Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.
Abdominal surgery
Clinical decision-making
Digitalization
Machine learning
Postoperative complications
Risk prediction
Journal
Langenbeck's archives of surgery
ISSN: 1435-2451
Titre abrégé: Langenbecks Arch Surg
Pays: Germany
ID NLM: 9808285
Informations de publication
Date de publication:
Feb 2022
Feb 2022
Historique:
received:
05
05
2021
accepted:
03
10
2021
pubmed:
31
10
2021
medline:
19
2
2022
entrez:
30
10
2021
Statut:
ppublish
Résumé
An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery. Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed. Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML's superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM. A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.
Identifiants
pubmed: 34716472
doi: 10.1007/s00423-021-02348-w
pii: 10.1007/s00423-021-02348-w
pmc: PMC8847247
doi:
Types de publication
Journal Article
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
51-61Informations de copyright
© 2021. The Author(s).
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