Artificial intelligence facilitates decision-making in the treatment of lumbar disc herniations.
Artificial intelligence
Conservative vs. Operative
Lumbar disc herniation
Outcome prediction
Supervised machine learning
Journal
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
ISSN: 1432-0932
Titre abrégé: Eur Spine J
Pays: Germany
ID NLM: 9301980
Informations de publication
Date de publication:
08 2021
08 2021
Historique:
received:
25
04
2020
accepted:
22
09
2020
revised:
05
09
2020
pubmed:
14
10
2020
medline:
23
9
2021
entrez:
13
10
2020
Statut:
ppublish
Résumé
Apart from patients with severe neurological deficits, it is not clear whether surgical or conservative treatment of lumbar disc herniations is superior for the individual patient. We investigated whether deep learning techniques can predict the outcome of patients with lumbar disc herniation after 6 months of treatment. The data of 60 patients were used to train and test a deep learning algorithm with the aim to achieve an accurate prediction of the ODI 6 months after surgery or the start of conservative therapy. We developed an algorithm that predicts the ODI of 6 randomly selected test patients in tenfold cross-validation. A 100% accurate prediction of an ODI range could be achieved by dividing the ODI scale into 12% sections. A maximum absolute difference of only 3.4% between individually predicted and actual ODI after 6 months of a given therapy was achieved with our most powerful model. The application of artificial intelligence as shown in this work also allowed to compare the actual patient values after 6 months with the prediction for the alternative therapy, showing deviations up to 18.8%. Deep learning in the supervised form applied here can identify patients at an early stage who would benefit from conservative therapy, and on the contrary avoid painful and unnecessary delays for patients who would profit from surgical therapy. In addition, this approach can be used in many other areas of medicine as an effective tool for decision-making when choosing between opposing treatment options, despite small patient groups.
Identifiants
pubmed: 33048249
doi: 10.1007/s00586-020-06613-2
pii: 10.1007/s00586-020-06613-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2176-2184Informations de copyright
© 2020. The Author(s).
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