{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:43:27Z","timestamp":1787067807520,"version":"build-2736575974"},"reference-count":54,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Health","award":["RF-2019-12369396"],"award-info":[{"award-number":["RF-2019-12369396"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Prediction of neurorehabilitation outcomes after a Spinal Cord Injury (SCI) is crucial for healthcare resource management and improving prognosis and rehabilitation strategies. Artificial neural networks (ANNs) have emerged as a promising alternative to conventional statistical approaches for identifying complex prognostic factors in SCI patients. Materials: a database of 1256 SCI patients admitted for rehabilitation was analyzed. Clinical and demographic data and SCI characteristics were used to predict functional outcomes using both ANN and linear regression models. The former was structured with input, hidden, and output layers, while the linear regression identified significant variables affecting outcomes. Both approaches aimed to evaluate and compare their accuracy for rehabilitation outcomes measured by the Spinal Cord Independence Measure (SCIM) score. Results: Both ANN and linear regression models identified key predictors of functional outcomes, such as age, injury level, and initial SCIM scores (correlation with actual outcome: R = 0.75 and 0.73, respectively). When also alimented with parameters recorded during hospitalization, the ANN highlighted the importance of these additional factors, like motor completeness and complications during hospitalization, showing an improvement in its accuracy (R = 0.87). Conclusions: ANN seemed to be not widely superior to classical statistics in general, but, taking into account complex and non-linear relationships among variables, emphasized the impact of complications during the hospitalization on recovery, particularly respiratory issues, deep vein thrombosis, and urological complications. These results suggested that the management of complications is crucial for improving functional recovery in SCI patients.<\/jats:p>","DOI":"10.3390\/jcm13154503","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T11:34:20Z","timestamp":1722512060000},"page":"4503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis"],"prefix":"10.3390","volume":"13","author":[{"given":"Federica","family":"Tamburella","sequence":"first","affiliation":[{"name":"Department of Life Sciences, Health and Health Professions, Link Campus University, 00165 Rome, Italy"},{"name":"Spinal Center, Spinal Rehabilitation Laboratory, IRCCS Fondazione S. Lucia, 00179 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuela","family":"Lena","sequence":"additional","affiliation":[{"name":"Spinal Center, Spinal Rehabilitation Laboratory, IRCCS Fondazione S. Lucia, 00179 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Mascanzoni","sequence":"additional","affiliation":[{"name":"Spinal Center, Spinal Rehabilitation Laboratory, IRCCS Fondazione S. Lucia, 00179 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2434-3887","authenticated-orcid":false,"given":"Marco","family":"Iosa","sequence":"additional","affiliation":[{"name":"Department of Psychology, Sapienza University of Rome, 00183 Rome, Italy"},{"name":"Smart Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giorgio","family":"Scivoletto","sequence":"additional","affiliation":[{"name":"Spinal Center, Spinal Rehabilitation Laboratory, IRCCS Fondazione S. Lucia, 00179 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100171","DOI":"10.1016\/j.wnsx.2023.100171","article-title":"Incidence of traumatic spinal cord injury worldwide: A systematic review, data integration, and update","volume":"18","author":"Jazayeri","year":"2023","journal-title":"World Neurosurg. X"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yokota, K., Sakai, H., Kawano, O., Morishita, Y., Masuda, M., Hayashi, T., Kubota, K., Ideta, R., Ariji, Y., and Koga, R. (2024). Changing trends in traumatic spinal cord injury in an aging society: Epidemiology of 1152 cases over 15 years from a single center in Japan. 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