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

Vincenzo Taormina, Donato Cascio, Giuseppe Raso, Sfar Imene, Myriam Ammar, Rym Bouhaha, Ahmed Abidi, Koudhi Soumaya, Sadok Yalaoui, Khouloud Hamdi, Maria Fregapane, Raja Marrakchi Triki, Koudhi Soumaya, Raja Rekik, Ignazio Brusca, Amel Benammar Elgaaied, Sfar Imene, Gati Asma, Bilel Neili, Walid BedhiafiGaetano Amato, Giuseppe Friscia, Vincenza Barbara, Maria Vasile Simone, Oussama Ben Fraj, Yassine Issaoui, Haouami Youssra, Trai Neila, Maria Catanzaro, Hayet Bouokez, Souayeh Turkia, Mariano Lucchese, Hechmi Louzir, Sfar Imene, Raja Marrakchi Triki, Melika Ben Ahmed, Gati Asma, Yousr Gorgi, Francesco Fauci, Alessandro Fauci, Maria Cristina Ciaccio, Rossella Morgante, Salvatore Bruno

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21 Citazioni (Scopus)


Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean ofIndirect ImmunoFluorescence (IIF)method, and performed by analyzing patterns and fluorescence intensity. This paper introducesthe AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-bordercooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of imagesand double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The GoldStandard 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 toidentify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as secondReader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared withtwo Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher PatternsAccuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%).
Lingua originaleEnglish
pagine (da-a)1-9
Numero di pagine9
RivistaBioMed Research International
Stato di pubblicazionePublished - 2016

All Science Journal Classification (ASJC) codes

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