Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

  • Autori: Benammar Elgaaied, A.; Cascio, D.; Bruno, S.; Ciaccio, M.; Cipolla, C.; Fauci, A.; Morgante, R.; Taormina, V.; Gorgi, Y.; Marrakchi Triki, R.; Ben Ahmed, M.; Louzir, H.; Yalaoui, S.; Imene, S.; Issaoui, Y.; Abidi, A.; Ammar, M.; Bedhiafi, W.; Ben Fraj, O.; Bouhaha, R.; Hamdi, K.; Soumaya, K.; Neili, B.; Gati, A.; Lucchese, M.; Catanzaro, M.; Barbara, V.; Brusca, I.; Fregapane, M.; Amato, G.; Friscia, G.; Neila, T.; Turkia, S.; Youssra, H.; Rekik, R.; Bouokez, H.; Vasile Simone, M.; Fauci, F.; Raso, G.
  • Anno di pubblicazione: 2016
  • Tipologia: Articolo in rivista (Articolo in rivista)
  • Parole Chiave: Computer Aided Diagnosis, Immunofluorescence, Pattern Classification, IIF images, Autoimmune diseases, SVM, ANN, HEp-2
  • OA Link:


Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF)method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of aCAD(Computer AidedDetection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%).