The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review
Rattachement africain : us, ir, it, tr, ca. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
STUDY DESIGN: Systematic Review. OBJECTIVE: To describe applications of AI for traumatic SCI management with focus on diagnostics, prognostication, and therapeutic interventions. METHODS: , 2020, and March 18, 2025, dealing with clinical aspects in the acute, post-injury rehabilitative and first year phases of SCI were included. Studies on brain computer interface, robotics and non-neurologic aspects of SCI were excluded. Extracted were country of study, study design, focus of study, total participants, American Spinal Injury Association (ASIA) Impairment Scale (AIS), machine learning (ML) models, inputs, outcomes and performance metrices. RESULTS: A total of 23 studies with 120,931 individuals were identified. Classical Machine Learning Models, Ensemble Learning Models and Deep Learning Models were the most used ML families. Age, AIS, neurologic level of injury, sex, mechanism of injury and motor score were the most common inputs. Predictions of neurologic status, functionality status, Hospital/ICU utilizations, complications, survival, discharge destination and results of image segmentation and patient grouping were the outputs of interest. The performance metrices were satisfactory in most and higher than humans in some studies. CONCLUSION: AI can facilitate personalized approach to diagnosis of SCI, prediction of outcomes like neurological improvement, complications, functionality indicators like walking, selfcare and independence, re-admissions, prolonged length of stays, discharge destination and mortality after injury. It was also useful to suggest specific MAP goals and time of surgical intervention. These functions complement clinical judgement.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review
- Date Crossref
- 04/12/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Thomas Jefferson University Department of Neurological Surgery pays non établi dans la noticeUniversité ou école supérieure
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Shahid Beheshti University of Medical Sciences Department of Neurological Surgery pays non établi dans la noticeUniversité ou école supérieure
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Università Cattolica del Sacro Cuore pays non établi dans la noticeUniversité ou école supérieure
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Drexel University pays non établi dans la noticeUniversité ou école supérieure
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Ankara University Department of Neurosurgery pays non établi dans la noticeUniversité ou école supérieure
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Rothman Orthopaedics pays non établi dans la noticeInstitution
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Rothman Institute pays non établi dans la noticeStructure de recherche
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University of Toronto pays non établi dans la noticeUniversité ou école supérieure
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Graduate School of Health Economics and Management (ALTEMS) Università Cattolica del Sacro Cuore pays non établi dans la noticeUniversité ou école supérieure
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Rothman Orthopedics Institute pays non établi dans la noticeStructure de recherche
Department of Neurological Surgery — Thomas Jefferson University, Department of Neurological Surgery — Shahid Beheshti University of Medical Sciences et Università Cattolica del Sacro Cuore, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.