Version 1
: Received: 3 July 2024 / Approved: 4 July 2024 / Online: 4 July 2024 (05:57:49 CEST)
How to cite:
Tamburella, F.; Lena, E.; Mascanzoni, M.; Iosa, M.; Scivoletto, G. Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis. Preprints2024, 2024070383. https://doi.org/10.20944/preprints202407.0383.v1
Tamburella, F.; Lena, E.; Mascanzoni, M.; Iosa, M.; Scivoletto, G. Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis. Preprints 2024, 2024070383. https://doi.org/10.20944/preprints202407.0383.v1
Tamburella, F.; Lena, E.; Mascanzoni, M.; Iosa, M.; Scivoletto, G. Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis. Preprints2024, 2024070383. https://doi.org/10.20944/preprints202407.0383.v1
APA Style
Tamburella, F., Lena, E., Mascanzoni, M., Iosa, M., & Scivoletto, G. (2024). Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis. Preprints. https://doi.org/10.20944/preprints202407.0383.v1
Chicago/Turabian Style
Tamburella, F., Marco Iosa and Giorgio Scivoletto. 2024 "Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis" Preprints. https://doi.org/10.20944/preprints202407.0383.v1
Abstract
Background: Prediction of neurorehabilitation outcomes after a Spinal Cord Injury (SCI) is crucial for healthcare resource management and improving prognosis and rehabilitation strategies. Arti-ficial 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 demographics 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 ap-proaches aimed to evaluate and compare their predictive 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 alimented also with parameters recorded during hospitalization, the ANN highlighted the importance of these additional factors like motor completeness and compli-cations during hospitalization, showing an improvement in its predictive accuracy (R=0.87).
Conclusion: 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 com-plications is crucial for improving functional recovery in SCI patients.
Medicine and Pharmacology, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
