Development and validation of a neural network for NAFLD diagnosis

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Publicat a:Scientific Reports (Nature Publisher Group) vol. 11, no. 1 (2021)
Autor principal: Sorino Paolo
Altres autors: Campanella, Angelo, Bonfiglio Caterina, Mirizzi Antonella, Franco, Isabella, Bianco, Antonella, Caruso, Maria Gabriella, Misciagna Giovanni, Aballay, Laura R, Buongiorno, Claudia, Liuzzi Rosalba, Cisternino, Anna Maria, Notarnicola, Maria, Chiloiro Marisa, Fallucchi Francesca, Pascoschi Giovanni, Osella, Alberto Rubén
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022 |a 2045-2322 
024 7 |a 10.1038/s41598-021-99400-y  |2 doi 
035 |a 2581099055 
045 2 |b d20210101  |b d20211231 
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100 1 |a Sorino Paolo  |u “S de Bellis” Research Hospital, Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, Castellana Grotte, Italy 
245 1 |a Development and validation of a neural network for NAFLD diagnosis 
260 |b Nature Publishing Group  |c 2021 
513 |a Journal Article 
520 3 |a Non-Alcoholic Fatty Liver Disease (NAFLD) affects about 20–30% of the adult population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Liver ultrasound (US) is widely used as a noninvasive method to diagnose NAFLD. However, the intensive use of US is not cost-effective and increases the burden on the healthcare system. Electronic medical records facilitate large-scale epidemiological studies and, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. Our goal was to develop and validate a simple Neural Network (NN)-based web app that could be used to predict NAFLD particularly its absence. The study included 2970 subjects; training and testing of the neural network using a train–test-split approach was done on 2869 of them. From another population consisting of 2301 subjects, a further 100 subjects were randomly extracted to test the web app. A search was made to find the best parameters for the NN and then this NN was exported for incorporation into a local web app. The percentage of accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall and f1-score were verified. After that, Explainability (XAI) was analyzed to understand the diagnostic reasoning of the NN. Finally, in the local web app, the specificity and sensitivity values were checked. The NN achieved a percentage of accuracy during testing of 77.0%, with an area under the ROC curve value of 0.82. Thus, in the web app the NN evidenced to achieve good results, with a specificity of 1.00 and sensitivity of 0.73. The described approach can be used to support NAFLD diagnosis, reducing healthcare costs. The NN-based web app is easy to apply and the required parameters are easily found in healthcare databases. 
653 |a Databases 
653 |a Software 
653 |a Hepatocellular carcinoma 
653 |a Liver diseases 
653 |a Epidemiology 
653 |a Diagnosis 
653 |a Electronic medical records 
653 |a Neural networks 
653 |a Fatty liver 
653 |a Health care 
653 |a Developed countries 
653 |a Liver cancer 
653 |a Social 
700 1 |a Campanella, Angelo  |u “S de Bellis” Research Hospital, Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, Castellana Grotte, Italy 
700 1 |a Bonfiglio Caterina  |u “S de Bellis” Research Hospital, Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, Castellana Grotte, Italy 
700 1 |a Mirizzi Antonella  |u “S de Bellis” Research Hospital, Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, Castellana Grotte, Italy 
700 1 |a Franco, Isabella  |u “S de Bellis” Research Hospital, Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, Castellana Grotte, Italy 
700 1 |a Bianco, Antonella  |u “S de Bellis” Research Hospital, Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, Castellana Grotte, Italy 
700 1 |a Caruso, Maria Gabriella  |u “S de Bellis” Research Hospital, Laboratory of Nutritional Biochemistry, National Institute of Gastroenterology, Castellana Grotte, Italy 
700 1 |a Misciagna Giovanni  |u University of Bari, Scientific and Ethical Committee, Polyclinic Hospital, Bari, Italy (GRID:grid.7644.1) (ISNI:0000 0001 0120 3326) 
700 1 |a Aballay, Laura R  |u Universidad Nacional de Córdoba, Human Nutrition Research Center (CenINH), School of Nutrition, Faculty of Medical Sciences, Córdoba, Argentina (GRID:grid.10692.3c) (ISNI:0000 0001 0115 2557) 
700 1 |a Buongiorno, Claudia  |u “S de Bellis” Research Hospital, Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, Castellana Grotte, Italy (GRID:grid.10692.3c) 
700 1 |a Liuzzi Rosalba  |u “S de Bellis” Research Hospital, Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, Castellana Grotte, Italy (GRID:grid.10692.3c) 
700 1 |a Cisternino, Anna Maria  |u “S de Bellis” Research Hospital, Clinical Nutrition Outpatient Clinic, National Institute of Gastroenterology, Castellana Grotte, Italy (GRID:grid.10692.3c) 
700 1 |a Notarnicola, Maria  |u “S de Bellis” Research Hospital, Laboratory of Nutritional Biochemistry, National Institute of Gastroenterology, Castellana Grotte, Italy (GRID:grid.10692.3c) 
700 1 |a Chiloiro Marisa  |u San Giacomo Hospital, Monopoli, Italy (GRID:grid.10692.3c) 
700 1 |a Fallucchi Francesca  |u Guglielmo Marconi University, Department of Engineering Sciences, Rome, Italy (GRID:grid.440899.8) (ISNI:0000 0004 1780 761X) 
700 1 |a Pascoschi Giovanni  |u Polytechnic of Bari, Department of Electrical and Information Engineering, Bari, Italy (GRID:grid.4466.0) (ISNI:0000 0001 0578 5482) 
700 1 |a Osella, Alberto Rubén  |u “S de Bellis” Research Hospital, Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, Castellana Grotte, Italy (GRID:grid.4466.0) 
773 0 |t Scientific Reports (Nature Publisher Group)  |g vol. 11, no. 1 (2021) 
786 0 |d ProQuest  |t Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2581099055/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/2581099055/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch