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) |
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| Altres autors: | , , , , , , , , , , , , , , , |
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Nature Publishing Group
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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| 001 | 2581099055 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-021-99400-y |2 doi | |
| 035 | |a 2581099055 | ||
| 045 | 2 | |b d20210101 |b d20211231 | |
| 084 | |a 274855 |2 nlm | ||
| 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 |