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

Amel Benammar Elgaaied , Donato Cascio , Salvatore Bruno , Maria Cristina Ciaccio , Marco Cipolla , Alessandro Fauci , Rossella Morgante , Vincenzo Taormina , Yousr Gorgi , Raja Marrakchi Triki , Melika Ben Ahmed , Hechmi Louzir (1, 2) , Sadok Yalaoui (3) , Sfar Imene , Yassine Issaoui , Ahmed Abidi , Myriam Ammar , Walid Bedhiafi , Oussama Ben Fraj , Rym Bouhaha , Khouloud Hamdi (4) , Koudhi Soumaya , Bilel Neili , Gati Asma , Mariano Lucchese , Maria Catanzaro , Vincenza Barbara , Ignazio Brusca , Maria Fregapane , Gaetano Amato , Giuseppe Friscia , Trai Neila , Souayeh Turkia , Haouami Youssra , Raja Rekik , Hayet Bouokez , Maria Vasile Simone , Francesco Fauci , Giuseppe Raso

1 UTM - Université de Tunis El Manar
2 LR11IPT02 - Laboratoire de Transmission, Contrôle et Immunobiologie des Infections - Laboratory of Transmission, Control and Immunobiology of Infection
3 Laboratory of medical biology
4 Laboratory of Genetics, Immunology and Human Pathology

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 a CAD(Computer Aided Detection) 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%).

Domaines: Sciences du Vivant [q-bio]

Amel Benammar Elgaaied, Donato Cascio, Salvatore Bruno, Maria Cristina Ciaccio, Marco Cipolla, et al.. Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project. BioMed Research International , 2016, ⟨10.1155/2016/2073076⟩. ⟨hal-01358490⟩