{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:44:26Z","timestamp":1778575466447,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Antibiotics"],"abstract":"<jats:p>Background: Urine microbial analysis is a frequently requested test that is often associated with contamination during specimen collection or storage, which leads to false-positive diagnoses and delayed reporting. In the era of digitalization, machine learning (ML) can serve as a valuable tool to support clinical decision-making. Methods: This study investigates the application of a simple artificial neural network (ANN) to pre-identify negative and contaminated (false-positive) specimens. An ML model was developed using 8181 urine samples, including cytology, dipstick tests, and culture results. The dataset was randomly split 2:1 for training and testing a multilayer perceptron (MLP). Input variables with a normalized importance below 0.2 were excluded. Results: The final model used only microbial and either urine color or urobilinogen pigment analysis as inputs; other physical, chemical, and cellular parameters were omitted. The frequency of positive and negative specimens for bacteria was 6.9% and 89.6%, respectively. Contaminated specimens represented 3.5% of cases and were predominantly misclassified as negative by the MLP. Thus, the negative predictive value (NPV) was 96.5% and the positive predictive value (PPV) was 87.2%, leading to 0.82% of the cultures being unnecessary microbial cultures (UMC). Conclusions: These results suggest that the MLP is reliable for screening out negative specimens but less effective at identifying positive ones. In conclusion, ANN models can effectively support the screening of negative urine samples, detect clinically significant bacteriuria, and potentially reduce unnecessary cultures. Incorporating morphological information data could further improve the accuracy of our model and minimize false negatives.<\/jats:p>","DOI":"10.3390\/antibiotics14080768","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T07:37:23Z","timestamp":1753861043000},"page":"768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Neural Network for the Fast Screening of Samples from Suspected Urinary Tract Infections"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2816-7107","authenticated-orcid":false,"given":"Cristiano","family":"Ialongo","sequence":"first","affiliation":[{"name":"Department of Experimental Medicine, Policlinico Umberto I, \u2018Sapienza\u2019 University, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9943-9130","authenticated-orcid":false,"given":"Marco","family":"Ciotti","sequence":"additional","affiliation":[{"name":"Laboratory of Clinical Microbiology and Virology, Tor Vergata University Hospital, 00133 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alfredo","family":"Giovannelli","sequence":"additional","affiliation":[{"name":"Department of Experimental Medicine, University of Rome Tor Vergata, 00133 Rome, Italy"},{"name":"Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6336-814X","authenticated-orcid":false,"given":"Flaminia","family":"Tomassetti","sequence":"additional","affiliation":[{"name":"Department of Experimental Medicine, University of Rome Tor Vergata, 00133 Rome, Italy"},{"name":"Department of Laboratory Medicine, Tor Vergata University Hospital, 00133 Rome, Italy"},{"name":"Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6028-9739","authenticated-orcid":false,"given":"Martina","family":"Pelagalli","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Tor Vergata University Hospital, 00133 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Di Carlo","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Tor Vergata University Hospital, 00133 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergio","family":"Bernardini","sequence":"additional","affiliation":[{"name":"Department of Experimental Medicine, University of Rome Tor Vergata, 00133 Rome, Italy"},{"name":"Department of Laboratory Medicine, Tor Vergata University Hospital, 00133 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3463-0268","authenticated-orcid":false,"given":"Massimo","family":"Pieri","sequence":"additional","affiliation":[{"name":"Department of Experimental Medicine, University of Rome Tor Vergata, 00133 Rome, Italy"},{"name":"Department of Laboratory Medicine, Tor Vergata University Hospital, 00133 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1415-9808","authenticated-orcid":false,"given":"Eleonora","family":"Nicolai","sequence":"additional","affiliation":[{"name":"Departmental Faculty of Medicine, Unicamillus-Saint Camillus International University of Health and Medical Sciences, 00131 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mancuso, G., Midiri, A., Gerace, E., Marra, M., Zummo, S., and Biondo, C. 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