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Machine learning in pancreas surgery, what is new? literature review.
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- Author(s): Taha A;Taha A; Taha-Mehlitz S; Taha-Mehlitz S; Ortlieb N; Ortlieb N; Ochs V; Ochs V; Honaker MD; Honaker MD; Rosenberg R; Rosenberg R; Lock JF; Lock JF; Bolli M; Bolli M; Cattin PC; Cattin PC
- Source:
Frontiers in surgery [Front Surg] 2023 Jun 13; Vol. 10, pp. 1142585. Date of Electronic Publication: 2023 Jun 13 (Print Publication: 2023).- Publication Type:
Journal Article; Review- Language:
English - Source:
- Additional Information
- Source: Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101645127 Publication Model: eCollection Cited Medium: Print ISSN: 2296-875X (Print) Linking ISSN: 2296875X NLM ISO Abbreviation: Front Surg Subsets: PubMed not MEDLINE
- Publication Information: Original Publication: Lausanne : Frontiers Media S.A., [2014]-
- Abstract: Background: Machine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery.
Methods: We integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included.
Results: A search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022.
Conclusion: The integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(© 2023 Taha, Taha-Mehlitz, Ortlieb, Ochs, Honaker, Rosenberg, Lock, Bolli and Cattin.) - References: J Clin Med. 2020 Mar 07;9(3):. (PMID: 32155990)
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Technol Cancer Res Treat. 2021 Jan-Dec;20:15330338211050767. (PMID: 34738844) - Contributed Indexing: Keywords: deep learning; machine learning; pancreas; pancreas surgery; scoping review
- Publication Date: Date Created: 20230629 Latest Revision: 20230702
- Publication Date: 20230702
- Accession Number: PMC10293756
- Accession Number: 10.3389/fsurg.2023.1142585
- Accession Number: 37383385
- Source:
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