Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion.
Eur Spine J
; 31(8): 2104-2114, 2022 08.
Article
em En
| MEDLINE
| ID: mdl-35543762
ABSTRACT
PURPOSE:
Anterior cervical discectomy and fusion (ACDF) is a common surgical treatment for degenerative disease in the cervical spine. However, resultant biomechanical alterations may predispose to early-onset adjacent segment degeneration (EO-ASD), which may become symptomatic and require reoperation. This study aimed to develop and validate a machine learning (ML) model to predict EO-ASD following ACDF.METHODS:
Retrospective review of prospectively collected data of patients undergoing ACDF at a quaternary referral medical center was performed. Patients > 18 years of age with > 6 months of follow-up and complete pre- and postoperative X-ray and MRI imaging were included. An ML-based algorithm was developed to predict EO-ASD based on preoperative demographic, clinical, and radiographic parameters, and model performance was evaluated according to discrimination and overall performance.RESULTS:
In total, 366 ACDF patients were included (50.8% male, mean age 51.4 ± 11.1 years). Over 18.7 ± 20.9 months of follow-up, 97 (26.5%) patients developed EO-ASD. The model demonstrated good discrimination and overall performance according to precision (EO-ASD 0.70, non-ASD 0.88), recall (EO-ASD 0.73, non-ASD 0.87), accuracy (0.82), F1-score (0.79), Brier score (0.203), and AUC (0.794), with C4/C5 posterior disc bulge, C4/C5 anterior disc bulge, C6 posterior superior osteophyte, presence of osteophytes, and C6/C7 anterior disc bulge identified as the most important predictive features.CONCLUSIONS:
Through an ML approach, the model identified risk factors and predicted development of EO-ASD following ACDF with good discrimination and overall performance. By addressing the shortcomings of traditional statistics, ML techniques can support discovery, clinical decision-making, and precision-based spine care.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fusão Vertebral
/
Degeneração do Disco Intervertebral
Tipo de estudo:
Etiology_studies
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Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
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Female
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Humans
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Infant
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Male
/
Middle aged
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article