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

Giuseppe Raso, Donato Cascio, Rossella Morgante, Salvatore Bruno, Alessandro Fauci, Maria Cristina Ciaccio, Vincenzo Taormina, Melika Ben Ahmed, Hechmi Louzir, Yousr Gorgi, Francesco Fauci, Myriam Ammar, Rym Bouhaha, Ahmed Abidi, Sadok Yalaoui, Khouloud Hamdi, Maria Fregapane, Raja Marrakchi Triki, Koudhi Soumaya, Raja Rekik & 18 others Ignazio Brusca, Amel Benammar Elgaaied, Gati Asma, Bilel Neili, Walid Bedhiafi, Gaetano Amato, Giuseppe Friscia, Vincenza Barbara, Maria Vasile Simone, Oussama Ben Fraj, Yassine Issaoui, Haouami Youssra, Trai Neila, Maria Catanzaro, Hayet Bouokez, Sfar Imene, Souayeh Turkia, Mariano Lucchese

Risultato della ricerca: Article

12 Citazioni (Scopus)

Abstract

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%).
Lingua originaleEnglish
pagine (da-a)-
Numero di pagine9
RivistaBioMed Research International
Volume2016
Stato di pubblicazionePublished - 2016

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Pattern recognition
Computer aided design
Fluorescence
Databases
Computer-Assisted Diagnosis
Tunisia
Antinuclear Antibodies
Biomarkers
Autoimmunity
Autoantibodies
Italy
Autoimmune Diseases
Fluorescent Antibody Technique

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

Cita questo

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

In: BioMed Research International, Vol. 2016, 2016, pag. -.

Risultato della ricerca: Article

Raso, G, Cascio, D, Morgante, R, Bruno, S, Fauci, A, Ciaccio, MC, Taormina, V, Ben Ahmed, M, Louzir, H, Gorgi, Y, Fauci, F, Ammar, M, Bouhaha, R, Abidi, A, Yalaoui, S, Hamdi, K, Fregapane, M, Marrakchi Triki, R, Soumaya, K, Rekik, R, Brusca, I, Benammar Elgaaied, A, Asma, G, Neili, B, Bedhiafi, W, Amato, G, Friscia, G, Barbara, V, Vasile Simone, M, Ben Fraj, O, Issaoui, Y, Youssra, H, Neila, T, Catanzaro, M, Bouokez, H, Imene, S, Turkia, S & Lucchese, M 2016, 'Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project', BioMed Research International, vol. 2016, pagg. -.
Raso, Giuseppe ; Cascio, Donato ; Morgante, Rossella ; Bruno, Salvatore ; Fauci, Alessandro ; Ciaccio, Maria Cristina ; Taormina, Vincenzo ; Ben Ahmed, Melika ; Louzir, Hechmi ; Gorgi, Yousr ; Fauci, Francesco ; Ammar, Myriam ; Bouhaha, Rym ; Abidi, Ahmed ; Yalaoui, Sadok ; Hamdi, Khouloud ; Fregapane, Maria ; Marrakchi Triki, Raja ; Soumaya, Koudhi ; Rekik, Raja ; Brusca, Ignazio ; Benammar Elgaaied, Amel ; Asma, Gati ; Neili, Bilel ; Bedhiafi, Walid ; Amato, Gaetano ; Friscia, Giuseppe ; Barbara, Vincenza ; Vasile Simone, Maria ; Ben Fraj, Oussama ; Issaoui, Yassine ; Youssra, Haouami ; Neila, Trai ; Catanzaro, Maria ; Bouokez, Hayet ; Imene, Sfar ; Turkia, Souayeh ; Lucchese, Mariano. / Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project. In: BioMed Research International. 2016 ; Vol. 2016. pagg. -.
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T1 - Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

AU - Raso, Giuseppe

AU - Cascio, Donato

AU - Morgante, Rossella

AU - Bruno, Salvatore

AU - Fauci, Alessandro

AU - Ciaccio, Maria Cristina

AU - Taormina, Vincenzo

AU - Ben Ahmed, Melika

AU - Louzir, Hechmi

AU - Gorgi, Yousr

AU - Fauci, Francesco

AU - Ammar, Myriam

AU - Bouhaha, Rym

AU - Abidi, Ahmed

AU - Yalaoui, Sadok

AU - Hamdi, Khouloud

AU - Fregapane, Maria

AU - Marrakchi Triki, Raja

AU - Soumaya, Koudhi

AU - Rekik, Raja

AU - Brusca, Ignazio

AU - Benammar Elgaaied, Amel

AU - Asma, Gati

AU - Neili, Bilel

AU - Bedhiafi, Walid

AU - Amato, Gaetano

AU - Friscia, Giuseppe

AU - Barbara, Vincenza

AU - Vasile Simone, Maria

AU - Ben Fraj, Oussama

AU - Issaoui, Yassine

AU - Youssra, Haouami

AU - Neila, Trai

AU - Catanzaro, Maria

AU - Bouokez, Hayet

AU - Imene, Sfar

AU - Turkia, Souayeh

AU - Lucchese, Mariano

PY - 2016

Y1 - 2016

N2 - 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%).

AB - 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%).

KW - Computer Aided Diagnosis, Immunofluorescence, Pattern Classification, IIF images, Autoimmune diseases, SVM, ANN, HEp-2

UR - http://hdl.handle.net/10447/172813

UR - http://www.hindawi.com/journals/bmri/2016/2073076/

M3 - Article

VL - 2016

SP - -

JO - BioMed Research International

JF - BioMed Research International

SN - 2314-6133

ER -