A Fuzzy-Based Clinical Decision Support System for Coeliac Disease
Coeliac disease (CD) is a permanent inflammatory disease of the small intestine characterized by the destruction of the mucous membrane of this intestinal tract. Coeliac disease represents the most frequent food intolerance and affects about 1% of the population, but it is severely underd...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9900343/ |
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| author | M. E. Tabacchi D. Tegolo D. Cascio C. Valenti S. Sorce V. Gentile V. Taormina I. Brusca G. Magazzu A. Giuliano G. Raso |
| author_facet | M. E. Tabacchi D. Tegolo D. Cascio C. Valenti S. Sorce V. Gentile V. Taormina I. Brusca G. Magazzu A. Giuliano G. Raso |
| author_sort | M. E. Tabacchi |
| collection | DOAJ |
| description | Coeliac disease (CD) is a permanent inflammatory disease of the small intestine characterized by the destruction of the mucous membrane of this intestinal tract. Coeliac disease represents the most frequent food intolerance and affects about 1% of the population, but it is severely underdiagnosed. Currently available guidelines require CD-specific serology and atrophic histology in duodenal biopsy samples to diagnose CD in adults. In paediatric CD, but recently in adults also, non-invasive diagnostic strategies have become increasingly popular. In order to increase the rates of correct diagnosis of the disease without the use of biopsy, researchers have recently been using approaches based on artificial intelligence techniques. In this work, we present a Clinical Decision Support System (CDSS)system for supporting CD diagnosis, developed in the context of the Italy-Malta cross-border project ITAMA. The implemented CDSS has been based on a neural-network-based fuzzy classifier. The system was developed and tested using a Virtual Database and a Real Database acquired during the ITAMA project. Analysis on 10,000 virtual patients shows that the system achieved an accuracy of 99% and a sensitivity of 99%. On 19,415 real patients, of which 109 with a confirmed diagnosis of coeliac disease, the system achieved 99.6% accuracy, 85.7% sensitivity, 99.6% specificity and 96% precision. Such results show that the developed system can be used effectively to support the diagnosis of the CD by reducing the appeal to invasive techniques such as biopsy. |
| first_indexed | 2024-04-11T09:20:08Z |
| format | Article |
| id | doaj.art-2e4fdccfc1eb4a389c4b77f840057724 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T09:20:08Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2e4fdccfc1eb4a389c4b77f8400577242022-12-22T04:32:13ZengIEEEIEEE Access2169-35362022-01-011010222310223610.1109/ACCESS.2022.32089039900343A Fuzzy-Based Clinical Decision Support System for Coeliac DiseaseM. E. Tabacchi0https://orcid.org/0000-0001-7230-8192D. Tegolo1https://orcid.org/0000-0001-5417-5584D. Cascio2https://orcid.org/0000-0001-6522-1259C. Valenti3https://orcid.org/0000-0002-4961-2054S. Sorce4https://orcid.org/0000-0003-1976-031XV. Gentile5V. Taormina6https://orcid.org/0000-0002-8313-2556I. Brusca7G. Magazzu8A. Giuliano9G. Raso10https://orcid.org/0000-0002-5660-3711Dipartimento di Matematica e Informatica, Università degli Studi di Palermo, Palermo, ItalyDipartimento di Matematica e Informatica, Università degli Studi di Palermo, Palermo, ItalyDipartimento di Fisica e Chimica, Università degli Studi di Palermo, Palermo, ItalyDipartimento di Matematica e Informatica, Università degli Studi di Palermo, Palermo, ItalyFacoltà di Ingegneria e Architettura, Università degli Studi di Enna ‘‘Kore,’’, Enna, ItalyDipartimento di Fisica e Chimica, Università degli Studi di Palermo, Palermo, ItalyDipartimento di Matematica e Informatica, Università degli Studi di Palermo, Palermo, ItalyOspedale Fatebenefratelli, Buccheri La Ferla, Palermo, ItalyDipartimento di Patologia Umana dell’adulto e dell’età evolutiva, Università di Messina, Messina, ItalyAcrossLimits Ltd., Valletta, MaltaDipartimento di Fisica e Chimica, Università degli Studi di Palermo, Palermo, ItalyCoeliac disease (CD) is a permanent inflammatory disease of the small intestine characterized by the destruction of the mucous membrane of this intestinal tract. Coeliac disease represents the most frequent food intolerance and affects about 1% of the population, but it is severely underdiagnosed. Currently available guidelines require CD-specific serology and atrophic histology in duodenal biopsy samples to diagnose CD in adults. In paediatric CD, but recently in adults also, non-invasive diagnostic strategies have become increasingly popular. In order to increase the rates of correct diagnosis of the disease without the use of biopsy, researchers have recently been using approaches based on artificial intelligence techniques. In this work, we present a Clinical Decision Support System (CDSS)system for supporting CD diagnosis, developed in the context of the Italy-Malta cross-border project ITAMA. The implemented CDSS has been based on a neural-network-based fuzzy classifier. The system was developed and tested using a Virtual Database and a Real Database acquired during the ITAMA project. Analysis on 10,000 virtual patients shows that the system achieved an accuracy of 99% and a sensitivity of 99%. On 19,415 real patients, of which 109 with a confirmed diagnosis of coeliac disease, the system achieved 99.6% accuracy, 85.7% sensitivity, 99.6% specificity and 96% precision. Such results show that the developed system can be used effectively to support the diagnosis of the CD by reducing the appeal to invasive techniques such as biopsy.https://ieeexplore.ieee.org/document/9900343/Coeliac diseasecomputer aided diagnosisartificial intelligenceendoscopyneural networkfuzzy classifier |
| spellingShingle | M. E. Tabacchi D. Tegolo D. Cascio C. Valenti S. Sorce V. Gentile V. Taormina I. Brusca G. Magazzu A. Giuliano G. Raso A Fuzzy-Based Clinical Decision Support System for Coeliac Disease IEEE Access Coeliac disease computer aided diagnosis artificial intelligence endoscopy neural network fuzzy classifier |
| title | A Fuzzy-Based Clinical Decision Support System for Coeliac Disease |
| title_full | A Fuzzy-Based Clinical Decision Support System for Coeliac Disease |
| title_fullStr | A Fuzzy-Based Clinical Decision Support System for Coeliac Disease |
| title_full_unstemmed | A Fuzzy-Based Clinical Decision Support System for Coeliac Disease |
| title_short | A Fuzzy-Based Clinical Decision Support System for Coeliac Disease |
| title_sort | fuzzy based clinical decision support system for coeliac disease |
| topic | Coeliac disease computer aided diagnosis artificial intelligence endoscopy neural network fuzzy classifier |
| url | https://ieeexplore.ieee.org/document/9900343/ |
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